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https://www.easst4s2024.net/open-panels/#14388

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EASST-4S 2024 code of ethics & privacy policy - EASST-4S 2024 code of ethics & privacy policy

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Open Panels - EASST-4S 2024 code of ethics & privacy policy

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This two-day, hybrid event is aimed at researchers, practitioners, policy-makers and campaigners working in the area of 'public data' - which we take to mean data (personal and non-personal) held by the public sector and other public interest/public goods data which may be held by companies, cooperatives and charities - in Scotland and Europe.

We have run three previous online workshops on different aspects of using public data for development - social, economic and environmental, and this final event will bring together different themes and questions and will make an important contribution to providing insights and material for the programme's final report.

This project is funded by the Scottish Universities Insight Institute (SUII) and Scotland Europa.

For more information, visit the project's dedicated page on the SUII website.
Timings and joining details:
Thursday 25th of January & Friday 26th of January , 10:00-16.00

T﻿he workshop will be held in a Hybrid format with the option to participate remotely on Zoom. Joining details sent on registration.

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economic and environmental development tickets

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economic and environmental development glasgow

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economic and environmental development glasgow tickets

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Public data for Social, Economic and Environmental Development

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Eventbrite - The Scottish Universities Insight Institute presents Public data for Social, Economic and Environmental Development - Thursday, 25 January 2024 | Friday, 26 January 2024 at Scottish Universities Insight Institute. Find event and ticket information.

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Public Data for Social Development in Scotland and the EU workshop roundup.docx

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 Ciuriak, Dan, Enterprise Value and the Value of Data (November 3, 2023).

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 Ciuriak, Dan, Enterprise Value and the Value of Data (November 3, 2023).

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Data is often said to be the most valuable commodity of our age. It is a curiosity, therefore, that it remains largely invisible on the balance sheets of compa

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1. What is public data (related, what is ‘public’)? What should be public data? 

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1. What is public data (related, what is ‘public’)? What should be public data? 

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Scottish local government during COVID-19

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UBDC carried out an in-depth analysis of data engagements by Scottish local authorities between autumn 2020 and spring 2021. The resulting report comprises 15 key findings, and related recommendations for policy and practice.

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IMPETUS: enhancing the impact of citizen science

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Improving the way citizens work together to achieve sustainable development goals

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1. What is public data, the 'public' and what should be public data in an economic context? 

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1. What is public data, the 'public' and what should be public data in an economic context? 

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4. Opportunities and challenges for using public data for social development? 

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4. Opportunities and challenges for using public data for social development? 

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5. What can Scotland learn from Europe, and vice versa, Europe from Scotland? And beyond Europe?

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5. What can Scotland learn from Europe, and vice versa, Europe from Scotland? And beyond Europe?

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6. Please share any other thoughts/comments/resources here. Thanks!

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6. Please share any other thoughts/comments/resources here. Thanks!

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2. What is economic development when it comes to public data? How can public data be used for economic development? 

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2. What is economic development when it comes to public data? How can public data be used for economic development? 

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3. What are the opportunities and challenges for using public data for economic development?

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3. What are the opportunities and challenges for using public data for economic development?

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4. How does public data for economic development compare between the UK and EU since Brexit? And compare internationally?

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4. How does public data for economic development compare between the UK and EU since Brexit? And compare internationally?

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Call for proposals - hybrid final workshop 25-26 January 2024 - deadline 30 November

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Call for proposals - hybrid final workshop 25-26 January 2024 - deadline 30 November

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Call for proposals: Valuing Public Data - data governance for economic, environmental and social development

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Rethinking "public benefit" is more fully discussed in this paper by Kerasidou et. al - Machine learning models, trusted research environments and UK health data: ensuring a safe and beneficial future for AI development in healthcare

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Rethinking "public benefit" is more fully discussed in this paper by Kerasidou et. al - Machine learning models, trusted research environments and UK health data: ensuring a safe and beneficial future for AI development in healthcare

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Abstract

Digitalisation of health and the use of health data in artificial intelligence, and machine learning (ML), including for applications that will then in turn be used in healthcare are major themes permeating current UK and other countries' healthcare systems and policies. Obtaining rich and representative data is key for robust ML development, and UK health data sets are particularly attractive sources for this. However, ensuring that such research and development is in the public interest, produces public benefit and preserves privacy are key challenges. Trusted research environments (TREs) are positioned as a way of balancing the diverging interests in healthcare data research with privacy and public benefit. Using TRE data to train ML models presents various challenges to the balance previously struck between these societal interests, which have hitherto not been discussed in the literature. These challenges include the possibility of personal data being disclosed in ML models, the dynamic nature of ML models and how public benefit may be (re)conceived in this context. For ML research to be facilitated using UK health data, TREs and others involved in the UK health data policy ecosystem need to be aware of these issues and work to address them in order to continue to ensure a 'safe' health and care data environment that truly serves the public.

Data availability statement

Data sharing not applicable as no data sets generated and/or analysed for this study. Not applicable.

https://creativecommons.org/licenses/by/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

Introduction

There is a broad structural shift taking place in the UK and beyond, i which ushers in the increasing digitisation of the health and care sector. This is a shift that is balancing between two diverging yet interconnected developments: an increasing appetite for data-driven and machine learning (ML) healthcare technologies, supported by an innovation-driven research, technology and policy sector; and increasing awareness of the importance of legal and ethical safeguards guiding such innovations to ensure that legal rights and obligations, such as confidentiality and privacy, are protected and upheld along with more ethical approaches including the public's continued collaboration in such endeavours. 1 2 In the UK, trusted research environments (TREs) sit at the junction of these developments, attempting to balance differing interests between the public, research and rights.

TREs, also known as 'data enclaves', 'research data centre/centres' or 'safe havens', are physical or virtual analytical environments which can hold various data sets (such as population, census, or healthcare data, etc). Subject to monitoring and access controls, a TRE user can be allowed to work with these data but is prevented from releasing their analysis without permission. The aim of TREs is to provide a secure location for researchers to analyse data, especially personal data, enabling collaborative and transparent research while protecting data confidentiality and privacy.

While TREs have received relatively little attention in academic literature and debate, especially from ethical perspectives, 3 4 they are not new. ii Some have been in operation for almost 20 years now. Yet, they have recently come to the limelight as a key service for the UK's National Health Service (NHS) data which can engender public trust by facilitating the intensifying demand for sensitive data for research purposes while ensuring privacy, confidentiality and safe access. 5-7

Alongside the increasing prominence of TREs sits the drive for health data to feed into artificial intelligence (AI). AI is the prevailing umbrella term to refer to a range of computational techniques that can be used to make machines complete tasks in a way that would be considered intelligent were they to be completed by a human. Here we specifically refer to ML developments. This is a particular form of AI which involves computers 'learning' and adapting without specific instructions, doing so by using algorithms to analyse and draw inferences from data. With the UK's healthcare sector being positioned as a unique data-rich ecosystem that could wield significant medical advances due to its centralised nature and longitudinal population data 8 and as a lucrative business opportunity potentially worth several billions, 9 there is a concerted push to realise the UK's plans to become a global technological superpower 10 via its national healthcare system. This increased interest in the application of ML on sensitive data (ie, special category personal data iii), such as healthcare and medical data, means that TREs are increasingly approached with requests to use their data to develop new types of outputs, such as trained ML models. Such developments present new opportunities but also challenges for the secure and trusted function of TREs.

In order to advance these new opportunities for TREs while maintaining high privacy standards, the Data and Analytics Research Environments UK (DARE UK) Sprint Project titled Guidelines and Resources for AI Model Access from TrusTEd Research environments (GRAIMATTER) investigated the additional risk posed for the disclosure of personal data introduced by the release of trained ML models from TREs, and developed a set of technical, legal and ethical recommendations for how TREs should carry out disclosure control on ML models. Reflecting on our work as the Legal and Ethical project subteam, in this article, we focus on key ethical and legal issues stemming from the training of ML models from TRE data and how this impacts on TREs' operation. While the export of research output from TREs is generally regulated through controls such as manual supervision ('eyeballing') to ensure no personal data leave the TRE, the situation changes when TRE exports present more complex configurations such as ML models which then may be released to open source repositories such as GitHub. In such cases, it becomes harder to identify potential risks using conventional manual checks and therefore harder to guarantee the 'T' of TREs. To address these points, we will (1) explain what a TRE is and how it works. In particular, we will discuss how the 'Five Safes' framework contributes to their use and governance. We will then (2) address the relationship between the digitisation of healthcare in the UK and data-intensive innovations such as AI, and specifically ML, while identifying how TREs are being positioned as a way to ensure much needed public trust in such developments. We will conclude (3) by highlighting three legal and ethical critical areas that require further consideration for TREs and others involved in ML healthcare research for public benefit. This is significant for TREs, and for health data research more broadly, as ML research may disturb the current balance struck between facilitating research and protecting privacy in TREs given the risk of disclosure of sensitive personal data once a trained ML model is exported from the TRE, and the lack of clarity in terms of legal responsibility were a data breach to occur. This also relates to the dynamic nature of models compared with the 'static' nature of traditional TRE outputs, which may also require a more dynamic ethics process accompanying the research, and in turn require a rethinking of the public benefit produced by such research.

What is a TRE

A TRE is a secure physical or virtual environment designed for approved and named researchers to access sensitive pseudonymised data, where access to specific data sets is provided only to approved research projects. TREs differ from other data use models such as the more traditional data release model, where data are made available to approved researchers to download and analyse in their own data environments, hence risking losing control of their security and management. Instead, in a TRE, data are not released externally to data users for analysis on their own computers but placed on a server within a restricted, secure information technology environment, where the approved user is given secure access to carry out their project analysis. No row-level data leave the TRE environment. Traditionally, only aggregate-level results (eg, summary tables, graphs, statistical models) are released from a TRE at the end of the project, and only after a range of automatic and manual screening controls are applied to ensure that all outputs are non-disclosive of personal data.

The use of TREs is meant to address the challenges of using health and other forms of sensitive personal data to facilitate research that is assessed to be in the public interest while at the same time protecting privacy and ensuring trustworthiness. 5 6 Importantly, their use does not eliminate the risk of disclosure of sensitive personal data but greatly mitigates it 11 by providing assurances that data are handled securely, as data use can be tracked and technical and organisational measures are in place to check that no data leave the secure environment. Acting as data processors, TREs are meant to maintain a balance between:

Confidence of data controllers (who determine the purposes for and manner in which any special category personal data are to be processed) through increased security.

Benefits to the user/researcher (who can be from an academic, commercial or government setting) through improved access to larger data sets.

Transparency for public and patients (whose personal data are made available in pseudonymised form) as to who has access to the data and for what purposes in order to ensure their continuing confidence and engagement. 7

Robust data governance is key in achieving and maintaining such a balance. This means meeting all relevant legal obligations (eg, data protection, confidentiality, contracts and intellectual property), technical and cybersecurity requirements, and research ethics and data governance requirements.

