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This graphic presents five core pillars of ethical AI: fairness and non-harm; transparency; explainability; data protection; and human autonomy and control. It simplifies complex ethical concepts into accessible categories that can apply across different AI systems. The visual format helps clarify the guiding principles that developers and organizations should follow; it complements my other sources by reinforcing the foundational values behind responsible AI design.

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In this TED talk, AI expert Toby Walsh discusses the urgent ethical challenges posed by artificial intelligence. He highlights the risks of letting machines make life-or-death decisions, such as in autonomous weapons, and emphasizes the need for strong ethical frameworks and human oversight. Walsh makes a compelling case for designing AI systems that prioritize accountability, transparency, and moral responsibility.

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Professor Walsh is a “rock star” of Australia’s digital revolution and a leading researcher in Artificial Intelligence. ‘Bots behaving badly: In his TEDxBlighStreet talk, Toby speaks about ‘good old-fashioned bad behaviour’ in a thoroughly modern context, and the ethical implications of Artificial Intelligence. He is Scientia Professor of Artificial Intelligence at UNSW, leads the Algorithmic Decision Theory group at Data61, Australia’s Centre of Excellence for ICT Research, and is Guest Professor at TU Berlin. He has been elected a fellow of the Australian Academy of Science, and has won the prestigious Humboldt research award as well as the NSW Premier’s Prize for Excellence in Engineering and ICT.

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This article by Thilo Hagendorff examines 22 major AI ethics guidelines. It finds that many are vague, overlapping, and lack real enforcement mechanisms. The author critiques the current state of AI ethics as largely symbolic; he offers practical recommendations to improve accountability and ensure real-world impact. This article deepens the conversation on AI ethics; it shows that strong ethical principles must be clearly defined and supported by effective regulation, not just stated as idealistic goals.

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The Ethics of AI Ethics: An Evaluation of Guidelines

Abstract

Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the "disruptive" potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compares 22 guidelines, highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems-and how the effectiveness in the demands of AI ethics can be improved.

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1 Introduction

The current AI boom is accompanied by constant calls for applied ethics, which are meant to harness the "disruptive" potentials of new AI technologies. As a result, a whole body of ethical guidelines has been developed in recent years collecting principles, which technology developers should adhere to as far as possible. However, the critical question arises: Do those ethical guidelines have an actual impact on human decision-making in the field of AI and machine learning? The short answer is: No, most often not. This paper analyzes 22 of the major AI ethics guidelines and issues recommendations on how to overcome the relative ineffectiveness of these guidelines.

AI ethics-or ethics in general-lacks mechanisms to reinforce its own normative claims. Of course, the enforcement of ethical principles may involve reputational losses in the case of misconduct, or restrictions on memberships in certain professional bodies. Yet altogether, these mechanisms are rather weak and pose no eminent threat. Researchers, politicians, consultants, managers and activists have to deal with this essential weakness of ethics. However, it is also a reason why ethics is so appealing to many AI companies and institutions. When companies or research institutes formulate their own ethical guidelines, regularly incorporate ethical considerations into their public relations work, or adopt ethically motivated "self-commitments", efforts to create a truly binding legal framework are continuously discouraged. Ethics guidelines of the AI industry serve to suggest to legislators that internal self-governance in science and industry is sufficient, and that no specific laws are necessary to mitigate possible technological risks and to eliminate scenarios of abuse (Calo 2017). And even when more concrete laws concerning AI systems are demanded, as recently done by Google ( 2019), these demands remain relatively vague and superficial.

Science- or industry-led ethics guidelines, as well as other concepts of self-governance, may serve to pretend that accountability can be devolved from state authorities and democratic institutions upon the respective sectors of science or industry. Moreover, ethics can also simply serve the purpose of calming critical voices from the public, while simultaneously the criticized practices are maintained within the organization. The association "Partnership on AI" ( 2018) which brings together companies such as Amazon, Apple, Baidu, Facebook, Google, IBM and Intel is exemplary in this context. Companies can highlight their membership in such associations whenever the notion of serious commitment to legal regulation of business activities needs to be stifled.

This prompts the question as to what extent ethical objectives are actually implemented and embedded in the development and application of AI, or whether merely good intentions are deployed. So far, some papers have been published on the subject of teaching ethics to data scientists (Garzcarek and Steuer 2019; Burton et al. 2017; Goldsmith and Burton 2017; Johnson 2017) but by and large very little to nothing has been written about the tangible implementation of ethical goals and values. In this paper, I address this question from a theoretical perspective. In a first step, 22 of the major guidelines of AI ethics will be analyzed and compared. I will also describe which issues they omit to mention. In a second step, I compare the principles formulated in the guidelines with the concrete practice of research and development of AI systems. In particular, I critically examine to what extent the principles have an effect. In a third and final step, I will work out ideas on how AI ethics can be transformed from a merely discursive phenomenon into concrete directions for action.

2 Guidelines in AI Ethics

2.1 Method

Research in the field of AI ethics ranges from reflections on how ethical principles can be implemented in decision routines of autonomous machines (Anderson and Anderson 2015; Etzioni and Etzioni 2017; Yu et al. 2018) over meta-studies about AI ethics (Vakkuri and Abrahamsson 2018; Prates et al. 2018; Boddington 2017; Greene et al. 2019; Goldsmith and Burton 2017) or the empirical analysis on how trolley problems are solved (Awad et al. 2018) to reflections on specific problems (Eckersley 2018) and comprehensive AI guidelines (The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems 2019). This paper mainly deals with the latter issue. The list of ethics guidelines considered in this article therefore includes compilations that cover the field of AI ethics as comprehensively as possible. To the best of my knowledge, a few preprints and papers are currently available, which also deal with the comparison of different ethical guidelines (Zeng et al. 2018; Fjeld et al. 2019; Jobin et al. 2019). While especially the paper from Jobin et al. ( 2019) is a systematic scoping review of all the existing literature on AI ethics, this paper does not aim at a full analysis of every available soft-law or non-legal norm document on AI, algorithm, robot, or data ethics, but rather a semi-systematic overview of issues and normative stances in the field, demonstrating how the details of AI ethics relate to a bigger picture.

The selection and compilation of 22 major ethical guidelines were based on a literature analysis. This selection was undertaken in two phases. In the first phase, I searched different databases, namely Google, Google Scholar, Web of Science, ACM Digital Library, arXiv, and SSRN for hits or articles on "AI ethics", "artificial intelligence ethics", "AI principles", "artificial intelligence principles", "AI guidelines", and "artificial intelligence guidelines, following every link in the first 25 search results, while at the same time ignoring duplicates in the search process. During the analysis of the search results, I also sifted through the references in order to manually find further relevant guidelines. Furthermore, I used Algorithm Watch's AI Ethics Guidelines Global Inventory, a crowdsourced, comprehensive list of ethics guidelines, to check whether I missed relevant guidelines. Via the list, I found three further guidelines that meet the criteria for the selection. In this context, a shortcoming one has to consider is that my selection is biased towards documents which are western/northern in nature, excluding guidelines which are not written in English.

I rejected all documents older than 5 years in order to only take guidelines into account that are relatively new. Documents that only refer to a national context-such as for instance position papers of national interest groups (Smart Dubai Smart Dubai 2018), the report of the British House of Lords (Bakewell et al. 2018), or the Nordic engineers' stand on Artificial Intelligence and Ethics (Podgaiska and Shklovski)-were excluded from the compilation. Nevertheless, I included the European Commission's "Ethics Guidelines for Trustworthy AI" (Pekka et al. 2018), the Obama administration's "Report on the Future of Artificial Intelligence" (Holdren et al. 2016), and the "Beijing AI Principles" (Beijing Academy of Artificial Intelligence 2019), which are backed by the Chinese Ministry of Science and Technology. I have included these three guidelines because they represent the three largest AI "superpowers". Furthermore, I included the "OECD Principles on AI" (Organisation for Economic Co-operation and Development 2019) due to their supranational character. Scientific papers or texts that fall into the category of AI ethics but focus on one or more specific aspects of the topic were not considered either. The same applies to guidelines or toolkits, which are not specifically about AI but rather about big data, algorithms or robotics (Anderson et al. 2018; Anderson and Anderson 2011). I further excluded corporate policies, with the exception of the "Information Technology Industry AI Policy Principles" ( 2017), the principles of the "Partnership on AI" ( 2018), the IEEE first and second version of the document on "Ethically Aligned Design" (The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems 2016, 2019), as well as the brief principle lists of Google ( 2018), Microsoft ( 2019), DeepMind (DeepMind), OpenAI ( 2018), and IBM (Cutler et al. 2018) which have become well-known through media coverage. Other large companies such as Facebook or Twitter have not yet published any systematic AI guidelines, but only isolated statements of good conduct. Paula Boddington's book on ethical guidelines ( 2017) funded by the Future of Life Institute was also not considered as it merely repeats the Asilomar principles ( 2017).

