• Example: Can algorithms be racist?

• Data: Official Identity

• Values: Commerce

• Rights: Equality Rights

In the example the insinuation is that an algorithm has reinforced racial stereotypes by selecting certain images that are associated with 'unprofessional hair' and 'black men' respectively.

How might this intersect with the data point 'official identity'?

Official identity is defined as a government metric. These types of metrics are necessarily lacking in detail and nuance, and could lead to people being misrepresented. For example, a trans person may be listed using their birth gender, which is not representative of their current identity. Their trans identity may intersect with their racial identity, and put them at greater risk of bias and discrimination.

Building on the above, how could this impact values related to commerce?

Algorithms reinforcing stereotypes of black people as unprofessional may impact their opportunities in the workplace, making them less likely to be approached for promotions or new roles. They may have to work harder to prove themselves when in competition with white colleagues.

A biased algorithm that surfaces and reinforces racial stereotypes is explicitly, though perhaps inadvertently, reinforcing racist and discriminatory viewpoints. The Equality Act (2010) protects people from being treated unfairly based on their race (as well as other protected characteristics), but does not protect them from being represented in discriminatory ways unless there is a claim that they were 'treated unfairly' as a result. This is a good example of a situation in which their is legislation in place to protect people, and designers should work in the spirit of the value embodied in the legislation to try and minimise potential harm caused by their products.

The data preferences can easily tie themselves to the lies and fake news. If someone reads an article, pauses on it long enough, ‘likes’ it, or comments on it, the algorithm will continue to send similar articles fueling the warped and misguided beliefs.

The value of power used to mean something. But those ‘in power’ hold the power and silence others. We see what happens in Russia when someone speaks out and that is what Trump will try to turn America into – hopefully we continue to have enough sane people to stop him. The power of ‘ability to succeed’ ties into other biases around race and gender and this issue is worse with AI sadly.

Patient Rights: We have seen cases where America’s health systems have been hacked, which has threatened or causes individual health data to be leaked. While not 100% sure, most of these leaks pull the heads of our insurance companies (i.e., UnitedHealthcare) to capitol hill so it isn’t part of the fake news BUT I don’t listen to FOX! Our HIPPA laws here have been successful in helping people keep their data private and only shared with those we approve.

This goes hand and hand with the issue of privacy as if so much data was not being collected and creating algorithms that feed people things they may have previously viewed it creates an information bubble. This is a serious problem as it creates a serious rift within society and causes people to end up at the further extremes of their political party. As everything from their social media feed to the ads that they see reinforce fake news its people are becoming extremely susceptible to foreign actors that can undermine elections and even entire democratic systems of governance, such as the recent example of Tucker Carlson travelling to Russia and praising life under the Putin Regime even though there is serious evidence of election interference and this gave legitimacy to certain far-right ideologies in the United States and makes it even easier for election interference through Russian spread propaganda. Given he is seen by many as a journalist instead of a political commentator this also helps reinforce fake news stories which he himself at times have help spread which creates a feedback loop of fake and even possibly dangerous information, such as the infamous "Pizzagate" story that led a man to walk into a pizza shop with a gun believing he was preventing a Democrat led pedophile ring..

Rights Card - Human Rights

Value Card - Security

Analyzing Facebook posts to determine insurance premiums violates privacy rights and traditional values of fair treatment, while potentially discriminating based on personal expression rather than actual driving behavior. I believe this fringes upon Freedom of Expression.

This data then can also be used for political purposes of creating social divide for political/financial gains, by bad actors.

A better solution would be to offer optional devices that monitor only relevant driving data. Customers should also be given control over what information they can share (necessary informations, optional informations - which can be opted out of) while ensuring pricing decisions are based on legitimate risk factors.