There are several related frameworks used for providing guidance on TRE governance iv most of which are based on the 'Five Safes' model. 'Five Safes' is an internationally recognised model introduced by the UK Office for National Statistics in 2003. It has been described as an 'explicitly relativistic, subjective and empirical' framework which has proved a 'useful' tool to frame, rather than prescribe, the crucial discussions around governance and management of sensitive data involving data providers, users and regulators. 12 The 'Five Safes' breaks down the decisions surrounding data access and use into five related but separate dimensions: 13 , v

AI and the digitisation of healthcare: improving safety through the use of TREs

Healthcare has been identified as 'one of the most important sectors for AI both for better services and for better efficiency'. 14 15 This has paved the way for new, and arguably controversial, public-private-academic partnerships 16 for the development of new AI technologies, including ML, which can be used in several healthcare areas such as diagnostics, therapeutics, population health management and administration, and for providing key infrastructure for the storage, maintenance and management of the data that underpin these technologies. 17

Such developments align with the ongoing efforts since 2002 towards the digitisation of the NHS-from (missed) aims of achieving a 'paperless' NHS by 2018 to the renewed target for a 'core level of digitisation' by 2024 18-which has resulted in a rich and valuable wealth of healthcare data. The COVID-19 pandemic has reconfirmed and further accelerated plans for the digitisation of the NHS (ie, NHS apps, virtual appointments, online treatments, etc), along with recent plans to facilitate the more effective sharing of digital health and social care records and data. 19 Further plans to personalise healthcare through the use of wearable technologies and apps will only enrich these data sets.

AI, and in particular ML, technologies for healthcare rely on the availability of big data for their training and development. As such, the extensive medical and healthcare data that result from the ongoing interactions between the UK public and the NHS have long been seen as a prime opportunity for the adoption of innovative AI technologies, for day-to-day patient care and for the further advancement of health research.

While the opportunities that the increasing digitisation of healthcare offers appear exciting, the risks and concerns that such developments entail are considerable. There have been a series of situations where public trust has been eroded in data sharing-some of which have attracted significant media attention and regulatory enforcement, while others may be more 'routine' infractions of contracts. Nevertheless, these instances cumulatively may instil a negative attitude in the public towards data sharing. Among these, past big data health initiatives, such as care.data-an English initiative designed to allow the repurposing of primary care medical data for research and other purposes-and more recently the postponed GP Data for Planning and Research programme, 20 demonstrate the importance of public trust for major projects which seek to aggregate and centralise healthcare and related data, and the costly danger of losing it if legitimate public concerns are not taken seriously. 21 22 Scandals, such as the ongoing case of DeepMind/Google and the Royal Free, vi along with a recent report in the BMJ that there are hundreds of organisations such as clinical commissioning groups, private companies and universities which have breached patient sharing agreements, some of them with little or no consequences 23 (see also ref 24 25), demonstrate that what is often termed the 'deficit of public trust' 26 is not the result of public ignorance or badly publicised information 21 27 (see also ref 28 29). Instead, it is an appropriate response of a public who, while willing for their data to be used for the benefit of patients and the NHS, are wary of a weak regulatory landscape that allows such data security failures. 2 30 Nevertheless, this emerging picture has received limited attention in the academic literature and limited discussion as to how it may impact on data sharing and governance arrangements and policies more broadly as the NHS seeks to digitise and share more data. vii

To address the issue of the protection of personal data and the facilitation of research, TREs are positioned as a way to maintain and restore public trust. 5-7 The technical and organisational safety measures that TREs offer can provide assurances that, not only will data not be leaving their secure environments but every interaction and subsequent analysis will be checked and tracked. Furthermore, their commitment to Safe Projects means that each project is assessed by an ethical and data governance committee for their potential to public benefit before a project approval is granted. viii In order to ensure public benefit and build public trust, the importance of patient and public participation alongside transparency of decision-making and data use has been further highlighted. 7 31 32

However, while TREs may be identified as the appropriate way to address public trust concerns, our research shows that the increase in the development and adoption of AI, and in particular ML, in the medical and healthcare fields presents new challenges for the next generation of TREs which may threaten the 'T' of the TREs due to additional risks of disclosure of personal data by ML models trained on TRE data and a lack of clarity about chains of responsibility once the ML model has left the TRE environment.

TRE outputs and ML

Typically, TRE outputs take the form of aggregated results, graphs and tables. Before their release, these outputs go through both automatically and manually disclosive controls and are checked to ensure that no identifiable information is attached to them before allowed to leave the TRE. With the increased interest in ML trained on special category data such as healthcare and medical data, TREs are increasingly approached with requests to use their data for such purposes and to disclose new types of outputs, such as ML models trained on TRE data.

As the GRAIMATTER research demonstrates, the release of trained ML models from TREs introduces an additional risk for the disclosure of personal data. 13 33 In other words, while on the one hand models are being constructed using training data, on the other, training data and/or a semblance or subset of it, or information about who was in the training set can also, in certain cases, be reconstructed from a model. This means that trained ML models may be considered as containing personal data and therefore constitute personal data sets, bringing them within the jurisdiction of data protection legislation. 34 Personal data disclosure from trained ML models can happen inadvertently-for example, if the ML algorithm is overtrained, and the weights of the algorithm which are then exported from the TRE correspond to the data underneath-or, there can be malicious intent-for example, when a malicious researcher 'hides' individual-level data within the files (eg, sensitive data could be embedded in the weights of an ML algorithm which are then exported). 33 , ix

In order to mitigate any risk of direct or indirect personal data breach from the disclosure of trained ML models, and hence maintain public trust, key aspects of the technical, legal and ethical governance of TREs need to be reconsidered. Our project GRAIMATTER explored these challenges and proposed a range of measures and recommendations that need to be considered for the safe disclosure of trained ML models from TREs. 13 We focused specifically on the ethical and legal governance issues arising from such practices recommending ways that they can be addressed. Here we present some broader issues that informed our thinking.

Legal and ethical challenges

While TREs have been positioned as the safer response to the riskier and controversial data release model, it is important to highlight that they are not a magic bullet. All their technical controls notwithstanding, they too are complex sociotechnical systems which, each in their own ways, bring together people, technology, regulations, institutional bodies, auditing and organisational procedures in a sophisticated but always precarious balance that seeks to facilitate research access to data while preserving privacy. The introduction of ML in such a setting requires us to rethink carefully whether and how a new balance can be achieved. In the paragraphs that follow, we highlight three critical areas that require further consideration if we want to ensure a 'safe' health and care data environment that truly serves the public, namely the possibility of disclosure of personal data by ML models once they have left the TRE, the dynamic nature of ML models and the impact that these and other factors have on the discussions around public benefit.

Conclusion

In this article, we have highlighted developments in health data and ML research and policy vis-a-vis TREs in the UK. We identified how TREs are being positioned at the junction between an increasing appetite for data-driven and ML healthcare technologies and an increasing awareness of the importance of legal and ethical safeguards guiding such innovations. Drawing from our work on the GRAIMATTER project which explored the additional risks when disclosing trained ML models from TREs, 13 we first explained what TREs are and how the 'Five Safes' framework contributes to their use and governance. We then addressed the relationship between the digitisation of healthcare in the UK and data-intensive innovations such as AI, and in particular ML, while identifying how TREs are being positioned as a way to ensure much needed public trust in such developments. We concluded this article by highlighting three broad legal and ethical critical areas that require further consideration if we want to ensure a 'safe' health and care data environment that truly serves the public: (1) the risk of personal data being disclosed in ML models, (2) the dynamic nature of ML models and (3) how public benefit may be (re)conceived in this context. We argue that these broad critical areas require further thought from TREs and others involved in the UK health data policy ecosystem if they want to ensure a truly 'safe' health and care data environment that indeed serves the public while facilitating AI and ML research on UK health data.

Data availability statement

Data sharing not applicable as no data sets generated and/or analysed for this study. Not applicable.

Acknowledgments

We acknowledge and thank the project's PI Emily Jefferson and the rest of our colleagues and collaborators on the GRAIMATTER project.

The views expressed in this paper are the authors' alone and do not necessarily reflect those of the GRAIMATTER team.

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Machine learning models, trusted research environments and UK health data: ensuring a safe and beneficial future for AI development in healthcare

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Digitalisation of health and the use of health data in artificial intelligence, and machine learning (ML), including for applications that will then in turn be used in healthcare are major themes permeating current UK and other countries' healthcare systems and policies.

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Today, The Open Data Policy Lab at The GovLab published a new primer, "Data Sandboxes: Managing the Open Data Spectrum", written by Uma Kalkar, Sampriti Saxena, and Stefaan Verhulst. The white paper is designed to support responsible and innovative data collaboration practices through the design, adoption, and governance of data sandboxes.
Opening up data offers opportunities to enhance governance, elevate public and private services, empower individuals, and bolster public well-being. However, achieving the delicate balance between open data access and the responsible use of sensitive and valuable information presents complex challenges. Data sandboxes are an emerging approach to balancing these needs.

In this white paper, The GovLab seeks to answer the following questions surrounding data sandboxes: What are data sandboxes? How can data sandboxes empower decision-makers to unlock the potential of open data while maintaining the necessary safeguards for data privacy and security? Can data sandboxes help decision-makers overcome barriers to data access and promote purposeful, informed data (re-)use?

Data sandboxes present six unique characteristics that make them a strong tool for facilitating open data and data re-use. These six characteristics are: controlled, secure, multi-sectoral and collaborative, high computing environments, temporal in nature, adaptable, and scalable.

Data sandboxes can be used for: pre-engagement assessment, data mesh enablement, rapid prototyping, familiarization, quality and privacy assurance, experimentation and ideation, white labeling and minimization, and maturing data insights.

There are many benefits to implementing data sandboxes. We found ten value propositions, such as: decreasing risk in accessing more sensitive data; enhancing data capacity; and fostering greater experimentation and innovation, to name a few.

When looking to implement a data sandbox, decision-makers should consider how they will attract and obtain high-quality, relevant data, keep the data fresh for accurate re-use, manage risks of data (re-)use, and translate and scale up sandbox solutions in real markets.

Advances in the use of the Internet of Things and Privacy Enhancing Technologies could help improve the creation, preparation, analysis, and security of data in a data sandbox. The development of these technologies, in parallel with European legislative measures such as the Digital Markets Act, the Data Act and the Data Governance Act, can improve the way data is unlocked in a data sandbox, improving trust and encouraging data (re-)use initiatives.

The 10 value propositions of data sandboxes for participants. Image from The GovLab.

1. Appointing a Data Sandbox Facilitator, who will lead the sandbox and team, facilitate implementation, and ensure smooth communication among stakeholders. This person should have expertise in both the sandbox domain and data management. We refer to these individuals as 'bilinguals.'

2. Defining the purpose and problem by clearly articulating the objectives of the data sandbox project (i.e. is it a space to test the quality of data, prototype new data products, or improve existing data solutions?) and identifying the specific problem(s) the project aims to address. This can also involve creating a list of prioritized milestones.

3. Identifying additional roles and positions to ensure all necessary skills and expertise are represented. It can also be helpful to establish the roles and responsibilities of the various team members and stakeholders at this time. This can include system administrators, data stewards, and technical project managers who can speak to the good data practices, funding and sustainability needs, and overall management of the data sandbox.

4. Establishing data and technological infrastructure requirements, including the minimum specifications (min-specs) of the data and technological resources needed to support the data sandbox project.

5. Establishing the role of a data steward to define the standards of quality for data and continuously monitor, audit, and help enhance data in the sandbox to uphold quality standards. A data steward is also responsible for summarizing and communicating the results of projects in the sandbox with relevant stakeholders.

6. Developing a methodology for categorizing the sandbox's data assets in collaboration with industry players to map key datasets. This can be especially important when using PETs such as synthetic data to create bespoke datasets for sensitive use cases.

7. Providing strong metadata about the data available in the sandbox to help potential users understand the sandbox's purpose and its alignment with their needs.

8. Defining data access and use parameters that clearly outline the eligibility requirements and process of joining the sandbox. This can include creating a system for soliciting, vetting, selecting, and communicating with sandbox users to stay aligned to core values.