The decisive factor for the selection of ethics guidelines was not the depth of detail of the individual document, but the discernible intention of a comprehensive mapping and categorization of normative claims with regard to the field of AI ethics. In Table 1, I only inserted green markers if the corresponding issues were explicitly discussed in one or more paragraphs. Isolated mentions without further explanations were not considered, unless the analyzed guideline is so short that it consists entirely of brief mentions altogether.

Table 1 Overview of AI ethics guidelines and the different issues they cover

2.2 Multiple Entries

As shown in Table 1, several issues are unsurprisingly recurring across various guidelines. Especially the aspects of accountability, privacy or fairness appear altogether in about 80% of all guidelines and seem to provide the minimal requirements for building and using an "ethically sound" AI system. What is striking here is the fact that the most frequently mentioned aspects are those for which technical fixes can be or have already been developed. Enormous technical efforts are undertaken to meet ethical targets in the fields of accountability and explainable AI (Mittelstadt et al. 2019), fairness and discrimination aware data mining (Gebru et al. 2018), as well as privacy (Baron and Musolesi 2017). Many of those endeavors are unified under the FAT ML or XAI community (Veale and Binns 2017; Selbst et al. 2018). Several tech-companies already offer tools for bias mitigation and fairness in machine learning. In this context, Google, Microsoft and Facebook have issued the "AI Fairness 360" tool kit, the "What-If Tool", "Facets", "fairlern.py" and "Fairness Flow", respectively (Whittaker et al. 2018).

Accountability, explainability, privacy, justice, but also other values such as robustness or safety are most easily operationalized mathematically and thus tend to be implemented in terms of technical solutions. With reference to the findings of psychologist Carol Gilligan, one could argue at this point that the way AI ethics is performed and structured constitutes a typical instantiation of a male-dominated justice ethics (Gilligan 1982). In the 1980s, Gilligan demonstrated in empirical studies that women do not, as men typically do, address moral problems primarily through a "calculating", "rational", "logic-oriented" ethics of justice, but rather interpret them within a wider framework of an "empathic", "emotion-oriented" ethics of care. In fact, no different from other parts of AI research, the discourse on AI ethics is also primarily shaped by men. My analysis of the distribution of female and male authors of the guidelines, as far as authors were indicated in the documents, showed that the proportion of women was 41.7%. This ratio appears to be close to balance. However, it should be considered that the ratio of female to male authors is reduced to a less balanced 31.3% if the four AI Now Reports are discarded, which come from an organization that is deliberately led by women. The proportion of women is lowest at 7.7% in the FAT ML community's guidelines which are focused predominantly on technical solutions (Diakopoulos et al.). Accordingly, the "male way" of thinking about ethical problems is reflected in almost all ethical guidelines by way of mentioning aspects such as accountability, privacy or fairness. In contrast, almost no guideline talks about AI in contexts of care, nurture, help, welfare, social responsibility or ecological networks. In AI ethics, technical artefacts are primarily seen as isolated entities that can be optimized by experts so as to find technical solutions for technical problems. What is often lacking is a consideration of the wider contexts and the comprehensive relationship networks in which technical systems are embedded. In accordance with that, it turns out that precisely the reports of AI Now (Crawford et al. 2016, 2019; Whittaker et al. 2018; Campolo et al. 2017), an organization primarily led by women, do not conceive AI applications in isolation, but within a larger network of social and ecological dependencies and relationships (Crawford and Joler 2018), corresponding most closely with the ideas and tenets of an ethics of care (Held 2013).

2.3 Omissions

Despite the fact that the guidelines contain various parallels and several recurring topics, what are issues the guidelines do not discuss at all or only very occasionally? Here, I want to give a (non-exhaustive) overview of issues that are missing. Two things should be considered in this context. First, the sampling method used to select the AI ethics guidelines has an effect on the list of issues and omissions. When deliberately excluding for instance robot ethics guidelines, this has the effect that the list of entries lacks issues that are connected with robotics. Second, not all omissions can be treated equally. There are omissions which are missing or severely underrepresented without any good reason-for instance the aspect of political abuse or "hidden" social and ecological costs of AI systems-, and omissions that can be justified-for instance deliberations on artificial general intelligence or machine consciousness, since those technologies are purely speculative.

Nevertheless, in view of the fact that significant parts of the AI community see the emergence of artificial general intelligence as well as associated dangers for humanity or existential threats as a likely scenario (Müller and Bostrom 2016; Bostrom 2014; Tegmark 2017; Omohundro 2014), one could argue that those topics could be discussed in ethics guidelines under the umbrella of potential prohibitions to pursue certain research strands in this area (Hagendorff 2019). The fact that artificial general intelligence is not discussed in the guidelines may be due to the fact that most of the guidelines are not written by research groups from philosophy or other speculative disciplines, but by researchers with a background directly in computer science or its application. In this context, it is noteworthy that the fear of the emergence of superintelligence is more frequently expressed by people who lack technical experience in the field of AI-one just has to think of people like Stephen Hawking, Elon Musk or Bill Gates-while "real" experts generally regard the idea of a strong AI as rather absurd (Calo 2017, 26). Perhaps the same holds true for the question of machine consciousness and the ethical problems associated with it (Lyons 2018), as this topic is also omitted from all examined ethical guidelines. What is also striking is the fact that only the Montréal Declaration for Responsible Development of Artificial Intelligence ( 2018) as well as the AI Now 2019 Report ( 2019) explicitly addresses the aspect of democratic control, governance and political deliberation of AI systems. The mentioned documents are also the only guidelines that explicitly prohibits imposing certain lifestyles or concepts of "good living" on people by AI systems, as it is for example demonstrated in the Chinese scoring system (Engelmann et al. 2019). The former document further criticizes the application of AI systems for the reduction of social cohesion, for example by isolating people in echo chambers (Flaxman et al. 2016). In addition, hardly any guideline discusses the possibility for political abuse of AI systems in the context of automated propaganda, bots, fake news, deepfakes, micro targeting, election fraud, and the like. What is also largely absent from most guidelines is the issue of a lack in diversity within the AI community. This lack of diversity is prevailing in the field of artificial intelligence research and development, as well as in the workplace cultures shaping the technology industry. In the end, a relatively small group of predominantly white men determines how AI systems are designed, for what purposes they are optimized, what is attempted to realize technically, etc. The famous AI startup "nnaisense" run by Jürgen Schmidhuber, which aims at generating an artificial general intelligence, to name just one example, employs only two women-one scientist and one office manager-in its team, but 21 men. Another matter, which is not covered at all or only very rarely mentioned in the guidelines, are aspects of robot ethics. As mentioned in the methods chapter, specific guidelines for robot ethics exist, most prominently represented by Asimov's three laws of robotics (Asimov 2004), but those guidelines were intentionally excluded from the analysis. Nonetheless, advances in AI research contribute, for instance, to increasingly anthropomorphized technical devices. The ethical question that arises in this context echoes Immanuel Kant's "brutalization argument" and states that the abuse of anthropomorphized agents-as, for example, is the case with language assistants (Brahnam 2006)-also promotes the likelihood of violent actions between people (Darling 2016). Apart from that, the examined ethics guidelines pay little attention to the rather popular trolley problems (Awad et al. 2018) and their alleged relation to ethical questions surrounding self-driving cars or other autonomous vehicles. In connection to this, no guideline deals in detail with the obvious question where systems of algorithmic decision making are superior or inferior, respectively, to human decision routines. And finally, virtually no guideline deals with the "hidden" social and ecological costs of AI systems. At several points in the guidelines, the importance of AI systems for approaching a sustainable society is emphasized (Rolnick et al. 2019). However, it is omitted-with the exception of the AI Now 2019 Report ( 2019)-that producer and consumer practices in the context of AI technologies may in themselves contradict sustainability goals. Issues such as lithium mining, e-waste, the one-way use of rare earth minerals, energy consumption, low-wage "clickworkers" creating labels for data sets or doing content moderation are of relevance here (Crawford and Joler 2018; Irani 2016; Veglis 2014; Fang 2019; Casilli 2017). Although "clickwork" is a necessary prerequisite for the application of methods of supervised machine learning, it is associated with numerous social problems (Silberman et al. 2018; Irani 2015; Graham et al. 2017), such as low wages, work conditions and psychological work consequences, which tend to be ignored by the AI community. Finally, yet importantly, not a single guideline raises the issue of public- private partnerships and industry- funded research in the field of AI. Despite the massive lack of transparency regarding the allocation of research funds, it is no secret that large parts of university AI research are financed by corporate partners. In light of this, it remains questionable to what extent the ideal of freedom of research can be upheld-or whether there will be a gradual "buyout" of research institutes.