9. Building dispute resolution processes before they are needed to proactively handle disagreements or legal/regulatory issues that may arise in the sandbox.

After evaluating a series of case studies, we identified the following key findings:

Further, we offer a series of operational considerations and principles to take into account when designing a data sandbox, as well as a series of processes and practices that can operationalize these governance requirements. The operational methodology applies a 'scrum' framework to help achieve a flexible and agile sandbox, and includes the following considerations:

We conclude that data sandboxes can offer a secure and controlled environment for data access and exploration that provides a low-risk and high-reward way of trialing data sharing and (re-)use. This model of data collaboration can help promote data openness among new players, improving the availability and access to data to address public problems.

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Data Sandboxes: Managing the Open Data Spectrum

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Today, The Open Data Policy Lab at The GovLab published a new primer, "Data Sandboxes: Managing the Open Data Spectrum", written by Uma Kalkar, Sampriti Saxena, and Stefaan Verhulst. The white paper...

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Independent Expert Group on Unlocking the Value of Public Sector Personal Data for Public Benefit - Final Report published August 2023

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Independent Expert Group on Unlocking the Value of Public Sector Personal Data for Public Benefit - Final Report published August 2023

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Final Report of the Independent Expert Group to the Scottish Government on Unlocking the Value of Data

The Independent Expert Group

Chaired by:

Angela Daly - University of Dundee

Members:

Annie Sorbie - University of Edinburgh

Ruchir Shah - Open Government and Civil Society Activist

Esperanza Miyake - University of Strathclyde

Colin Birchenall - Digital Office for Scottish Local Government

Charlie Mayor - NHS Greater Glasgow and Clyde

Carol Young - Coalition for Racial Equality and Rights

Alexander Weir - Canon Medical Research Europe Ltd.

Ronnie Kelly - Fujitsu UK

Former members:

Mahlet Zimeta ('Milly') - The Open Data Institute

James Stevenson - Duo Verre Partnership LLP

The writing of this report was coordinated and overseen by Angela Daly with contributions from IEG members.

Acknowledgements

We would like to thank the Scottish Government Secretariat who supported this work. We would also like to thank all those who have provided feedback on earlier iterations of the principles, recommendations and other aspects of this report. In particular we thank: members of the Practitioner Forum; the Democratic Society and all those members of the public and experts who participated in workshops; all those who attended the September 2022 webinar; and the authors of the three literature reviews. We thank Charalampia (Xaroula) Kerasidou (University of Oxford Ethox Centre in Population Health) for peer reviewing this report.

Angela Daly would like to thank the Leverhulme Research Centre for Forensic Science and the Law School at the University of Dundee for supporting her role as IEG chair. She would also like to thank Magdalen College, University of Oxford which hosted her for a research visit from October to December 2022.

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Unlocking the value of data - Independent Expert Group: final report

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This report is the final output of the Independent Expert Group on the Unlocking the Value of Data programme, to the Scottish Government. This report is a Ministerial commission, and was originally commissioned by the former Minister for Business, Trade, Tourism and Enterprise.

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This two-day, hybrid event is aimed at researchers, practitioners, policy-makers and campaigners working in the area of 'public data' - which we take to mean data (personal and non-personal) held by the public sector and other public interest/public goods data which may be held by companies, cooperatives and charities - in Scotland and Europe.

We have run three previous online workshops on different aspects of using public data for development - social, economic and environmental, and this final event will bring together different themes and questions and will make an important contribution to providing insights and material for the programme's final report.

This project is funded by the Scottish Universities Insight Institute (SUII) and Scotland Europa.

For more information, visit the project's dedicated page on the SUII website.
Timings and joining details:
Thursday 25th of January & Friday 26th of January , 10:00-16.00

T﻿he workshop will be held in a Hybrid format with the option to participate remotely on Zoom. Joining details sent on registration.

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economic and environmental development tickets

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economic and environmental development glasgow

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economic and environmental development glasgow tickets

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Public data for Social, Economic and Environmental Development

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Eventbrite - The Scottish Universities Insight Institute presents Public data for Social, Economic and Environmental Development - Thursday, 25 January 2024 | Friday, 26 January 2024 at Scottish Universities Insight Institute. Find event and ticket information.

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This project aims to share research, policy insights and good practice about how public sector data can be best managed and used to ensure its value is of public benefit from social, economic and environmental perspectives. Some members of the core team collaborated together as part of the Scottish Government's Independent Expert Group for the Unlocking the Value of Public Data programme (2022-23), and in extending their partnership to other academics, policymakers, practitioners and public members from the EU, this project will provide the opportunity to explore:

a) how Scotland is situated within broader post-Brexit European data governance frameworks;

b) the diversity of different publics and their interplay with public sector data in Scotland and the EU;

c) inequalities in data to ensure economic, social and environmental development are both ethical and equitable.

The programme will consider these in relation to health, criminal justice, environmental protection and local government because public sector data most affects citizens in these areas, and have the potential to produce public benefit and value from social, economic and environmental perspectives.

The programme will start by running online workshops on data for social, economic and environmental benefits, respectively, with discussions aiming to feed into a cross-cutting KE hybrid workshop bringing together academics and practitioners in Scotland and the EU. A report will be produced containing policy recommendations and points for further research. This KE event will also include a session for the general public, and a symposium for the wider academic/practitioner community.

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Valuing Public Sector Data in Scotland and Europe: Data Governance for Economic, Environmental and Social Development

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This project aims to share research, policy insights and good practice about how public sector data can be best managed and used to ensure its value is of public benefit from social, economic and environmental perspectives. The programme will consider this in relation to health, criminal justice, en…

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Prof. Angela Daly, University of Dundee (co-lead)

Dr Esperanza Miyake, University of Strathclyde (co-lead)

Mr Andrew Porter (project researcher)

Other programme team members:

Prof. Niamh Nic Daeid, University of Dundee

Dr Annie Sorbie, University of Edinburgh

Dr Stephanie Switzer, University of Strathclyde

Lucille Tetley-Brown, UNU-EGOV

Dr Anna Berti Suman, Systasis – Research Centre for the Governance of Environmental Conflict (Italy)

Prof. Julián Valero, University of Murcia (Spain)

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We’re the global independent body opening finance for good. A mission-led organisation here to unlock the positive power of financial data to improve people’s lives.

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DATALOG - Associació Dades pel Benestar Planetari (DATALOG)

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Ayuda a construir la mayor comunidad de datos de consumo para conocer mejor tu ciudad.

DATALOG es una comunidad de datos que ofrece la oportunidad de gestionar datos de consumo públicos (agua, luz y gas) del área metropolitana de Barcelona.

Junto a algoritmos de análisis y predicción avanzados, la ciudadanía podrá tomar decisiones informadas sobre su consumo para luchar contra la pobreza energética y la emergencia climática.

¿Qué podrás hacer?

Conoce el impacto de la ciudad
en la huella de carbono

Descubre si eres pionero del consumo responsable en tu barrio

Plantea objetivos de consumo y recibe informes personalizados

Descubre eventos anómalos en tu consumo

¿Por qué nace datalog?

La ausencia de datos de consumo en la ciudad dificulta tomar decisiones para disminuir el impacto. Pero su accesibilidad está limitada por razones de privacidad, de granularidad y por falta de conocimiento experto para analizarlos. DATALOG nace como un sistema de gobernanza de datos con principios de participación ciudadana que ofrece acceso a datos mediante algoritmos de anonimización y agregación que, a su vez, desarrolla herramientas de visualización y predicción que ayudarán a entender los consumos en la ciudad, mejorar su gestión y reducir la huella de carbono.

Fases de trabajo

Preguntas frecuentes

¿Cómo funcionará la comunidad?

DATALOG será lo que se conoce como Data Trust. Es decir, una entidad fiduciaria que estará a cargo de proteger la privacidad de los datos al mismo tiempo que promoverá la investigación.

¿Quienes están a cargo del proyecto?

A fines prácticos, funciona como una asociación de interés general y sin fines de lucro, coincidiendo con la definición de Altruismo de Datos según la Regulación de Gobernanza de Datos de la Unión Europea.

¿Qué tipos de datos gestionará?

DATALOG tendrá el objetivo de gestionar datos que faciliten la investigación urbana. Es decir, todos aquellos que nos permitan entender cómo funcionan las ciudades, qué recursos utiliza, y cómo se mueven las personas dentro de ella. En una primera etapa, DATALOG se enfocará en datos de consumo energético y de recursos como el agua o el gas.

¿Como protegerán mis datos?

La gestión de los datos se refiere a diversas operaciones por las que se recogen, almacenan y se procesan los datos. Muchos de estos términos están regulados en Europa por la Regulación General de Protección de Datos (GDPR). Para todos los datos recolectados, se utilizarán métodos de protección de privacidad y anonimización.

¿Qué beneficios tendré por participar?

Formar parte de la primera comunidad de datos urbanos de Europa es todo un reto 😉 Con DATALOG podrás profundizar tu conocimiento sobre tu consumo para tomar decisiones más responsables y sostenibles que te ayudarán a gestionar mejor tu día a día con informes personalizados. Además contribuirás a disminuir la huella de carbono, conocer el consumo de tu ciudad y así luchar contra la emergencia climática.

¿Cómo puedo contribuir al proyecto?

Si tienes interés en aportar tu conocimiento, herramientas o tiempo al proyecto, ¡escríbenos para poder comenzar a colaborar!

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DATALOG - Associació Dades pel Benestar Planetari (DATALOG)

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Associació Dades pel Benestar Planetari (DATALOG)

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For decades, online ecosystems have been dominated by commercial platforms that profit from people's engagement and interactions. True digital public spaces, where people can openly express themselves, exchange ideas, and engage in substantive discourse without being exploited and having value extracted from them, are rare. Against this backdrop, in the newly published paper, Warso examines digital rights as a framework that plays a role in shaping the dynamics of online ecosystems.

The paper considers the feasibility of a holistic rights-based approach to creating genuine digital public spaces.

How does Warso examine the need for digital public spaces to fulfil digital rights?

Warso looks into the concept of digital rights and its scope, pointing out that it is a mixture of various rights frameworks with roots in human rights. It highlights a historical context where distinctions among different categories of human rights, stemming from the political and ideological divisions of the 1950s and 1960s, led to a prolonged period of overlooking social and economic rights by states, both in the digital and non-digital domains.

In the paper, you can learn about the complex landscape of claiming digital rights, given the peculiar situation of privately owned platforms, such as social media platforms, frequently serving as de facto digital public spaces. Within these spaces, various rights frameworks operate concurrently due to the different functions and conceptualisations of the same digital environments. These spaces, which allow for community activism as well as social and political interaction, are dominated by market-driven principles and dynamics.

What are the key limitations around the concept of digital rights?

The key limitations inherent in the prevailing conception of digital rights encompass challenges with the enforcement of rights, a disregard for social justice issues, and neglect of materiality and the environmental impacts of technologies that underpin digital public spaces. Despite its limitations, a rights-based approach to building digital public spaces can shape these spaces in a way that combats exploitative business models and protects individual and collective well-being.

To enjoy digital rights we need truly public digital spaces

The practical realisation of rights is contingent on the existence of an environment conducive to their exercise. Therefore, addressing the challenges arising from the digital environment's interrelated political, economic, and social dynamics necessitates a dual approach.

While acknowledging the value of a rights-based approach, it alone cannot resolve complex digital challenges. There is a fundamental misalignment between private economic interests on the one hand and conditions required for the full enjoyment of rights on the other as the potential root cause of the difficulty in enforcing digital rights. Given that exploitative business models characterise the digital platform landscape, creating truly public digital spaces is just as important as platform regulation and defending rights within current online ecosystems.

Contribution by: EDRi member, Open Future

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New paper calls for digital public spaces - European Digital Rights (EDRi)

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In a newly published paper Open Future, explains why establishing Digital Public Space is necessary for the realisation of digital rights.

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Explore the transformative power of non-personal data in this report. Understand how non-personal data plays a crucial role in addressing global challenges and discover how it fuels positive societal impacts. From tracking disease outbreaks to enhancing cross-border cybersecurity, the report outlines the landscape of non-personal data benefits while highlighting challenges hindering its full potential. It advocates for a balanced and targeted approach to cross-border data, promoting economic and societal well-being.

Non-personal data plays a critical role in providing solutions to global challenges. Unlocking its full potential requires policymakers, businesses, and all other stakeholders to collaborate to construct policy environments that can capitalise on its benefits.

This report gives insights into the different ways that non-personal data has a positive impact on society, with benefits including, but not limited to:

Tracking disease outbreaks;

Facilitating international scientific cooperation;

Understanding climate-related trends;

Improving agricultural practices for increased efficiency;

Optimising energy consumption;

Developing evidence-based policy;

Enhancing cross-border cybersecurity cooperation.

In addition, businesses of all sizes benefit from the transfer of data across borders, allowing companies to establish and maintain international supply chains and smaller businesses to enter new markets or reduce operating costs.

Despite these benefits, international flows of non-personal data are frequently limited by restrictions and data localisation measures. A growing patchwork of regulations can also create barriers to realising the potential of non-personal data. This report explores the impact of data flow restrictions including:

Hindering global supply chains;

Limiting the use of AI reliant on large datasets;

Disincentivising data sharing amongst companies;

Preventing companies from analysing the data they hold.

As the world's largest business organisation, ICC is committed to advocating for policies which allow non-personal data to be leveraged for economic and societal benefits. ICC believes in a multi-stakeholder and interoperable approach to data governance based on the realities of a transboundary digital world.

A balanced and targeted approach to cross-border data flows can help unlock the immense benefits of non-personal data. The highly diverse nature of non-personal calls for policy measures that allow for the transfer of non-personal data across borders as a default, with any necessary regulations for specific cases being highly targeted and evidence-based.

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Policy primer on non-personal data - ICC - International Chamber of Commerce

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Explore the transformative power of non-personal data in this report. Understand how non-personal data plays a crucial role in addressing global challenges and discover how it fuels positive societal impacts. From tracking disease outbreaks to enhancing cross-border cybersecurity, the report outlines the landscape of non-personal data benefits while highlighting challenges hindering its full potential.

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Intelligenza artificiale: Garante privacy apre un'indagine sulla raccolta di dati personali on line per addestrare gli algoritmi
L'iniziativa è volta a verificare l'adozione di misure di sicurezza da parte di siti pubblici e privati
Il Garante per la protezione dei dati personali ha avviato una indagine conoscitiva sui siti internet pubblici e privati per verificare l'adozione di idonee misure di sicurezza adeguate ad impedire la raccolta massiva ( webscraping) di dati personali a fini di addestramento degli algoritmi di intelligenza artificiale (IA) da parte di soggetti terzi.

L'indagine conoscitiva riguarda tutti i soggetti pubblici e privati, operanti quali titolari del trattamento, stabiliti in Italia o che offrono in Italia servizi, che mettono a disposizione on-line dati personali liberamente accessibili anche dagli "spider" dei produttori di algoritmi di intelligenza artificiale.

È nota, infatti, l'attività di diverse piattaforme di IA, le quali attraverso il webscraping raccolgono, per differenti usi, enormi quantità di dati anche personali pubblicati per specifiche finalità (cronaca, trasparenza amministrativa ecc.) all'interno di siti internet gestiti da soggetti pubblici e privati.

Il Garante rivolge quindi un invito alle associazioni di categoria interessate, alle associazioni di consumatori, ad esperti e rappresentanti del mondo accademico affinché facciano pervenire i loro commenti e contributi sulle misure di sicurezza adottate e adottabili contro la raccolta massiva di dati personali a fini di addestramento degli algoritmi, all'indirizzo webscraping@gpdp.it, entro 60 giorni dalla data di pubblicazione dell'avviso di consultazione sul sito dell'Autorità.

A seguito dell'indagine conoscitiva l'Autorità si riserva di adottare i necessari provvedimenti, anche in via d'urgenza.
Roma, 22 novembre 2023

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Intelligenza artificiale: Garante privacy apre un'indagine sulla raccolta di dati personali on line per addestrare gli algoritmi. L'iniziativa è volta a verificare l'adozione di misure di sicurezza da parte di siti pubblici e privati

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Intelligenza artificiale: Garante privacy apre un'indagine sulla raccolta di dati personali on line per addestrare gli algoritmi L'iniziativa è volta a verificare l'adozione di misure di sicurezza da parte di siti pubblici e privati Il Garante per la protezione dei dati personali ha avviato una indagine conoscitiva sui siti internet pubblici e privati per verificare l'adozione di idonee misure di sicurezza adeguate ad impedire la raccolta massiva ( webscraping) di dati personali a fini di addestramento degli algoritmi di intelligenza artificiale (IA) da parte di soggetti terzi.

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1. What is public data, the ‘public’ and what should be public data in an environmental context?

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1. What is public data, the ‘public’ and what should be public data in an environmental context?

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How should environmental Public Sector data “blend in” with the broader ecosystem, from an impact-driven perspective?

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2. What is environmental development when it comes to public data? How can public data be used for environmental development?

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2. What is environmental development when it comes to public data? How can public data be used for environmental development?

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What are the desired goals of providing access to environmental information, and should users share these goals?

How can we enhance collaboration between government agencies, NGOs, and private sectors to improve the accessibility and quality of environmental data in Scotland and the EU?

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3. How does public data for environmental development compare between the UK and EU since Brexit?

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3. How does public data for environmental development compare between the UK and EU since Brexit?

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How can the new EU legal instruments (esp. the Data Act, and the DGA) leverage environmental data sharing for public use?

In what ways can the EU facilitate cross-border sharing of environmental data among member states to address common challenges and promote sustainable development? And beyond the EU?

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4. What are the opportunities and challenges for using public data for environmental development?

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4. What are the opportunities and challenges for using public data for environmental development?

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Does the public hold the expertise to effective engage with environmental open data? Should the state provide aid in helping the public to engage with such data?"

How do the public sectors in Scotland and the EU manage the tension between leveraging data to combat climate change and addressing the environmental impact of digital data usage? Share strategies for optimising data benefits while minimising its carbon footprint.

How can the concept of data stewardship serve as a solution for the public sector to not only harness but also demonstrate the value of data in addressing climate change? Share insights on effective data stewardship practices that align with environmental goals.

What functions can data altruism organizations and data intermediaries play to leverage the use (and impacts) of public data?

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Multi-Stakeholder Governance Scheme — DS4SSCC

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This document outlines the governance plan for DS4SSCC, developed collaboratively with input from various stakeholders, including cities, local authorities, private sector, academia, and civil society organizations. It aims to establish a foundation for trustworthy local data systems and to pave the way for a broader European data space.

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This report, emerging from ODI's Power and diplomacy in data ecosystems project, sets out the findings of research unpacking the stakes and challenges related to what we came to consider as 'critical data infrastructure', namely the physical architecture enabling the internet and our societies to function. For this project, the ODI's Data as Culture programme has been commissioned to collaborate with the research team to conduct joint interdisciplinary research and enable new ideas and innovative multi-disciplinary methods, processes and outcomes. Building on her long-term research into nature, data and complex systems, artist Julie Freeman has created a new kinetic data-driven work of art, named 'Allusive Protocols', due for launch on 19th May at City Hall, London.

The internet is a complex and multifaceted entity that exists in both virtual and material forms. While the virtual dimension of the internet is often the focus of analysis, governance, regulation, discussions and criticisms, its materiality is equally important to consider and way less well understood. The physical infrastructure that underpins the internet, such as data centres, fibre-optic undersea cables, and satellites, is vital to its functioning and is responsible for the transmission and storage of the vast amounts of data that circulate online. To make the most of this constant flow of data, it is essential to consider both its virtual and material dimensions.

Our findings

In this report, we investigate how the physical aspects of the internet reveal its vulnerability to global issues like climate change and geopolitical structures. We examine:

The power dynamics at play in data infrastructures and the (im)balances they reveal worldwide, as well as how the wielding of power by a few groups of actors is changing international power dynamics and relationships.

The ecological cost of the internet's material infrastructure (the cloud seems to have a greater carbon footprint than the airline industry) and the necessity of highlighting and investigating this impact further in order to build efficient and sustainable advocacy work around climate change.

The ways in which data, digital technologies and the technical infrastructure underpinning the internet can be used as a tool for diplomacy, showcasing how the state and private companies are embedded in a complex system of influences, and subsequently redefining traditional international diplomatic rules.

What's next?

At the ODI we will continue to reflect on global and local dynamics within data ecosystems and we encourage other relevant stakeholders to do the same. We are recognising the tensions within advocating for a world where data works for everyone and some of the findings in this report. We would like to collaborate with organisations who are similarly contemplating these topics, including those working in public sector and civil society, academic and industry researchers, artists and arts organisations, journalists and thought-leaders.

Building on the analysis within this report we suggest further areas of research to be explored with interested partner organisations, including:

The environmental impact of digital technologies and data infrastructures and how to be more responsible with the volume of data created, stored and processed.

How to encourage regulatory domains to consider not just international data sharing but also energy use and environmental harms.

How to counter the commerce-oriented visions of the future being pushed by private sector organisations, by exploring alternative futures that better benefit people and the planet, including those envisioned by other organisations, artists and academics.

If you have any thoughts or feedback on this work, or if you would like to collaborate on Power and Diplomacy, please get in touch via email at [email protected] .

Read the report

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Power, ecology and diplomacy in critical data infrastructures

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This report examines the stakes and challenges related to 'critical data infrastructure', considering both virtual and material dimensions of the internet.

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At the Open Data Institute, we've been working on defining responsible data stewardship for the between June 2022 and March 2023, with the support of the Patrick J. McGovern Foundation. This blog was collaboratively written with Professors Tom Jackson and Ian Hodgkinson from the Loughborough Business School at Loughborough University.

The internet and climate change: a structural paradox

Data and digital technologies play a pivotal role in tackling climate change, serving as catalysts towards achieving the Sustainable Development Goals (SDGs). Data-driven and AI technologies are already being used to tackle different impacts of climate change. This includes enhancing our capacities to monitor and analyse environmental variables, such as resource depletion, droughts, pollution and predicting deforestation patterns and temperature fluctuations in a more comprehensive manner. Data stewardship is an important part of ensuring that data is available to support the fight against the climate crisis. Data enables more informed decision making and better targeted interventions, as well as improving and enriching scientific research. This was illustrated by the case of Nepal, where effective data sharing played a pivotal role after the 2015 earthquake. Collaborative data sharing between private, public and third sectors emerged from this tragic event, leading to a mitigation of the natural disaster aftermath. Given the current climate crisis, similar catastrophes may become more commonplace, and collaborative data sharing will play a key role in averting and mitigating these crises.