3 AI in Practice

3.1 Business Versus Ethics

The close link between business and science is not only revealed by the fact that all of the major AI conferences are sponsored by industry partners. The link between business and science is also well illustrated by the AI Index 2018 (Shoham et al. 2018). Statistics show that, for example, the number of corporate-affiliated AI papers has grown significantly in recent years. Furthermore, there is a huge growth in the number of active AI startups, each supported by huge amounts of annual funding from Venture Capital firms. Tens of thousands of AI-related patents are registered each year. Different industries are incorporating AI applications in a broad variety of fields, ranging from manufacturing, supply-chain management, and service development, to marketing and risk assessment. All in all, the global AI market comprises more than 7 billion dollars (Wiggers 2019).

A critical look at this global AI market and the use of AI systems in the economy and other social systems sheds light primarily on unwanted side effects of the use of AI, as well as on directly malevolent contexts of use. These occur in various areas (Pistono and Yampolskiy 2016; Amodei et al. 2017). Leading, of course, is the military use of AI in cyber warfare or regarding weaponized unmanned vehicles or drones (Ernest and Carroll 2016; Anderson and Waxman 2013). According to media reports, the US government alone intends to invest two billion dollars in military AI projects over the next 5 years (Fryer-Biggs 2018). Moreover, governments can use AI applications for automated propaganda and disinformation campaigns (Lazer et al. 2018), social control (Engelmann et al. 2019), surveillance (Helbing 2019), face recognition or sentiment analysis (Introna and Wood 2004), social sorting (Lyon 2003), or improved interrogation techniques (McAllister 2017). Notwithstanding the above, companies can cause massive job losses due to AI implementation (Frey and Osborne 2013), conduct unmonitored forms of AI experiments on society without informed consent (Kramer et al. 2014), suffer from data breaches (Schneier 2018), use unfair, biased algorithms (Eubanks 2018), provide unsafe AI products (Sitawarin et al. 2018), use trade secrets to disguise harmful or flawed AI functionalities (Whittaker et al. 2018), rush to integrate and put immature AI applications on the market and many more. Furthermore, criminal or black-hat hackers can use AI to tailor cyberattacks, steal information, attack IT infrastructures, rig elections, spread misinformation for example through deepfakes, use voice synthesis technologies for fraud or social engineering (Bendel 2017), or disclose personal traits that are actually secret or private via machine learning applications (Kosinski and Wang 2018; Kosinski et al. 2013, 2015). All in all, only a very small number of papers is published about the misuse of AI systems, even though they impressively show what massive damage can be done with those systems (Brundage et al. 2018; King et al. 2019; O'Neil 2016).

3.2 AI Race

While the United States currently has the largest number of start-ups, China claims to be the "world leader in AI" in 2030 (Abacus 2018). This claim is supported by the sheer amount of data that China has at its disposal to train its own AI systems, as well as by the large label companies that take over the manual preparation of data sets for supervised machine learning (Yuan 2018). Conversely, China is seen to have a weakness vis-à-vis the USA in that the investments of the market leaders Baidu, Alibaba and Tencent are too application-oriented comprising areas such as autonomous driving, finance or home appliances, while important basic research on algorithm development, chip production or sensor technology is neglected (Hao 2019). The constant comparison between China, the USA and Europe renders the fear of being inferior to each other an essential motive for efforts in the research and development of artificial intelligence.

Another justification for competitive thinking is provided by the military context. If the own "team", framed in a nationalist way, does not keep pace, so the consideration, it will simply be overrun by the opposing "team" with superior AI military technology. In fact, potential risks emerge from the AI race narrative, as well as from an actual competitive race to develop AI systems for technological superiority (Cave and ÓhÉigeartaigh 2018). One risk of this rhetoric is that "impediments" in the form of ethical considerations will be eliminated completely from research, development and implementation. AI research is not framed as a cooperative global project, but as a fierce competition. This competition affects the actions of individuals and promotes a climate of recklessness, repression, and thinking in hierarchies, victory and defeat. The race for the best AI, whether a mere narrative or a harsh reality, reduces the likelihood of the establishment of technical precaution measures as well as of the development of benevolent AI systems, cooperation, and dialogue between research groups and companies. Thus, the AI race stands in stark contrast to the idea of developing an "AI4people" (Floridi et al. 2018). The same holds true for the idea of an "AI for Global Good", as was proposed at the 2017's ITU summit, or the large number of leading AI researchers who signed the open letter of the "Future of Life Institute", embracing the norm that AI should be used for prosocial purposes.

Despite the downsides, in less public discourses and in concrete practice, an AI race has long since established itself. Along with that development, in- and outgroup-thinking has intensified. Competitors are seen more or less as enemies or at least as threats against which one has to defend oneself. Ethics, on the other hand, in its considerations and theories always stresses the danger of an artificial differentiation between in- and outgroups (Derrida 1997). Constructed outgroups are subject to devaluation, are perceived de-individualized and in the worst case can become victims of violence simply because of their status as "others" (Mullen and Hu 1989; Vaes et al. 2014). I argue that only by abandoning such thinking in- and outgroups may the AI race be reframed into a global cooperation for beneficial and safe AI.

3.3 Ethics in Practice

Do ethical guidelines bring about a change in individual decision-making regardless of the larger social context? In a recent controlled study, researchers critically reviewed the idea that ethical guidelines serve as a basis for ethical decision-making for software engineers (McNamara et al. 2018). In brief, their main finding was that the effectiveness of guidelines or ethical codes is almost zero and that they do not change the behavior of professionals from the tech community. In the survey, 63 software engineering students and 105 professional software developers were scrutinized. They were presented with eleven software-related ethical decision scenarios, testing whether the influence of the ethics guideline of the Association for Computing Machinery (ACM) (Gotterbarn et al. 2018) in fact influences ethical decision-making in six vignettes, ranging from responsibility to report, user data collection, intellectual property, code quality, honesty to customer to time and personnel management. The results are disillusioning: "No statistically significant difference in the responses for any vignette were found across individuals who did and did not see the code of ethics, either for students or for professionals." (McNamara et al. 2018, 4).