However, too little is known (and done) about how data and technology contribute to the crisis. The internet creates 1.6 billion metric tons of greenhouse gas emissions per year. There is a growing recognition of the internet's cost to the climate, including the impact of online video streaming. This is intrinsically linked to the way we collect, use and share data as individuals, as organisations and as society more broadly. Questions of environmental sustainability of data and technology, like energy-intensive AI models, are gaining traction in the public debate.

As our reliance on data and technology increases, the environmental impacts of their use and development are likely to become more and more significant. Governments and organisations creating these technologies and stewarding large amounts of data around the world will need to act to mitigate these risks. This could be through stronger international policy agreements related to climate change, or via responsible data sharing in times of natural disasters or climate-related catastrophes.

This blog will explore the concept of responsibility, which pertains to being accountable for actions and their effects on others, a common principle that applies both to data stewardship (defined as 'the collection, maintenance and sharing of data') and to the environment. There is even a commonality between how people use the language of responsibility and stewardship for both the planet and data.

Environmental sustainability is an important consideration for organisations who want to steward data responsibly

At the ODI, we've been working on the topic of responsible data stewardship, which seeks to explore how organisations stewarding data can do so 'responsibly'. In our work we found responsibility to be a multidimensional concept, which should include a commitment to sustainability.

We consider responsible data stewardship as 'an iterative, systemic process of ensuring that data is collected, used and shared for public benefit, mitigating the ways that data can produce harm, and addressing how it can redress structural inequalities.' Responsible data stewardship involves a holistic outlook that goes beyond merely addressing privacy and security concerns to encompass ecological obligations and the pursuit of sustainability.

Overview of practices aiming to mitigate the digital impact on the planet

With projections indicating that the data industry will contribute to a larger share of carbon emissions than the automotive, airline, and energy sectors combined. As such, it is critical that governments and organisations not only tackle traditional carbon emissions but also address digital decarbonisation. This section provides some examples of how organisations are mitigating the impact of data on the planet.
Measuring the digital carbon footprint for future decarbonisation strategies
Measuring and monitoring the environmental impact of data stewardship appears to be the first step to any decarbonisation strategy at an organisation level. Several initiatives have been launched in order to address this challenge.

While a variety of tools exist to evaluate the carbon footprint associated with digital activities like data storage, they predominantly operate in a 'reactive' manner -assessing and gauging CO2 impacts post-execution. A case in point is cloud providers who offer CO2 assessments of your digital cloud footprint through a customer dashboard. Though valuable, these tools primarily facilitate retrospective action, such as reducing dark data, only after the generation of greenhouse gas emissions has transpired.

Recognising the challenge confronting organisations and decision-makers who want a more proactive approach to digital sustainability, Loughborough University have developed a tool that enables organisational teams to forecast the data CO2 footprint of any upcoming project.

The Data Carbon Ladder is an empirically driven diagnostic instrument, offering a structured approach encompassing five pivotal considerations for data teams during the project planning phase:

1. Dis(aggregation) Strategy: Evaluating how the new data will be aggregated or disaggregated, influencing the resulting CO2 output. This involves decisions related to data importing, or integration with existing datasets.

2. Dataset Size: Computing the carbon score of the proposed dataset, based on its size (measured in megabytes, gigabytes, terabytes, petabytes).

3. Data Velocity: Assessing the data's velocity against a project's requirements for real-time information (measured in megabytes, gigabytes, terabytes, petabytes over one month).

4. Storage Strategy: Defining the intended data storage approach, which could include no storage, storage in a data centre, or on-premises storage.

5. Analytics Type: Choosing the category of data analytics to be performed-ranging from low carbon impact (descriptive analytics) to moderately carbon-intensive (prescriptive, predictive) and highly carbon-intensive (cognitive analytics).

By employing these decision guidelines project teams can swiftly ascertain the data CO2 footprint of their proposed initiatives. This exercise reveals potential CO2 hotspots, enabling teams to introspect and evaluate how data can be harnessed more effectively for their organisations while positively contributing to their ecological responsibilities. The Data Carbon Ladder offers an opportunity to embrace a proactive stance in digital sustainability efforts, fostering environmentally conscious decision-making and shaping a more responsible digital landscape.
Discussing the movement towards data minimisation and digital sobriety
The concept of data minimisation, primarily considered as a principle to adhere to within GDPR, holds promise as a way to reduce the climate impact of data processing and practices associated with it. This principle goes beyond privacy concerns and encompasses the idea that " less is more" when it comes to collecting and storing data. Data is everywhere, every connected individual or organisation is handling vast amounts of data on a daily basis - but is all of this data necessary and useful? Tools like the Data Carbon Ladder are helping organisations to understand what data is useful and solutions to more sustainable data practices are becoming more common, for example using less technological devices internally, using a shared platform and reducing the amount of email exchanges. There are also voices from the private sector advocating for more sustainable data practices, for example, the Economist argues for the elimination of storage waste, realising the value of small/dark data, optimising networks and data transmission.

Going even further than data minimisation, data removal or erasure would be to delete non-relevant data to minimise the data storage of a given activity. It could be an interesting avenue to explore to encourage organisations to think about how they use data and the environmental impact of that use and storage. For example, repurposing the RAD tipsheet practical guide/resource put together by The Engine Room for environmental purposes.
Policy and governmental frameworks should integrate efforts for digital decarbonisation
Governments and international organisations have a key role to play in supporting and encouraging stakeholders to steward data for environmental purposes and in the most sustainable way possible. The literature on the topic illustrates that data stewardship is not taken into account in sustainability frameworks, such as ESG. Similarly environmental sustainability is not always embedded within data governance frameworks. This is an important moment to act in this space. For example, B-Corp is launching a second round of public consultation as part of the review of their certification requirements. This presents an opportune moment to champion the inclusion of a criterion addressing the ecological cost of data and digital technologies.
A cultural shift is needed to address digital decarbonisation
Achieving environmental sustainability in responsible data stewardship requires a cultural shift. There is a lack of understanding of the value that responsible data stewardship in addressing climate change could constitute.

Public perceptions around the environmental impact of the meat or the airline industries seem much more developed and structured than the one around the environmental cost of data digital technologies. Research, documentaries, and public awareness campaigns have risen in the last decade contributing to sensitising public opinions and policies about the environmental cost of those industries. Widespread understanding of the true environmental cost of the data stewardship and practices would be a powerful tool in achieving cultural shift by:

Embedding understanding of the scale of the phenomenon, thereby driving ambitious policy-making.

Sharing good practices that could inspire and educate other organisations across an ecosystem.

Mitigating harmful scenarios where organisations transition from offline to online services, inadvertently causing adverse environmental consequences.

Boosting innovation in sustainable technologies and data practices

Enhancing collaborative efforts, e.g. data sharing partnerships, research collaborations, towards reducing the digital carbon footprint

For that matter, it is essential that sustainability is included in all dialogue around digital and data ethics.

Conclusion

The risks to environmental sustainability will only increase as data and technology continue to proliferate. Data stewardship can be seen as a way to address climate change. However, this approach must also acknowledge that data practices generate carbon emissions, adding to the larger issue. Therefore, there is an urgency to concretely connect these elements and increasingly integrate them into public environmental discourse.

Embedding environmentally-friendly data stewardship practices requires involvement and commitment from various groups, including academia, third-sector research, policy-makers and the public, just like the initiative Climate Change AI demonstrates.

If you advocate for climate change within civil society, private companies, policy-making or the tech sector and understand the impacts of data on our environment, come and speak to us about Responsible Data Stewardship by sending us an email on [email protected] . Feel free to share your valuable insights or any responsible practices you may have come across.

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Why we need to be responsible about data and the environment

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The risks to environmental sustainability will only increase as data and technology continue to proliferate. Data stewardship can be seen as a way to address climate change.

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This report sets out the findings of research to define responsible data stewardship undertaken by the Open Data Institute (ODI) between June 2022 and March 2023, supported by the Patrick J. McGovern Foundation.
The right kind of access to data is vital in tackling the big challenges we face in society - from the earlier detection and treatment of disease to reducing pollution in urban spaces. However, data and related technologies can also cause harm, including through automating decisions that need a human touch, or embedding existing biases and inequities.

In response to rising awareness of the harms and negative impacts of data, different concepts have emerged that represent new ways of thinking about how it should be used, including things like 'data ethics' and 'data justice'. One of the concepts we've used at the ODI is data stewardship, generally describing it simply as 'the collection, maintenance and sharing of data'. In our work on data institutions in recent years, we've expanded on this by suggesting that these organisations are stewarding data 'on behalf of others towards public, educational or charitable aims'.

However, at the outset of this research, our understanding of what makes for good data stewardship was largely anecdotal and based mainly on our experiences with specific use cases. We were driven to develop and articulate an interpretation of what responsible data stewardship means to add a normative element to our description of data stewardship. This definition provides a more critical lens that we can use in our work to help others design and practice data collection, use and sharing. We understand responsible data stewardship to be:

an iterative, systemic process of ensuring that data is collected, used and shared for public benefit, mitigating the ways that data can produce harm, and addressing how it can redress structural inequalities.

Iterative: To us, responsible data stewardship is a negotiated and reflective process. Because contexts vary and change over time, mitigations and approaches to collecting, maintaining and sharing data need to constantly evolve.

Systemic: The impacts of data collection and use are rarely fully within the control of any one organisation, so organisations need to develop a systemic view of their data practices that links how choices made around data have impacts outside of the organisation.

Public benefit: Stewarding data responsibly involves ensuring it's used and shared for the benefit of others, rather than only for the benefit of the organisation that holds it.

Harm: Alongside seeking positive impact from the use of data, responsible data stewardship involves identifying and reducing harmful impacts to individuals and communities, often going above and beyond legal requirements around privacy, security and transparency.

Redress structural inequalities: Data stewardship always occurs within a wider system of relationships, value exchanges and power imbalances, which have real-world consequences for data. Responsible data stewardship may involve meaningful new communication with data subjects and other stakeholders, or adopting alternative forms of governance.

Key findings from the research

In this report we discuss some of the prominent concepts we came across in our work, including 'data stewardship', 'data ethics', 'data justice', 'data for good' and more. We discuss how these narratives have helped people to think about how data should be collected, used and shared.

We explore the concept of 'responsible data', and how it's used by civil society organisations, organisations stewarding data, governments, international NGOs and large technology organisations. We conclude that it's used inconsistently and with varying depth of thought, and that its meaning differs significantly based on geographical and cultural context.

But what does being responsible with data mean in practice? We investigate different ways that the concept has shaped behaviours, policies and processes in the real world. We found examples of efforts designed to protect privacy, address biases and enable participation in data practices. We also document interventions that have been developed to support and compel organisations to use data responsibly - including frameworks, technologies, training and legislative action.

What's next?

We recognise that there is an appetite for more practical guidance and support in this area. Our research has highlighted that, in isolation, high-level principles can be difficult to apply and therefore may have limited impact. In our next phase of work on responsible data stewardship, we will explore the potential interventions that we could take to support organisations and operationalise the learnings of this research. We intend to put particular emphasis on concepts such as 'public benefit', 'reducing harm' and 'redressing structural inequalities', exploring what's needed to practise them in different organisational, geographic and sectoral contexts.

With this research, we want to bring people together to coalesce around how to critically and responsibly think about data and digital technologies to address ecological, democratic, and societal concerns, ensuring that data and technology are used to create a world where data works for everyone.

Finally, we are keen for feedback on this work. We're interested in any emerging normative concepts and language we may have missed in our research, efforts to practise responsible data stewardship in the real world and views on our interpretation of responsible data stewardship.