Irrespective of such considerations on the microsociological level, the relative ineffectiveness of ethics can also be explained at the macrosociological level. Countless companies are eager to monetize AI in a huge variety of applications. This strive for a profitable use of machine learning systems is not primarily framed by value- or principle-based ethics, but obviously by an economic logic. Engineers and developers are neither systematically educated about ethical issues, nor are they empowered, for example by organizational structures, to raise ethical concerns. In business contexts, speed is everything in many cases and skipping ethical considerations is equivalent to the path of least resistance. Thus, the practice of development, implementation and use of AI applications has very often little to do with the values and principles postulated by ethics. The German sociologist Ulrich Beck once stated that ethics nowadays "plays the role of a bicycle brake on an intercontinental airplane" (Beck 1988, 194). This metaphor proves to be particularly true in the context of AI, where huge sums of money are invested in the development and commercial utilization of systems based on machine learning (Rosenberg 2017), while ethical considerations are mainly used for public relations purposes (Boddington 2017, 56).

The tension between ethical principles and wider societal interests on the one hand, and research, industry, and business objectives on the other can be explained with recourse to sociological theories. Especially on the basis of system theory it can be shown that modern societies differ in their social systems, each working with their own codes and communication media (Luhmann 1984, 1997, 1988). Structural couplings can lead decisions in one social system to influence other social systems. Such couplings, however, are limited and do not change the overall autonomy of social systems. This autonomy, which must be understood as an exclusive, functionalist orientation towards the system's own codes is also manifested in the AI industry, business and science. All these systems have their own codes, their own target values, and their own types of economic or symbolic capital via which they are structured and based upon which decisions are made (Bourdieu 1984). Ethical intervention in those systems is only possible to a very limited extent (Hagendorff 2016). A certain hesitance exists towards every kind of intervention as long as these lie beyond the functional laws of the respective systems. Despite that, unethical behavior or unethical intentions are not solely caused by economic incentives. Rather, individual character traits like cognitive moral development, idealism, or job satisfaction play a role, let alone organizational environment characteristics like an egoistic work climate or (non-existent) mechanisms for the enforcement of ethical codes (Kish-Gephart et al. 2010). Nevertheless, many of these factors are heavily influenced by the overall economic system logic. Ethics is then, so to speak, "operationally effectless" (Luhmann 2008).

And yet, such system-theoretical considerations apply only on a macro level of observation and must not be generalized. Deviations from purely economic behavioral logics in the tech industry occur as well, for example when Google withdrew from the military project "Maven" after protests from employees (Statt 2018) or when people at Microsoft protested against the company's cooperation with Immigration and Customs Enforcement (ICE) (Lecher 2018). Nevertheless, it must also be kept in mind here that, in addition to genuine ethical motives, the significance of economically relevant reputation losses should not be underestimated. Hence, the protest against unethical AI projects can in turn be interpreted in an economic logic, too.

3.4 Loyalty to Guidelines

For many of the issues mentioned in the guidelines, it is difficult to assess the extent to which efforts to meet the set objectives are successful or whether conflicting trends prevail. This is the case in the areas of safety and cybersecurity, the science-policy link, future of employment, public awareness about AI risks, or human oversight. In other areas, including the issue of hidden costs and sustainability, the protection of whistleblowers, diversity in the field of AI, the fostering of solidarity and social cohesion, the respect for human autonomy, the use of AI for the common good or the military AI arms race, it can certainly be stated that the ethical goals are being massively underachieved. One only has to think of the aspect of gender diversity: Even though ethical guidelines clearly demand its improvement, the state of affairs is that on average 80% of the professors at the world's leading universities such as Stanford, Oxford, Berkeley or the ETH are male (Shoham et al. 2018). Furthermore, men make up more than 70% of applicants for AI jobs in the U.S. (Shoham et al. 2018). Alternatively, one can take human autonomy: As repeatedly demanded in various ethical guidelines, people should not be treated as mere data subjects, but as individuals. In fact, however, countless examples show that computer decisions, regardless of their susceptibility to error, are attributed a strong authority which results in the ignorance of individual circumstances and fates (Eubanks 2018). Furthermore, countless companies strive for the opposite of human autonomy, employing more and more subtle techniques for manipulating user behavior via micro targeting, nudging, UX-design and so on (Fogg 2003; Matz et al. 2017). Another example is that of cohesion: Many of the major scandals of the last years would have been unthinkable without the use of AI. From echo chamber effects (Pariser 2011) to the use of propaganda bots (Howard and Kollanyi 2016), or the spread of fake-news (Vosoughi et al. 2018), AI always played a key role to the effect of diminishing social cohesion, fostering instead radicalization, the decline of reason in public discourse and social divides (Tufekci 2018; Brady et al. 2017).

4 Advances in AI Ethics

4.1 Technical Instructions

Given the relative lack of tangible impact of the normative objectives set out in the guidelines, the question arises as to how the guidelines could be improved to make them more effective. At first glance, the most obvious potential for improvement of the guidelines is probably to supplement them with more detailed technical explanations-if such explanations can be found. Ultimately, it is a major problem to deduce concrete technological implementations from the very abstract ethical values and principles. What does it mean to implement justice or transparency in AI-systems? What does a "human-centered" AI look like? How can human oversight be obtained? The list of questions could easily be continued.

A good example of such a microethical work which can be implemented easily and concretely in practice is the paper by Gebru et al. ( 2018). The researchers propose the introduction of standardized datasheets listing the properties of different training data sets, so that machine learning-practitioners can check to what extent certain data sets are best suitable for their purposes, what the original intention was when the data set was created, what data the data set is composed of, how the data was collected and pre-processed, etc. The paper by Gebru et al. makes it possible for practitioners to obtain a more informed decision on the selection of certain training data sets, so that supervised machine learning ultimately becomes fairer, and more transparent, and avoids cases of algorithmic discrimination (Buolamwini and Gebru 2018). Such work is, however, an exception.

In general, ethical guidelines postulate very broad, overarching principles which are then supposed to be implemented in a widely diversified set of scientific, technical and economic practices, and in sometimes geographically dispersed groups of researchers and developers with different priorities, tasks and fragmental responsibilities. Ethics thus operates at a maximum distance from the practices it actually seeks to govern. Of course, this does not remain unnoticed among technology developers. In consequence, the generality and superficiality of ethical guidelines in many cases not only prevents actors from bringing their own practice into line with them, but rather encourages the devolution of ethical responsibility to others.

4.2 Virtue Ethics

Regardless of the fact that normative guidelines should be accompanied by in-depth technical instructions-as far as they can reasonably be identified-, the question still arises how the precarious situation regarding the application and fulfillment of AI ethics guidelines can be improved. To address this question, one needs to take a step back and look at ethical theories in general. In ethics, several major strands of theories were created and shaped by various philosophical traditions. Those theories range from deontological to contractualistic, utilitarian, or virtue ethical approaches (Kant 1827; Rawls 1975; Bentham 1838; Hursthouse 2001). In the following, two of these approaches-deontology and virtue ethics-will be selected to illustrate different approaches in AI ethics. The deontological approach is based on strict rules, duties or imperatives. The virtue ethics approach, on the other hand, is based on character dispositions, moral intuitions or virtues-especially "technomoral virtues" (Vallor 2016). In the light of these two approaches, the traditional type of AI ethics can be assigned to the deontological concept (Mittelstadt 2019). Ethics guidelines postulate a fixed set of universal principles and maxims which technology developers should adhere to (Ananny 2016). The virtue ethics approach, on the other hand, focuses more on "deeper-lying" structures and situation-specific deliberations, on addressing personality traits and behavioral dispositions on the part of technology developers (Leonelli 2016). Virtue ethics does not define codes of conduct but focusses on the individual level. The technologists or software engineers and their social context are the primary addressees of such an ethics (Ananny 2016), not technology itself.

I argue that the prevalent approach of deontological AI ethics should be augmented with an approach oriented towards virtue ethics aiming at values and character dispositions. Ethics is then no longer understood as a deontologically inspired tick-box exercise, but as a project of advancing personalities, changing attitudes, strengthen responsibilities and gaining courage to refrain from certain actions, which are deemed unethical. When following the path of virtue ethics, ethics as a scientific discipline must refrain from wanting to limit, control, or steer (Luke 1995). Very often, ethics or ethical guidelines are perceived as something whose purpose is to stop or prohibit activity, to hamper valuable research and economic endeavors (Boddington 2017, 8). I want to resign this negative notion of ethics. It should not be the objective of ethics to stifle activity, but to do the exact opposite, i.e. broadening the scope of action, uncovering blind spots, promoting autonomy and freedom, and fostering self-responsibility.