If you have any thoughts or feedback on this work, or would like to collaborate on the topic of responsibility please get in touch via email at [email protected] .

Read the report

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In this report, we place responsible data amongst other concepts which describe how data should be used. From our research we then develop an interpretation of responsible data stewardship to use in our work.

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We are now working with water and wastewater service provider United Utilities’ to support the development of its open data strategy

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We are now working with water and wastewater service provider United Utilities’ to support the development of its open data strategy

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The ODI is working with water and wastewater services provider United Utilities on an exciting project to support the development of its open data strategy.

United Utilities provides water and wastewater services to 7 million customers across the North West of England. The ODI will be supporting the company to develop and implement an open data strategy to set out its ambition, strategy, governance and plan for implementing open data, and becoming an open data leader in the water sector.

The water sector faces a challenging external environment - whether that is from the impact of climate change on water resources or flood risk, or increasing expectations from customers to deliver better services and to protect and enhance the natural environment. United Utilities recognises how open data can transform its approach to tackling these challenges by driving innovative solutions, raising awareness of performance and ensuring the best decisions are made to deliver outcomes for customers and the environment.

To begin with, the project will pilot the release of open data sets to generate improvements in water and environmental resources management, alongside creating open data processes. For the longer term, the focus will be on firmly establishing the culture and practices of open data to help the company realise the value from better management of data.

Building on United Utilities' existing open data strategy plans, which are aligned with OFWAT's H2Open principles, the ODI will support the organisation to better understand current and potential user needs and assess and improve open data maturity. The company is a partner and strong advocate of the Stream collaboration project alongside the ODI, and the open data strategy will complement and support the initiative.
Tim Stanley, Head of Data and Analytics at United Utilities, said: "Open data gives us a great opportunity to generate innovative solutions to some of the challenges we face as a service provider and generate value for our customers by encouraging new ways of thinking. Delivering open data and the foundations needed to sustain and grow our open data capability is a core part of our data strategy."

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Supporting the development of United Utilities' open data strategy

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The ODI is working with water and wastewater services provider United Utilities on an exciting project to support the development of its open data strategy

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On rethinking information flows of env data from citizens to authorities rather than vice versa, see

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On rethinking information flows of env data from citizens to authorities rather than vice versa, see

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On the status of implementation of the Aarhus Convention, see “The Aarhus Convention’s new frontiers” just out in the last edition of ‘Environmental Scientist’, the journal of the Institution of Environmental Sciences, available in OA at https://www.the-ies.org/resources/seeking-natural-justice

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On the status of implementation of the Aarhus Convention, see “The Aarhus Convention’s new frontiers” just out in the last edition of ‘Environmental Scientist’, the journal of the Institution of Environmental Sciences, available in OA at https://www.the-ies.org/resources/seeking-natural-justice

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Artificial intelligence (AI) holds the potential for both great public good, but also, through algorithmic bias or nefarious use, great societal harm. This means the stakes are very high, and AI needs to be managed responsibly. While governments cannot ignore the potential of AI, it is incumbent upon them to ensure the use of government-held data is for the benefit of all, with a strong emphasis on protecting vulnerable groups and entrenching ethical principles and human rights.

Government data has immense value for the development of AI systems in both public services and private sector innovations. However, the sharing of government-held data with private AI developers raises critical legal and ethical concerns for governments and society. This is because of the sensitivity of data processed, and the potential harm citizens could face through the sharing of such data.

To offer guidance to governments around the sharing of data in a way that is grounded in the principles of human rights, inclusion, diversity, innovation, and economic growth, Research ICT Africa (RIA) was commissioned by the International Center of Expertise in Montreal on Artificial Intelligence (CEIMIA) to undertake a project with the Global Partnership on Artificial Intelligence (GPAI). This work resulted in a report titled ' The Role of Government as a Provider of Data for Artificial Intelligence '.

The report establishes key principles that should govern when and how governments share data for AI development. These include principles around legal concerns such as the creation of public trust, the importance of data collaboration, accountability, transparency, and human oversight, among others. Key case studies have proved insightful into the issues faced, and the report looks at several case studies across the globe, including the National Health Service (NHS) in the United Kingdom (UK), and the implementation of a social protection programme to make cash transfers during the COVID-19 pandemic in Nigeria.

Based on the learnings drawn from the case studies, RIA assessed the legal landscape covering anti-trust laws, cross-border data flows, intellectual property laws, data protection, access to information, and relevant infrastructure.

The case studies raised several issues, and our recommendations address different themes including:

Building public trust in AI

The need for data collaboration

Algorithmic decision-making and the need for human oversight

Tackling digital inequality

Data and AI justice

Regulatory certainty and efficient redress mechanisms

Robust public procurement process for AI development

Transparency and accountability and

The role of AI in advancing a development agenda

The principles recommended for government data sharing for AI development within the GPAI report are also guided by the responsible AI principles of public benefit, accountability, data subject participation, data equity and data justice, and more.

Included in our work for the report was the hosting of several foresight workshops with experts from Africa, Latin America, and the GPAI Data Governance Working Group. Here participants were taken through future scenario scoping exercises to imagine The objective of the workshops was to explore future uses of government data for AI development and key risks and challenges for the responsible, sustainable, and rights-respecting provision of government data for AI. The outcomes were incorporated into the report to ensure comparative insights and globally inclusive advice for governments.

Download The Role of Government as a Provider of Data for Artificial Intelligence.

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CEIMIA/GPAI: The Role of Government as a Provider of Data for Artificial Intelligence

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This blog delineates the efforts undertaken in exploring the government's role as a data provider for AI.

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Part of the Global Transformations in Media and Communication Research - A Palgrave and IAMCR Series book series (GTMCR)

Introduction

China has the second-largest internet market in the world. With the rapid creation and adaptation of digital platforms and e-commerce, the access to, collection and dissemination of data have become the focus of academic debate and policymaking. Three factors contributed to these developments: (1) the internet and data are perceived in China as an important driving force for economic development and an important manifestation of social vitality; (2) with the rapid development of the platform economy, the mass production of data has raised governance problems in relation to the storage, transmission and use of data; and (3) the role of digital social media platforms in data access and dissemination has strengthened the public demand for the government to protect the right to information in China. It is within this context that the question on the right to access to data in academic research, policy and regulation becomes the research focus of this chapter.

The primary data used for the analysis include policy documents and regulations produced by the Chinese government concerning data access, the right to information and data protection. The secondary data include academic literature, policy research and news media reports.

Epistemic Rights and Right to Data Access

According to the definition given by Lani Watson ( 2021), epistemic rights are closely linked to the creation and dissemination of knowledge-relating not only to being informed but also to being informed truthfully, understanding the relevance of information and acting on its basis to benefit themselves and society as a whole. Hannu Nieminen's chapter in this volume also highlights the equality nature of epistemic rights, such as equality in the access to and availability of information and knowledge, and equality in obtaining critical literacy in information and communication.

While often thought of as pure information, data is a form of knowledge. As argued by Gitelman and Jackson ( 2013, p. 4), 'raw data is an oxymoron' and 'data does not just exist' (Manovich, 2001, p. 224). The three concepts of data, information and knowledge are interrelated, but the nature of the relations among them as well as their meanings are debatable. Many scholars claim that data is the raw material for information and that information is the raw material for knowledge (Zins, 2007, p. 479). In this chapter, data is defined as a set of symbols representing a perception of raw factors. Information is organised data that has been processed into a form that is meaningful to the recipient; knowledge is understood information (Davis & Olson, 1985; Debons et al., 1988; Zins, 2007), and digital data is a set of symbols made up of units of binary code that are intended to be stored, processed and transmitted by digital computers (Zins 2007, p. 482). Personal data refers to any information that is related to an identified or identifiable natural person (Art. 4 (1), GDPR, 2016). Public data refers to the information collected, produced, or paid for by the public or government bodies. Enterprise data refers to the data collected and processed by market entities in production and business activities that do not involve personal information. Commercial data refers to proprietary data commercialised by a company and sold by professional data providers with commercial support. It needs to be imagined as data to exist and function, and the imagination of data involves interpretation. Therefore, data, as a form of knowledge, is created through social processes; its creation and definition therefore involve human agency and interpretation (Berger & Luckmann, 1967, p. 10; Haggart, 2019). As such, Chinese academic and policy debates on access to digital data and its regulation inevitably become a social construction process, involving different agencies and interpretations.

Underpinned by the normative criteria of epistemic rights discussed in this volume, this chapter examines the academic debate on access to digital data in China and its national policy. More precisely, this chapter discusses the conceptualisation of the right to access data in China and the related regulatory framework. It also considers the legitimacy of those rules in relation to the public's epistemic right to data.

Right to Access Data

In this chapter, the right to access data is defined as consisting of two elements: (1) a right to access public information (recognised as an individual human right by many jurisdictions and human rights bodies, see Riegner, 2017); and (2) an inclusive right for all members of society to benefit from the availability of data.

Viktor Mayer-Schonberger and Thomas Ramge ( 2022) define data as a non-rivalrous informational good, as opposed to a physical good, and a public good for accelerating innovation for the benefit of all. Access to data must align with the fundamental principles of free enterprise and open information flows. They argue that through control of access to data and monopoly of data as raw material, major technology companies could undermine the capacity for innovation, as they have less incentive to be disruptive. To address this problem, economic policy must focus on the structural issues of data access and drastically broaden access to data. In addition, data cannot legally be owned like physical property; affording an exclusive ownership right, such as the property right to data, is impractical due to the difficulties in restricting the use of data to a specific purpose or specific users, and trading data in the market is inefficient because the market cannot adequately perform its role as an allocation mechanism. A compulsory opening of the dataset is proposed by Mayer-Schonberger and Ramge ( 2022) to promote innovation capacity and crack down on the information-based domination derived from exclusive access to data. Thus, a competitive advantage will rely on extracting insights from data, not from access to data. The access mandate provides that non-confidential data should be open access, and that the direct exchange of data between the data holder and requester should be facilitated by an open system of data access.

Purtova ( 2015) argued that data is not a public good but a rivalrous resource. Without policy action to assign property rights, including no access and non-disclosure in personal data to the data subject, it will effectively make the individual defenceless in the face of corporate power, eroding the autonomy, privacy and right to informational self-determination of the individual.

In Europe, the EU Commission has adopted data access for all strategies, that is, data is to be available for access to all-whether public or private, big or small, start-up or giant. 'Big commercial digital players must accept their responsibility, including by letting Europeans access the data they collect. Europe's digital transition is not about the profits of the few but the insights and opportunities of the many' (von der Leyen, 2020, p. 2). The 2022 Data Governance Act allows the creation of common European data spaces in certain key areas: health, environment, energy, agriculture, mobility, finance, manufacturing, public administration, and skills. Data marketplaces, that is, online platforms where users can buy or sell data, will help new intermediaries to be recognised as trustworthy data organisers. Companies, individuals and public organisations can also share personal data for the benefit of society, i.e., data altruism (European Parliament, 2022). Meanwhile, it is suggested that the EU needs to establish a framework for business-to-government (B2G) data access and explore the creation of a cross-EU regulatory framework (European Commission, 2020).

In comparison to the European approach, in 2022, the World Economic Forum proposed that Data Marketplace Service Providers (or DMSPs) operate and manage data exchanges. These are defined as platforms where information, or the right to access certain information under certain conditions, can be traded in an open, efficient and accountable way and where participants in data exchanges would trade information collected in a wide range of fields, from healthcare to manufacturing (WEF, 2022).