In view of AI ethics, approaches that focus on virtues aim at cultivating a moral character, expressing technomoral virtues such as honesty, justice, courage, empathy, care, civility, or magnanimity, to name just a few (Vallor 2016). Those virtues are supposed to raise the likelihood of ethical decision-making practices in organizations that develop and deploy AI applications. Cultivating a moral character, in terms of virtue ethics, means to educate virtues in families, schools, communities, as well as companies. At best, every individual, every member of a society should encourage this cultivation, by generating the motivation to adopt and habituate practices that influence technology development and use in a positive manner. Especially the subject of responsibility diffusion can only be circumvented when virtue ethics is adopted on a broad and collective level in communities of tech professionals. Simply every person involved in data science, data engineering and data economies related to applications of AI has to take at least some responsibility for the implications of their actions (Leonelli 2016). This is why researchers such as Floridi argue that every actor who is causally relevant for bringing about the collective consequence or impacts in question, has to be held accountable (Floridi 2016). Interestingly, Floridi uses the backpropagation method known from Deep Learning to describe the way in which responsibilities can be assigned, except that here backpropagation is used in networks of distributed responsibility. When working in groups, actions that are on first glance allegedly morally neutral can nevertheless have consequences or impacts-intended or non-intended-that are morally wrong. This means that practitioners from AI communities always need to discern the overarching, short- and long-term consequences of the technical artefacts they are building or maintaining, as well as to explore alternative ways of developing software or using data, including the option of completely refraining from carrying out particular tasks, which are considered unethical.

In addition to the endorsement of virtue ethics in tech communities, several institutional changes should take place. They include the adoption of legal framework conditions, the establishment of mechanisms for an independent auditing of technologies, the establishment of institutions for complaints, which also compensate for harms caused by AI systems, and the expansion of university curricula in particular through content from ethics of technology, media, and information (Floridi et al. 2018; Cowls and Floridi 2018; Eaton et al. 2017; Goldsmith and Burton 2017). So far, however, hardly any of these demands have been met.

5 Conclusion

Currently, AI ethics is failing in many cases. Ethics lacks a reinforcement mechanism. Deviations from the various codes of ethics have no consequences. And in cases where ethics is integrated into institutions, it mainly serves as a marketing strategy. Furthermore, empirical experiments show that reading ethics guidelines has no significant influence on the decision-making of software developers. In practice, AI ethics is often considered as extraneous, as surplus or some kind of "add-on" to technical concerns, as unbinding framework that is imposed from institutions "outside" of the technical community. Distributed responsibility in conjunction with a lack of knowledge about long-term or broader societal technological consequences causes software developers to lack a feeling of accountability or a view of the moral significance of their work. Especially economic incentives are easily overriding commitment to ethical principles and values. This implies that the purposes for which AI systems are developed and applied are not in accordance with societal values or fundamental rights such as beneficence, non-maleficence, justice, and explicability (Taddeo and Floridi 2018; Pekka et al. 2018).

Nevertheless, in several areas ethically motivated efforts are undertaken to improve AI systems. This is particularly the case in fields where technical "fixes" can be found for specific problems, such as accountability, privacy protection, anti-discrimination, safety, or explainability. However, there is also a wide range of ethical aspects that are significantly related to the research, development and application of AI systems, but are not or very seldomly mentioned in the guidelines. Those omissions range from aspects like the danger of a malevolent artificial general intelligence, machine consciousness, the reduction of social cohesion by AI ranking and filtering systems on social networking sites, the political abuse of AI systems, a lack of diversity in the AI community, links to robot ethics, the dealing with trolley problems, the weighting between algorithmic or human decision routines, "hidden" social and ecological costs of AI, to the problem of public-private-partnerships and industry-funded research. Again, as mentioned earlier, the list of omissions is not exhaustive and not all omissions can be justified equally. Some omissions, like deliberations on artificial general intelligence, can be justified by pointing at their purely speculative nature, while other omissions are less valid and should be a reason to update or improve existing and upcoming guidelines.

Checkbox guidelines must not be the only "instruments" of AI ethics. A transition is required from a more deontologically oriented, action-restricting ethic based on universal abidance of principles and rules, to a situation-sensitive ethical approach based on virtues and personality dispositions, knowledge expansions, responsible autonomy and freedom of action. Such an AI ethics does not seek to subsume as many cases as possible under individual principles in an overgeneralizing way, but behaves sensitively towards individual situations and specific technical assemblages. Further, AI ethics should not try to discipline moral actors to adhere to normative principles, but emancipate them from potential inabilities to act self-responsibly on the basis of comprehensive knowledge, as well as empathy in situations where morally relevant decisions have to be made.

These considerations have two consequences for AI ethics. On the one hand, a stronger focus on technological details of the various methods and technologies in the field of AI and machine learning is required. This should ultimately serve to close the gap between ethics and technical discourses. It is necessary to build tangible bridges between abstract values and technical implementations, as long as these bridges can be reasonably constructed. On the other hand, however, the consequence of the presented considerations is that AI ethics, conversely, turns away from the description of purely technological phenomena in order to focus more strongly on genuinely social and personality-related aspects. AI ethics then deals less with AI as such, than with ways of deviation or distancing oneself from problematic routines of action, with uncovering blind spots in knowledge, and of gaining individual self-responsibility. Future AI ethics faces the challenge of achieving this balancing act between the two approaches.

Change history

28 July 2020

In the original publication of this article, the Table 1 has been published in a low resolution. Now a larger version of Table 1 is published in this correction. The publisher apologizes for the error made during production.

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This research was supported by the Cluster of Excellence "Machine Learning - New Perspectives for Science" funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - Reference Number EXC 2064/1 - Project ID 390727645.

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# data[].attributes.attachment_link.title
The Ethics of AI Ethics: An Evaluation of Guidelines - Minds and Machines

# data[].attributes.attachment_link.description
Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the "disruptive" potentials of new AI technologies.

# data[].attributes.body
This APA article explores the ethical and equity challenges in artificial intelligence, highlighting how both human and algorithmic biases shape AI systems. It also discusses how AI can be used as a tool to reduce social inequities when designed responsibly. The piece adds a valuable psychological perspective to the broader conversation on AI ethics, which highlights the need for inclusive and ethical development practices.

# data[].attributes.attachment_link.readable_text
Algorithms and humans both contribute to bias in AI, but AI may also hold the power to correct or reverse inequities among humans

Cite This Article

Abrams, Z. (2024, April 1). Addressing equity and ethics in artificial intelligence. Monitor on Psychology, 55(3). https://www.apa.org/monitor/2024/04/addressing-equity-ethics-artificial-intelligence

As artificial intelligence (AI) rapidly permeates our world, researchers and policymakers are scrambling to stay one step ahead. What are the potential harms of these new tools-and how can they be avoided?

"With any new technology, we always need to be thinking about what's coming next. But AI is moving so fast that it's difficult to grasp how significantly it's going to change things," said David Luxton, PhD, a clinical psychologist and an affiliate professor at the University of Washington's School of Medicine who spoke at the 2024 Consumer Electronics Show (CES) on Harnessing the Power of AI Ethically.

Luxton and his colleagues dubbed recent AI advances "super-disruptive technology" because of their potential to profoundly alter society in unexpected ways. In addition to concerns about job displacement and manipulation, AI tools can cause unintended harm to individuals, relationships, and groups. Biased algorithms can promote discrimination or other forms of inaccurate decision-making that can cause systematic and potentially harmful errors; unequal access to AI can exacerbate inequality ( Proceedings of the Stanford Existential Risk Conference 2023, 60-74). On the flip side, AI may also hold the potential to reduce unfairness in today's world-if people can agree on what "fairness" means.