Academic Debate on the Right to Access Digital Data in China

The right to access data has not been treated as an independent right for deliberation in China but has been considered as part of the debates on the right to information and data property rights.

Regarding the former, where the data are owned by the government, the right to access data is interpreted as part of the personal right to public information (Zhang, 2022). There are two views on ownership. According to one view, these data should be owned by the public because the source of the original data comes from the daily work of the government, the collection of data is publicly financed and the data is ultimately used in people's daily lives, so it is a public good and its ownership belongs to the people (Huang et al., 2018). Others argue that the data belong to the state, as 'the ownership of collective data is rooted in state ownership' (Song & Qiu, 2022).

For non-public data, the legal basis of the right to personal information is argued as the right to self-determination of information: any data controller or processor needs to obtain the 'expressed consent' of individuals before collecting, obtaining, and processing data, and data commercialisation that ignores the personal dignity of individuals attached to data should not be accepted. If data protection is not in place, it will damage the rights and interests of individuals and organisations and even cause social and economic risks. If overprotected, big data analytics will become impossible (Huang, 2023a). However, the right to access personal data is not explicitly discussed, and the equality nature of the epistemic right, such as equality to access and availability of information and knowledge, has drawn little attention among Chinese academics.

As mentioned, the right to access data is also treated as part of discussions on data's property rights. In other words, in contrast with the EU's GDPR approach, which does not define the ownership of data but regulates the access of data, the Chinese academic debate has revolved around data ownership. This is partly because data are largely not seen as a public good shared by consumers or companies. All activities of data collection, analysis and processing are aimed at unlocking the potential commercial value of data, providing personal information and protecting national security (Zhang, 2021). Therefore, pragmatically, the priority is to formulate a data trading system supported by the right to data ownership so that data can be traded to generate economic value. This is also partly triggered by the government's policy objective of using big data. Thus, the Chinese academic debate is heavily policy-driven.

Some scholars advocate the establishment of a dual-right structure in which the data subjects own the data, and the data processor owns the data's usufruct or operational rights (Shen, 2020; Long, 2017). It is also argued that data property rights should be assigned to data companies that collect and process data and that the rights of 'sensitive personal data' should be assigned to data subjects (Xu, 2018). Xiaodong Ding ( 2019) argued against the allocation of data ownership rights to individuals, as this would incur extremely high transaction and communication costs and overtake some of the data rights enjoyed by platforms, making it impossible for platforms to carry out certain normal business activities.

Mei Xiaying ( 2022), among very few other scholars, supports the public good nature of data and argues that data sharing should be the default position and that control of access to data requires justification because data is a natural public good. The construction of a data control system should be based on the premise of data sharing.

Notably, the most recent debate has re-oriented the focus from data ownership to the structural separation of data property rights into three separate rights-data holding rights, data process and use rights, and data product management rights. Meanwhile, data sharing is no longer about the sharing of original data but the sharing of data products. In other words, it is not the original data but the access to data to perform the calculation that is shared (Huang, 2022, 2023a, 2023b). A researcher at the State Council's development research centre has conceded that the current data trading model is difficult to sustain from the perspectives of actual needs and government policy (People's Posts and Telecommunications News, 2022). In practice, it is unclear whether individuals have data ownership rights or how they can exercise this right, and it is therefore impossible to talk about data trading rights and data revenue distribution (Zhou et al., 2022). The idea is to use technology such as privacy encryption to separate data ownership from data use rights so that data can be used but not shared and data usage can be controlled and measured. Government policy should focus on the development of data services to release data value under the premise of ensuring privacy and security (People's Posts and Telecommunications News, 2022).

Rules Governing Access to Digital Data in China

According to incomplete statistics, (draft) regulations on 'data' have sprung up all over the country. By the end of 2021, nearly 225 local legislations (including 67 local regulations and 158 local departmental rules) had been adopted (Bai & Li, 2022). The most important element of China's data strategy policy is that data is officially defined as a new factor of production besides land, labour, capital and entrepreneurship, and it builds the foundation for the country's digitalisation, connectivity and AI. To qualify as a factor of production, according to a Chinese economist who participated in the government's data strategy policy drafting, 'it must be a must-have basic resource in the production of goods and services; data can only qualify as factor of production if it is used in production and business activities and generate significant value' (Huang, 2023b).

First, for the collection of and access to personal data, China's Personal Information Protection Law (PPL) stipulates that the data collector can collect personal information only if it obtains the consent of the individual, if the collection is necessary for the conclusion and performance of a contract, for the performance of statutory duties or obligations, to respond to public health emergencies or for conducting news reporting and other acts in the public interest. If the collector wants to provide personal information collected about third parties, it shall inform the individual and obtain their consent. Additionally, the individual has the right to know, decide, rectify, restrict and refuse the process, and to delete, be forgotten and obtain an explanation and copy of the data.

Also, Article 47 establishes an obligation for data collectors to actively delete personal information if the purpose for collecting the data has been achieved, cannot be achieved or is no longer necessary, or if the collector stops providing products or services, if the storage period has expired when the individual withdraws consent. Against this legal backdrop, on December 12, 2022, after the State Council announced seizure of the use of health code apps, including both the communication travel card and health code, three mobile operators (China Telecom, China Mobile and China Unicom), the main data collectors of communication travel cards, announced that they would delete data related to users synchronously to ensure the security of personal information in accordance with the law. Personal information collected by them after de-identification and anonymisation will be provided to relevant government departments in a targeted manner through the joint prevention and control mechanism of the State Council. According to Article 4 of the PPL, if the personal information received by the government is anonymised, the government agency may independently use such information (Zhang, 2022).

Second, data circulation in China is driven by the state's policies. Between 2015 and 2022, the Party, the State Council and its ministries announced a series of policies on the access and trading of data. The policies define data as a new factor of production that should be traded according to market mechanisms, i.e., to maximise benefits and optimise efficiency based on market rules, prices and competition, to facilitate the country's economic development (Table 13.1).

Table 13.1 Major data policies in China

In 2022, China adopted the most important data policy to date: 'Building a Data Base System for Better Use of Data as Factor of Production'. The goal of this policy is to facilitate the compliance and efficient circulation and use of data, to empower the economy and to enable sharing among all people of the benefits created by the digital economy. It is estimated that the scale of China's data trading market is nearly one trillion RMB, and no one can ignore such an untapped market (Fuxi Institution, 2022). The policy creates an authorised data access and trading system based on three different types of data: public, enterprise and personal data. Different access policies are formulated for and applied to each type of data (see Table 13.2). The property right of data is separated into three rights: the right to hold data resources, the right to process and use data and the right to manage data products. Ownership of data is no longer discussed in policy formulation. The government will guide and regulate the data revenue distribution system to ensure both efficiency and fairness (Xinhua News Agency, 2022).

Table 13.2 Access policy on three types of data

The policy also supports different methods to circulate data and establish data exchange market systems at national, regional and industrial sector levels. However, the policy has not adequately addressed how the system can benefit individual data subjects. While personal privacy, data security and the right to data portability are protected in the policy, how individual data subjects can share the benefits deriving from data remains unclear.

Conclusion

Access to data as an aspect of epistemic rights has different but similar interpretations in Chinese and global contexts. First, epistemic rights in Western academic literature stress the sociological nature of the creation and dissemination of information and knowledge. Rights are underpinned by the normative criteria of equal access to and availability of information and knowledge and used for the benefit of individuals and society as a whole. Therefore, data as a form of knowledge is often defined as a non-rivalrous informational good for the benefit of all, and open access to and sharing of non-confident data is proposed. In the Chinese context, epistemic rights have not drawn the attention of Chinese academics, and the closely related concept of the right to information is often approached from a legal perspective, stressing consumer rights to obtain public information and digital platforms' data rights. Data are defined as one kind of factor of production for national economic development.

Notably, in China, the implications of the public good nature of data have not been considered in either mainstream academic publications or in the government's data policies, even though it is agreed that data has non-rivalrous and non-exhaustive characteristics and that, given information asymmetry, data cannot be circulated in the market like land, labour and capital. As a result, the public good and equal access dimensions of data are largely ignored in policymaking. Under the premise of protection of national security and personal privacy, data collection, analysis and processing are aimed at unlocking the potential commercial value of data, especially enterprise data. Therefore, defining the different types of property rights of data has been the main point of contestation in academic and policy debates.

Second, like what has been proposed by Viktor Mayer-Schonberger and Thomas Ramge ( 2022), the recent data access policy in China has shifted from the sharing of original data to the sharing of data products, from the trading of ownership rights to the trading of holding, processing, use and management rights of data. The establishment of a three-level data trading system in the national, regional, and industrial sectors will be the next step for academic research and policymaking. The government will also guide and regulate such developments to promote market efficiency and fairness in the distribution of the benefits of data trading. The public good nature of data and data altruism might not be on either the academic research or policymaking agenda, but the open nature of public data and sharing mechanisms are endorsed and encouraged in government policy.

Finally, while the rights and interests of data enterprises are the main subject of protection in China's latest data policy, the power imbalance between the individual and corporations (Purtova, 2015) and the sharing of benefits derived from data with individual users or data subjects have not been addressed.

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Authors and Affiliations

School of Journalism and Communication, Beijing Normal University, Beijing, China

Yik Chan Chin

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Correspondence to Yik Chan Chin .

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Editors and Affiliations

1. University of Helsinki, Helsinki, Finland

Minna Aslama Horowitz

2. University of Helsinki, Helsinki, Finland

Hannu Nieminen

3. Tampere University, Tampere, Finland

Katja Lehtisaari

4. University of Westminster, London, UK

Alessandro D'Arma

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Chin, Y.C. (2024). Right to Data Access in the Digital Era: The Case of China. In: Aslama Horowitz, M., Nieminen, H., Lehtisaari, K., D'Arma, A. (eds) Epistemic Rights in the Era of Digital Disruption. Global Transformations in Media and Communication Research - A Palgrave and IAMCR Series. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-45976-4_13

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Right to Data Access in the Digital Era: The Case of China

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This chapter examines the academic debate on access to digital data in China and the Chinese state's policy on data, demonstrating the lack of consideration of epistemic rights in regulating access to digital data in China and the interplay of global tendencies...

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https://ict.go.ke/directorate-of-government-advertising-agency-gaa/

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Ministry of Information, Communications and The Digital Economy

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INTRODUCTION
The Government Advertising Agency is a directorate under the State Department of Broadcasting and Telecommunication of the Ministry of Information, Communication and the Digital Economy. It was formed in July 2015 to authorize Public Sector Advertising and to implement sectorial standards, procedures, guidelines and managing consolidated Government Advertising funds vide Treasury Circular No. 09/2015.
FUNCTIONS OF THE GOVERNMENT ADVERTISING AGENCY
The GAA undertakes the following functions:

1. Authorization of Advertising in Ministries, State Corporations, and Universities and SAGAs under the National Government;

2. Formulation, development, implementation and review of Government Advertising policies, News Editing and Graphic Design policies, standards, procedures and guidelines;

3. Management of consolidated government advertising funds; compilation of quarterly advertising plans from Ministries, State Corporations, Universities and SAGAs under the National Government;

4. Preparation of advertising plans and budgets for Ministries, State Corporations, Universities and SAGAs under the National Government;

5. Publication of MyGov weekly newspaper and other publications for MDAs and Commissions; publication of MyGov weekly online edition;

6. Information gathering and packaging for publication in MyGov newspaper;

7. Coordination of production of special reports on government projects and programmes for publication in MyGov newspaper.