"There's a lot of pushback against AI because it can promote bias, but humans have been promoting biases for a really long time," said psychologist Rhoda Au, PhD, a professor of anatomy and neurobiology at the Boston University Chobanian & Avedisian School of Medicine who also spoke at CES on harnessing AI ethically. "We can't just be dismissive and say: 'AI is good' or 'AI is bad.' We need to embrace its complexity and understand that it's going to be both."

With that complexity in mind, world leaders are exploring how to maximize the benefits of AI and minimize its harms. In 2023, the Biden administration released an executive order on Safe, Secure, and Trustworthy AI and the European Union came close to passing its first comprehensive AI Act. Psychologists, with their expertise on cognitive biases and cultural inclusion, as well as in measuring the reliability and representativeness of datasets-have a growing role in those discussions.

"The conversation about AI bias is broadening," said psychologist Tara Behrend, PhD, a professor at Michigan State University's School of Human Resources and Labor Relations who studies human-technology interaction and spoke at CES about AI and privacy. "Agencies and various academic stakeholders are really taking the role of psychology seriously."

Bias in algorithms

Government officials and researchers are not the only ones worried that AI could perpetuate or worsen inequality. Research by Mindy Shoss, PhD, a professor of psychology at the University of Central Florida, shows that people in unequal societies are more likely to say AI adoption carries the threat of job loss (Technology, Mind, and Behavior, Vol. 3, No. 2, 2022).

Those worries about job loss appear to be connected to overall mental well-being. For example, about half of employees who said they were worried that AI might make some or all of their job duties obsolete also said their work negatively impacted their mental health. Among those who did not report such worries about AI, only 29% said their work worsened their mental health, according to APA's 2023 Work in America survey.

"In places where there's a lot of inequality, those systems essentially create winners and losers," Shoss said, so there is additional concern about how AI tools could be used irresponsibly-even maliciously-to make things worse.

Those fears are not unfounded. Biased algorithmic decision-making has been reported in health care, hiring, and other settings. It can happen when the data used to train a system is inaccurate or does not represent the population it intends to serve. With generative AI systems, such as ChatGPT, biased decision-making can also happen unexpectedly due to the "black box" issue, which refers to the fact that even an algorithm's developers may not understand how it derives its answers.

"Even if we give a system the best available data, the AI may start doing things that are unpredictable," Luxton said.

Examples include a recruiting tool at Amazon that preferred male candidates for technical jobs, and Replika, an AI companion that harassed some of its users. Avoiding such issues requires careful auditing of AI tools-including testing them in extreme scenarios before they are released-but it also requires significantly more transparency about how a given algorithm learns from data, Luxton said.

On top of technical audits, Behrend and Richard Landers, PhD, a professor of industrial-organizational psychology at the University of Minnesota Twin Cities, have published guidelines for conducting a "psychological audit" of an AI model, or evaluating how it might impact humans (American Psychologist, Vol. 78, No. 1, 2023). That includes direct effects, such as who is recommended by a hiring algorithm, as well as broader ripple effects on organizations and communities.

The audit employs basic principles of psychological research to evaluate fairness and bias in AI systems. For example: Where is the data used to train an AI model coming from, and does it generalize to the population the tool intends to serve? Were the data collected using sound research methods, or were limitations introduced? Are developers making appropriate inferences from that data?

Conversations about algorithmic bias often center around high-stakes decision-making, such as educational and hiring selection, but Behrend said other applications of this technology are just as important to audit. For example, an AI-driven career guidance system could unintentionally steer a woman away from jobs in STEM (science, technology, engineering, and math), influencing her entire life trajectory.

"That can be potentially hugely consequential for a person's future decisions and pathways," she said. "It's equally important to think about whether those tools are designed well."

Even if an algorithm is well designed, it can be applied in an unfair way, Shoss said. For example, a system that determines salaries and bonuses could be implemented without transparency or human input-or it could be used as one of a series of factors to guide human decision-making. In that sense, using AI ethically requires asking the same questions that evaluate any other organizational change: Is it done with trust, transparency, and accountability?

Human error

An algorithm itself may be biased, but humans can also introduce inaccuracies based on how they use AI tools.

"AI has many biases, but we're often told not to worry, because there will always be a human in control," said Helena Matute, PhD, a professor of experimental psychology at Universidad de Deusto in Bilbao, Spain. "But how do we know that AI is not influencing what a human believes and what a human can do?"

In a study she conducted with graduate student Lucía Vicente, participants classified images for a simulated medical diagnosis either with or without the help of AI. When the AI system made errors, humans inherited the same biased decision-making, even when they stopped using the AI (Scientific Reports, Vol. 13, 2023).

"If you think of a doctor working with this type of assistance, will they be able to oppose the AI's incorrect advice?" Matute said, adding that human users need the training to detect errors, the motivation to oppose them, and the job security to speak up about it.

Decades of psychological research clearly show that once humans inherit a bias or encounter misinformation, those beliefs are hard to revise. Celeste Kidd, PhD, an assistant professor of psychology at the University of California, Berkeley, argues that assumptions about AI's capabilities, as well as the way many tools present information in a conversational, matter-of-fact way, make the risk of inheriting stubborn biases particularly high (Science, Vol. 380, 2023).

"By the point [that] these systems have transmitted the information to the person...it may not be easy to correct," Kidd said in a press release from the university (Berkeley News, June 22, 2023).

Companies also can-and do-intentionally leverage AI to exploit human biases for gain, said Matute. In a study of simulated AI dating recommendations, she and graduate student Ujué Agudo found that participants were more likely to agree to date someone whose profile they viewed more than once, a choice she said is driven by the familiarity heuristic (PLOS ONE, Vol. 16, No. 4, 2021). Guidelines for ethical AI should consider how it can be designed to intentionally play on cognitive biases and whether that constitutes safe use, she added.

"We all have cognitive biases, and AI can be used to exploit them in a very dangerous way," Matute said.

Working toward "fairness"

While poorly designed algorithms can perpetuate real-world biases, AI may also hold the power to correct or reverse inequities among humans. For example, an algorithm could detect whether a company is less likely to hire or promote women, then nudge leaders to adjust job ads and decision-making criteria accordingly.

"There are risks here, too, and it's equally important to have transparency about these types of systems-how they're deriving answers and making decisions-so they don't create distrust," Luxton said.

Using AI to reverse bias also requires agreeing on what needs to change in society. The current approach to building AI tools involves collecting large quantities of data, looking for patterns, then applying them to the future. That strategy preserves the status quo, Behrend said-but it is not the only option.

"If you want to do something other than that, you have to know or agree what is best for people, which I don't know that we do," she said.

As a starting point, Behrend is working to help AI researchers, developers, and policymakers agree on how to conceptualize and discuss fairness. She and Landers distinguish between various uses of the term, including statistical bias versus equity-based differences in group outcomes, in their recent paper.

"These are noncomparable ways of using the word 'fairness,' and that was really shutting down a lot of conversations," Behrend said.

Establishing a common language for discussing AI is an important step for regulating it effectively, which a growing contingent is seeking to do. In addition to Biden's 2023 executive order, New York State passed a law requiring companies to tell employees if AI is used in hiring or promotion. At least 24 other states have either proposed or passed legislation aiming to curtail the use of AI, protect the privacy of users, or require various disclosures ( U.S. State-by-State AI Legislation Snapshot, BCLP Law, 2023).

"It's pretty difficult to stay on top of what the best practice is at any given moment," Behrend said. "That's another reason why it's important to emphasize the role of psychology, because basic psychological principles-reliability, validity, fairness-don't change."

Luxton argues that executive orders and piecemeal legislation can be politicized or inefficient, so policymakers should instead focus on establishing standard guidelines and best practices for AI. That includes requiring developers to show an audit trail, or a record of how an algorithm makes decisions. (Luxton is also writing a guidebook for behavioral health practitioners on integrating AI into practice.) When challenges arise, he suggests letting those play out through the judicial system.

"Government does need to play a role in AI regulation, but we also want to reduce the inefficiencies of government roadblocks in technological development," Luxton said.

One thing is clear: AI is a moving target. Using it ethically will require continued dialogue as the technology grows ever more sophisticated.

"It's not entirely clear what the shelf life of any of these conversations about bias will be," said Shoss. "These discussions need to be ongoing, because the nature of generative AI is that it's constantly changing."

Further reading

How psychology is shaping the future of technology
Straight, S., & Abrams, Z. APA, 2024

Speaking of Psychology: How to use AI ethically, with Nathanael Fast, PhD
APA, 2024

Worried about AI in the workplace? You're not alone
Lerner, M. APA, 2024

The unstoppable momentum of generative AI
Abrams, Z., APA 2024

Six Things Psychologists are Talking About

The APA Monitor on Psychology ® sister e-newsletter offers fresh articles on psychology trends, new research, and more.

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Addressing equity and ethics in artificial intelligence

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Algorithms and humans both contribute to bias in AI, but AI may also hold the power to correct or reverse inequities among humans.

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In this article, Professor Johannes Himmelreich (an expert in AI ethics and public administration at Syracuse University), explores how artificial intelligence intersects with public policy, emphasizing the risks of biased datasets, the ethical challenges of using AI in government services, and the importance of maintaining human responsibility in automated decision-making. His work brings a practical, policy-driven perspective to the conversation about ethical AI design.

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🤖 What are the ethical concerns of AI? | Prof. Johannes Himmelreich

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Dr. Timnit Gebru is a computer scientist and prominent AI ethics researcher. She co-authored the groundbreaking Gender Shades study, which revealed significant racial and gender bias in commercial facial recognition systems. She is the founder of the Distributed AI Research Institute (DAIR), an organization focused on community-rooted, socially responsible AI development.

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Addis Coder: Algorithms and Programming for High Schoolers

An intensive 1 month summer class for high school students in Ethiopia, organized by Jelani Nelson. This is the most diverse/inclusive classroom I have ever been in. All regions of Ethiopia were represented with many religions and at least 10 languages (there were 85 students). There were different income levels ranging from students working as shoe shiners to put themselves through school to kids who went to private middle schools. All students currently go to public schools. The class had students from rural areas and cities, close to 50/50 female/male ratio and people with disabilities (e.g. Misgina who is deaf but is top of his class while going to a school that gives no resources for deaf people). Some kids had never touched a computer before while others have programmed in Java. But all of them currently understand the basics of recursion, dynamic programming, graphs etc. And they only took this class for one month. I hope to one day see a computer science classroom in the US that is this diverse.

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An intensive 1 month summer class for high school students in Ethiopia, organized by Jelani Nelson. This is the most diverse/inclusive classroom I have ever been in. All regions of Ethiopia were represented with many religions and at least 10 languages (there were 85 students).

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This episode from Oxford’s Ethics in AI series explores how artificial intelligence might compromise or redefine human autonomy; the speakers examine issues like algorithmic manipulation, over-reliance on AI systems, and how digital decision-making could alter our sense of agency. The discussion raises critical ethical questions about whether humans can retain meaningful control in an AI-driven world; it offers a philosophical yet practical perspective that adds depth to the broader topic of AI ethics.

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How can AI systems influence our decision-making in ways that undermine autonomy? Do they do so in new or more problematic ways? To what extent can we outsource tasks to AI systems without losing our autonomy? Do we need a new conception of autonomy that incorporates considerations of the digital self?

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This meme humorously highlights a real concern in AI ethics. While lighthearted, it reflects serious issues surrounding automation, job displacement, and the unintended consequences of innovation. It adds a satirical perspective to this project and prompts reflection on how ethical considerations must include the long-term impact on workers.

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This Forbes article explores how the shift from curated data to AI-generated content creates new ethical challenges. It emphasizes the need for transparency, fairness, and human oversight as AI becomes more creative and independent. It post adds an industry-based perspective to the project, showing how ethical values can shape innovation in real time.

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As CTO of Solutions at

getty

Rackspace Technology, Jeff is fanatical about helping people and companies see more success with technology.
Artificial intelligence (AI) has emerged as a powerful tool, transforming critical aspects of our daily lives, from data curation to active content creation. As AI systems such as ChatGPT, Google Bard and others evolve and gain ever more powerful content-generation capabilities, there is a pressing need to grapple with the ethical implications that arise along this transformative journey. Warning of its potential dangers, AI pioneer Dr. Geoffrey Hinton recently announced his resignation from Google, citing concerns over the potential ramifications of advancing AI technologies.

From biases and authenticity concerns to privacy and accountability, understanding and addressing the ethical dimensions of AI in the transition from curation to creation is vital to ensure that the use of AI technologies aligns with human values and contributes to a responsible and equitable future. To tackle these thorny issues effectively, organizations need to both understand the challenges and risks around AI and take these fully into account when designing and deploying applications.

AI's Transformative Influence

In recent years, AI has revolutionized the concept of curation for consumers, offering enhanced and personalized experiences across various aspects of their lives. From personalized recommendations on streaming platforms and social media feeds such as YouTube, Facebook and TikTok to voice assistants like Siri and Alexa, AI has seamlessly embedded itself into our digital experiences. With the abundance of information available online, AI-powered curation systems have played a crucial role in sifting through vast amounts of content and delivering tailored and relevant recommendations to consumers.

By leveraging the power of natural language processing and machine learning, ChatGPT and similar models are revolutionizing the consumer content landscape, enabling dynamic, interactive experiences that were previously limited to human curation. The shift from curation of consumer content to creation by models like ChatGPT has brought about a transformative change in the way content is generated and consumed. This move toward AI-driven content creation offers numerous advantages, including the ability to generate personalized responses, handle high volumes of queries and adapt to individual user preferences.

One of the key challenges in the path from curation to creation, however, lies in addressing biases and ensuring fairness in AI-generated content. AI systems learn from vast amounts of data, and if that data contains biases, those biases can be perpetuated in the content they generate and create "real time" deepfakes. For example, the rise of AI-powered deepfakes has raised concerns about their potential to manipulate and influence elections by generating convincing but fabricated content that can mislead voters and undermine the democratic process.

The fear of deepfake technology extends beyond its impact on elections, however. It also raises serious concerns about the potential use of manipulated media in warfare, where false information and fabricated evidence could exacerbate tensions, escalate conflicts and have far-reaching consequences.

Another potential risk of a well-timed deepfake extends to its ability to disrupt the financial markets with alarming consequences. This involves the deliberate and precise dissemination of deepfake content, such as a video showcasing a company CEO, which can induce drastic fluctuations in a company's stock price, either causing it to plummet or soar.

AI hallucination poses yet another concern as generative technologies like ChatGPT can generate misleading or false information, compromising the reliability and trustworthiness of AI systems and highlighting the need for thorough validation and verification mechanisms. Google CEO Sundar Pichai recently appeared on and spoke about AI's imperfections.

Harnessing Responsible AI: Shaping A Brighter Future

In the pursuit of a responsible future, it is crucial that companies prioritize the implementation of ethical AI practices now, as the potentially negative implications of the misuse of AI are becoming increasingly urgent. Failing to act may lead to reputational risks and missed opportunities to build trust with customers and key stakeholders. It is also important that companies develop their own AI guiding principles so stakeholders know where they stand. This should include how AI is going to be used within the organization, what it will not be used for, and what processes and parameters the company will use in evaluating its deployment.

It is also paramount that organizations resist the temptation to simply assign an "AI czar" or rely on a single individual for AI decision-making. Employing a cross-functional team-including marketing and sales, compliance and legal, as well as IT and data experts-will foster collaboration, ensure comprehensive oversight and make sure the deployment of AI technologies is aligned with the organization's business objectives, ethical principles and stakeholder expectations.

At Rackspace, we have invested a significant amount of effort in upholding responsible AI standards. This includes adding language to our company handbook emphasizing best practices, outlining guidelines and procedures to ensure the ethical development and deployment of AI, and making AI part of our annual compliance training meetings. We have also committed to not using AI for code generation or using ChatGPT to review financial contracts or documents.