8. Consolidate Public Sector Advertising (PSA)

9. GAA undertakes print advertising for public sector players, and approves electronic advertising, which MDAs are allowed to undertake on their own upon approval. This is done in accordance with the GAA contract framework under Public Procurement Regulations.

10. Co-ordinate and manage Public Sector advertising contracts and tendering.

11. Articulate and propagate Government development agenda, policies, initiatives and achievements, publish government tender and employment opportunities through MyGov publication.

12. Approves all Public Sector Advertising and ensure the implementation of PSA standards and procedures.

13. Ensure compliance with all applicable advertising legislation, policies, and standards.

14. Approve GOK institutional annual advertising plans, develop a comprehensive GAP and provide guidelines for GoK advertising proposals.

15. Analyze PSA data, prepare reports and disseminate findings to the public.

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Directorate of Government Advertising Agency (GAA)

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Directorate of Government Advertising Agency (GAA) INTRODUCTION The Government Advertising Agency is a directorate under the State Department of Broadcasting and Telecommunication of the Ministry of Information, Communication and the Digital Economy.

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GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs)

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TREs are widely, and increasingly used to support statistical analysis of sensitive data across a range of sectors (e.g., health, police, tax and education) as they enable secure and transparent research whilst protecting data confidentiality. There is an increasing desire from academia and industry to train AI models in TREs. The field of AI is developing quickly with applications including spotting human errors, streamlining processes, task automation and decision support. These complex AI models require more information to describe and reproduce, increasing the possibility that sensitive personal data can be inferred from such descriptions. TREs do not have mature processes and controls against these risks. This is a complex topic, and it is unreasonable to expect all TREs to be aware of all risks or that TRE researchers have addressed these risks in AI-specific training. GRAIMATTER has developed a draft set of usable recommendations for TREs to guard against the additional risks when disclosing trained AI models from TREs. The development of these recommendations has been funded by the GRAIMATTER UKRI DARE UK sprint research project. This version of our recommendations was published at the end of the project in September 2022. During the course of the project, we have identified many areas for future investigations to expand and test these recommendations in practice. Therefore, we expect that this document will evolve over time. The GRAIMATTER DARE UK sprint project has also developed a minimal viable product (MVP) as a suite of attack simulations that can be applied by TREs and can be accessed here (https://github.com/AI-SDC/AI-SDC). If you would like to provide feedback or would like to learn more, please contact Smarti Reel (sreel@dundee.ac.uk) and Emily Jefferson (erjefferson@dundee.ac.uk). The summary of our recommendations for a general public audience can be found at DOI: 10.5281/zenodo.7089514

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Kerasidou A. and Kerasidou C. 2023. Data-driven research and healthcare: public trust, data governance and the NHS. BMC Medical Ethics, 24(1), p.51.

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Kerasidou A. and Kerasidou C. 2023. Data-driven research and healthcare: public trust, data governance and the NHS. BMC Medical Ethics, 24(1), p.51.

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Public Data for Environmental Development in Scotland and the EU workshop roundup

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Climate change may kill data sovereignty

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Climate change may kill data sovereignty

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Facilitating Data Flows through Data Collaboratives: A Practical Guide to Designing Valuable, Accessible, and Responsible Data Collaboratives

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Data is an indispensable asset in today's society, but its production and sharing are subject to well-known market failures. Among these: neither economic nor academic markets efficiently reward costly data collection and quality assurance efforts; data providers cannot easily supervise the appropriate use of their data; and, correspondingly, users have weak incentives to pay for, acknowledge, and protect data that they receive from providers.

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In September 2023, Connected by Data brought together a diverse group of civil society, government and academic stakeholders on the fringes of the 2023 Open Government Partnership Summit in Tallinn, Estonia, to co-design model policy commitments that could deliver meaningful transparency, participation and accountability in data and AI governance.

The workshop developed three commitment drafts, focusing on:

Participatory oversight of technology procurement through creation of multi-stakeholder oversight groups that are empowered to advise, question and publicly report on public sector data and AI procurement.

Deliberative development of data and AI strategies at both national and local levels through use of both open, and more targeted, participation processes.

Strengthening citizen voice within sectoral regulators to create a robust feedback loop and ensure data and AI firms have social licence to operate in the way they do.

This Connected Conversation shared these commitment areas in more detail, and invited responses from a range of experts to explore the opportunities and challenges for putting these ideas into practice.

Invited responses

We started the conversation with three invited responses:

Adam Zable, Director of Emerging Technologies at Digital Trade and Data Governance Hub, shared work on mapping data governance globally, which compared the extent of public participation in developing National AI Strategies across 69 jurisdictions. Adam described how

Of 43 countries which had a strategy, 18 had some public outreach in the process of its development.

Of these 18 countries, few went beyond consultation into deeper public engagement, and few governments shared evidence of how public input informed their strategy.

While no country can be held up as a model of responsive democratic decision making around AI, Adam outlined some best practice/good examples that can be seen in ongoing consultative processes, such as Malaysia's AI roadmap designed as a living document to be continually updated.

Adam also pointed to Chile as an exemplar for participatory process around AI, with their extensive engagement of citizens during the development of the first National Policy on Artificial Intelligence, and engagement positioned on the spectrum of participation towards involvement rather than simple consultation. Chile organised regional roundtables to obtain citizen feedback throughout the country, and the government also let people organise self-convened roundtables: sharing results online on an open platform.

Tonu Basu, outgoing Deputy Director of Thematic Policy Areas at the Open Government Partnership, emphasised how action on participation around AI is an important aspect of the wider OGP digital governance conversation, and work on AI strategy is very timely. The new OGP strategy encourages ambitious roadmap thinking to add up to larger impacts over 5 years, rather than a commitment by commitment approach, and work on AI and data governance should fit this approach.

Tonu outlined how effective commitments on AI will require cross agency convening and mandate, and it is important to include this in the commitment design. A siloed agency cannot convene across a government. Even in specific processes such as procurement, or within sectoral regulators, there need to be links built in across government for their work to be effective.

Tonu raised the debate to be had about algorithmic accountability policies, and whether and when these should be binding or not. Some countries have optional opt-in approaches, but this could also be approached through building on data protection law. Tonu suggested that it has yet to be seen, and will require more thought, about what is the most suitable approach.

It can be easy to say "we will establish a multi stakeholder group", but this needs more detail. How do we make sure that citizens and affected communities are brought in effectively and thoughtfully, and how do we make sure officials are empowered to do that? At what stage of strategy do we best involve the public? Clear milestones are needed to set this out.

Kristen Robinson, Head of Advocacy at Open Contracting Partnership, focused her response on procurement, as an area where we can have goal-focused and meaningful participation. She noted that the goal focus is essential for effective participation, to avoid participation fatigue.

However, Kristen argued that setting up a single tech procurement advisory committee as proposed in the draft OGP commitment may not be realistic given the very broad application of different technologies across different public services. One stakeholder forum would be unlikely to be able to cover all of procurement in this space.

Where stakeholder engagement could be more effective may be in pre market engagement with suppliers; this would be a good moment for more stakeholders to be brought in. Following this, it would be ideal to build relationships so ongoing implementation - during which new issues may emerge - has stakeholder engagement throughout. More broadly, it may work best to seek processes that already exist within which public participation can be added.

Furthermore, Kristen noted, staff training is essential to keep up to date with the implementation of changing technologies, add-ons etc., and flagging key changes during implementation that should be subject to consultation or public engagement.

Some of the best practices to learn from in procurement have been driven by patient rights organisations, parents groups and so on - people who have a stake in the outcomes. The procurement officer is unlikely to see the best options alone, and this demonstrates the value of participation.

Discussed recommendations

Following our invited responses, we heard wider responses to the draft commitments. Some recommendations included:

A need for building public capacity to engage, building from the grassroots up to push for proactive change.

A need for success stories, exemplars, and inspiration.

Thinking about data as infrastructure to find useful analogies and learn from other processes, such as planning frameworks.

More exploration and understanding of what prevents public participation, for example governments' innovation focus, corporate capture, etc.

Broader learning from other areas, such as environmental law, to support participation advocacy in AI and data governance.

Refining recommendations & supporting action

We were delighted to have the opportunity to gather constructive critical feedback on the outputs of our Tallinn Design Lab, and we're looking forward to continued work exploring how Open Government movements, and movements for collective, participatory and powerful public governance of data and AI can work together.

If you are working on an Open Government action plan, whether nationally, or as part of OGP local, and you want to talk about ways to develop an effective commitment around the governance of data and AI, we would love to hear from you.

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Connected Conversation: Powerful Actions on Open Government, Data & AI

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In September 2023, Connected by Data brought together a diverse group of civil society, government and academic stakeholders on the fringes of the 2023 Open Government Partnership Summit in Tallinn, Estonia, to co-design model policy commitments that could deliver meaningful transparency, participation and accountability in data and AI governance.

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27 Pages Posted: 8 Jan 2024

Date Written: December 12, 2023

Abstract

Data are often subject to a multitude of rights (e.g. original works or personal data posted on social media, or collected through captcha, subject to copyright, database and data protection) and voluntary shared through non standardized, non interoperable contractual terms. This leads to fragmented legal regimes and has become an even major challenge in the AI-era, for example when online platforms set their own Terms of Services (ToS), in business-to-consumer relationship (B2C).

This article proposes standard terms that may apply to all kind of data (including personal and mixed datasets subject to different legal regimes) based on the open data philosophy initially developed for Free and Open Source software and Creative Commons licenses for artistic and other copyrighted works. In a first part, we analyse how to extend open standard terms to all kinds of data (II). In a second part, we suggest to combine these open standard terms with collective governance instruments, in particular data trust, inspired by commons-based projects and by the centennial collective management of copyright (III). In a last part, after few concluding remarks (IV), we propose a template "Open Data Commons Licenses" (ODCL) combining compulsory and optional elements to be selected by licensors, illustrated by pictograms and icons inspired by the bricks of Creative Commons licences and legal design techniques (V).

This proposal addresses the bargaining power imbalance and information asymmetry (by offering the licensor the ability to decide the terms), and conceptualises contract law differently. It reverses the current logic of contract: instead of letting companies (licensees) impose their own ToS to the users (licensors, being the copyright owner, data subject, data producer), licensors will reclaim the ability to set their own terms for access and use of data, by selecting standard terms. This should also allow the management of complex datasets, increase data sharing, and improve trust and control over the data. Like previous open licencing standards, the model is expected to lower the transaction costs by reducing the need to develop and read new complicated contractual terms. It can also spread the virality of open data to all data in an AI-era, if any input data under such terms used for AI training purposes propagates its conditions to all aggregated and output data. In other words, any data distributed under our ODCL template will turn all outcome into more or less open data and foster a data common ecosystem. Finally, instead of full openness, our model allows for restrictions outside of certain boundaries (e.g. authorized users and uses), in order to protect the commons and certain values. The model would require to be governed and monitored by a collective data trust.

Keywords: open licenses, open data, data commons, data trusts, license proposal, data governance

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open data commons licenses (odcl): licensing personal and non personal data supporting the commons and privacy

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Open Data Commons Licenses (ODCL): Licensing Personal and Non Personal Data Supporting the Commons and Privacy

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Data are often subject to a multitude of rights (e.g. original works or personal data posted on social media, or collected through captcha, subject to copyright

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Situating Data Sets: Making Public Data Actionable for Housing Justice

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Activists, governmentsm and academics regularly advocate for more open data. But how is data made open, and for whom is it made useful and usable? In this paper, we investigate and describe the work of making eviction data open to tenant organizers. We do this through an ethnographic description of ongoing work with a local housing activist organization.