Conclusion

Government regulation is the first step to creating a robust framework that establishes best practices for ethical AI. But it is still incumbent upon companies to take the initiative, recognizing the importance of transparency and accountability in their use of AI technologies. By proactively integrating responsible AI principles into their operations, companies can not only mitigate potential risks but also build trust with customers and pave the way for a future where AI technologies are harnessed for the benefit of society. It is only through the collective commitment of regulators and businesses that we will be able to shape an AI future that is both technologically advanced and ethically sound.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

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How Ethical AI Can Shape A Responsible Future

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In the pursuit of a responsible future, it is crucial that companies prioritize the implementation of ethical AI practices now.

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This article explores how ethical challenges like bias, transparency, and accountability unfold across sectors such as healthcare, education, and law enforcement; the authors take a multidisciplinary approach, combining insights from philosophy, sociology, and computer science. The paper emphasizes the growing need for practical ethical frameworks that can adapt to rapid advancements in AI; it offers a broad and accessible foundation for understanding how ethics must evolve alongside technology.

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The future of ethics in AI: challenges and opportunities

1 Introduction

Artificial Intelligence (AI) systems that are able to learn and reason like humans are now widely used in a large number of real-world applications. Their performance has dramatically increased the degree to which we are able to address previously inaccessible tasks. However, the debate around such systems is now pivoting towards their non-functional properties and their impact on society as a whole.

In this Special Issue, we collected several works that would trigger the discussion about a more sustainable AI developmental framework that encompasses a set of principles at the crossroads of AI, Ethics, Philosophy, and Sociology.

As the range of AI capabilities expands, so does our awareness of the ethical issues related to the design, development, deployment, and use of AI systems or their application for the social good. The promise for positive change that AI represents has been challenged by several reports on ethically questionable uses of AI in contexts as varied as healthcare, education, law enforcement, recruitment, risk assessment, and more.

2 Key themes in AI for people: an analysis of ethics in AI

Artificial intelligence (AI) has the potential to revolutionize the way we live and work, from improving healthcare to advancing scientific research. However, as with any powerful technology, there are concerns about its impact on society and ethics. To address these concerns, the Special Issue focuses on the ethical and societal implications of AI. The published papers cover a wide range of topics. This summary will provide an overview of the topics covered in the journal, highlighting some of the key insights and debates in the field.

2.1 Ethics in AI

This Special Issue addresses various ethical concerns related to the use of AI, such as psychological targeting, empathetic AI, cultural theories, fairness and discrimination, and accountability. Several papers face ethical concerns related to the development and deployment of artificial intelligence. For instance, the consideration of the ethical implications of AI for the entire world and beyond. Here, AI needs to be developed in a way that is transparent, controllable and aligned with human values.

Similarly, there is a discussion on how AI can be designed to act ethically in everyday situations, particularly those involving empathetic interactions. The papers argued that AI systems should be designed to understand human emotions and respond appropriately. The works highlight the need for transparency and accountability in the design of AI systems in the workplace.

2.2 AI and society

The Special Issue also looks at the societal implications of AI, such as the perception of AI, ageism, and the use of AI for societal interest. It features papers that consider the impact of AI on society examining the role of AI in the geopolitical landscape and argues that global cooperation is necessary to ensure that AI benefits humanity rather than causing harm.

Furthermore, it considers how AI is changing the nature of work and argues that workers should be empowered to adapt to new technologies. The papers highlight the need for AI to be designed with the goal of enhancing human capabilities rather than replacing human workers. There is a discussion on the importance of public interest theory in shaping the development and deployment of AI. The papers argue that AI should be developed in the public interest, with a focus on promoting social welfare and minimizing harm.

2.3 Explainable AI

Several papers focus on explainable AI, which is the idea that AI systems should be transparent and understandable to humans. Here, the reader can find works about the explainability of AI systems is essential for building trust between humans and machines and the importance of timing and context in providing explanations for AI decisions. Moreover, studies propose an "explanation space" that would allow humans to interact with AI systems and provide feedback on their decisions.

2.4 AI and healthcare

Several papers consider the role of AI in healthcare and address the ethical concerns related to bias and discrimination in healthcare AI. The papers propose a new principle of fairness that takes into account the social context in which the algorithm is used and where AI can be designed to provide individuals with the information they need to make informed decisions about their health.

3 Discussion and Future Work

As AI continues to evolve and become increasingly integrated into our daily lives, it is crucial that we continue to examine its ethical and societal implications. The papers published in "AI for People" represent important contributions to this ongoing conversation. However, there is much work to be done to address the complex and multifaceted issues that arise from the development and deployment of AI. Future research will need to grapple with questions of fairness, transparency, accountability, and the potential unintended consequences of AI.

Here are some potential topics for future discussions:

AI for People and the World: Discusses the expansion of AI to encompass not only people, but the world and the universe as well.

AI in the Workplace: Examines the impact of AI on the workplace and its potential for human adaptation, as well as the application of ethics to workplace AI.

Trustworthy AI: Explores the importance of trustworthy programming for autonomous concurrent systems, as well as the role of explainability in supporting trust.

AI for Social Good: Considers the use of AI for social good, aligning academic journal ratings with the United Nations Sustainable Development Goals (SDGs), and investing in AI for social good.

AI and the Law: Examines the legal implications of AI, including the legal status of intelligent service robots and the use of AI in criminal justice.

AI and Culture: Explores the intersection of AI and culture, such as cultural robotics, the cultural history of consciousness, and posthuman perception of AI in science fiction.

AI and Bias: Investigates biases in AI, such as implicit biases and stereotypes, age discrimination and exclusion, and bias and discrimination in job advertisements.

AI and Technology: Addresses various technological issues related to AI, such as developing a practical ethical methodology for integrating AI into the industry, algorithmic fairness, and the importance of transparency in AI operations.

As we continue to explore the possibilities of this technology, it is essential that we do so with a clear understanding of its limitations and ethical implications. By building on the work done so far, we can work towards a future in which AI is developed and deployed in a way that benefits all members of society.

The discussions on AI and ethics presented in this special issue demonstrate the complex interplay of different disciplines in creating a more sustainable AI development framework. Such a framework should be able to take into account moral and ethical considerations as well as other nonfunctional properties and social implications of AI technology. We have seen that addressing the ethical implications of AI goes beyond the traditional technical fields of AI and robotics and takes into consideration a much broader context of disciplines, such as ethics and philosophy, sociology and economics.

From the perspective of AI engineers and developers, it is important to embrace and practice a responsible approach to the development of AI technologies. This implies, for example, that developers should consider not only the technical aspects of their systems, but also the ethical implications of their use. In addition, developers should think about the various delegated responsibilities, such as data collection, privacy, and intellectual property, when developing or using AI technologies.

Discussion of the ethical implications of AI should not be limited to individual technology development. It should also consider the broader context of government policies and civil society initiatives. As such, it implies the need for a common framework of principles and standards that can ensure accountability, fairness and transparency in the use of AI. In addition, governments should create policies and guidelines to ensure that AI is used responsibly while enabling innovation and progress.

In conclusion, ethical considerations are a critical element in the development and use of AI technologies and must be taken into account by all stakeholders. The multidisciplinary approach of this special issue makes an important contribution to the debate by bringing different perspectives and disciplines into the conversation. We hope this will raise awareness and provide a framework for more ethical and responsible use of AI in the future.

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Authors and Affiliations

1. Department of Computer Science and Engineering, University of Bologna, Bologna, Italy

Angelo Trotta

2. Oxford Internet Institute, University of Oxford, Oxford, UK

Marta Ziosi

3. Department of Computer Science, University of Pisa, Pisa, Italy

Vincenzo Lomonaco

Corresponding author

Correspondence to Angelo Trotta.

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Trotta, A., Ziosi, M. & Lomonaco, V. The future of ethics in AI: challenges and opportunities. AI & Soc 38, 439-441 (2023). https://doi.org/10.1007/s00146-023-01644-x
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The future of ethics in AI: challenges and opportunities - AI & SOCIETY

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The future of ethics in AI: challenges and opportunities Artificial Intelligence (AI) systems that are able to learn and reason like humans are now widely used in a large number of real-world applications. Their performance has dramatically increased the degree to which we are able to address previously inaccessible tasks.