Insurance companies have explored different means by which to analyze user data in order to set insurance rates for said users; from driving records, credit history, age or home address. While some data points are standard, using personal information like social media can be intrusive and raise ethical concerns. Social media is a personal outlet for many and using it to assess someone’s insurance rate would have negative impacts on a person’s ability of self-expression of data, such as their political stance or beliefs. In fear of a negative outlook from an insurance company, a person may limit their power to share their diversity of perspectives or freedom to act. For example, a consumer may limit sharing a video of them dancing at a party late at night on their social media, worried that it may lead to an insurance company making assumptions or associations of driver responsibility. This practice also violates data protection rights, as personal data should only be used in ways that are relevant and necessary. A person's social media or political beliefs are not directly related to their driving behavior, making such data unnecessary for insurance decisions.

Proposed Solution:

One potential solution is to offer an opt-in or opt-out system, where consumers can voluntarily share relevant driving data to receive discounts or better rates. However, transparency is key. Insurance companies should clearly outline what data is being collected, how it will be used, and any consequences of sharing it. Data collection should also be minimized to only include relevant factors, excluding personal aspects like social media activity. Telematics or other monitoring devices that are currently offered by some insurance companies may land you better rates, but may also punish you. Third party audits and ethical guidelines are important to ensure that companies are not overstepping in data collection or discriminatory practices.

Example: AI learning to be racist

Data: personal

Value: power to succeed

Rights: Data protection

Given abundance of information an individual has online, such algorithms can "dig out" personal inforamtion that will put a targeted individual in direct danger and outward violates rights for data to be protected. For perpetrators it brings out thier values of power, albeit in the negative key, while depriving targeted individual or group of their ability to succeed, autonomy, freedom to act, etc.

The solution to this problem is a stonger enforcement of the law, where all involved (social platform, moderator or chatbots, etc.) can be punished.

The often anonymous nature of social

media enables some users to feel

emboldened to post things they would

not say in face-to-face conversations.

This may include hurtful or hateful

comments targeted at specific individuals

or about entire groups of people

that might be seen as racist, sexist or

homophobic. Celebrities often report

receiving hateful messages on sites

such as Twitter, and cyber bullying is

a problem in many schools, universities

and workplaces. The targets of

“trolling” and hate speech often find

the experience very distressing. Popular

social media sites all condemn the

posting of hate speech, but generally

rely on user reports to identify and

then remove it. By the time it is

reported, the content has often been

seen and shared by many people.

DATA PERSONAL INFO

• home address

• telephone number

• mobile phone number

• email addresses

• social media identities

• places of education

• places of work

• previous addresses

• sexual orientation

• ethnic identity

• family background

(e.g. natural family, adopted,

fostered, in social care)

VALUES

• accomplishment & mastery

• being visible

• high achievement & success

• public credit & respect

• social status

• fame

• competency & proficiency

• self-respect & pride

• self-esteem

• seniority

• prestige

RECOGNITION

VALUES

RECOGNITION

•accomplishment & mastery

• being visible

• high achievement & success

• public credit & respect

• social status

• fame

• competency & proficiency

• self-respect & pride

• self-esteem

• seniority

• prestige

RIGHTS

HUMAN RIGHTS

some of your basic rights are:

• right to life

• right to respect for private and

family life

• right to personal liberty

• right to freedom of expression

• right to freedom of belief and

religion

• right to education

• right to non-discrimination

• right to a fair trial

• right not to be tortured or treated

inhumanely

Rights: HUMAN RIGHTS

Value: ALTRUISM

Data: PERSONAL INFORMATION

There are some Key Tensions

Human Rights Conflicts

• Right to safety and dignity vs. Right to free expression

• Right to privacy (for both victims and accused perpetrators)

• Right to equal participation in digital spaces

• Children's rights to special protection online

The Personal Information Paradox

• Protecting victims often requires collecting more personal data

• Identifying perpetrators may violate their privacy rights

• Context and identity matter in determining what constitutes hate speech

• Anonymity can both protect vulnerable users and enable harassers

The Altruism Challenge

• How do we foster empathy and compassion in digital spaces designed for engagement?

• Can technological systems promote altruistic behavior?

• How do we balance individual autonomy with collective well-being?

The solution is not perfect prevention (would go deep down into personal surveillance too quickly) but to create digital environments where altruism flourishes. This means:

• Designing for empathy: Making kindness easier than cruelty

• Celebrating helpers: Highlighting those who support victims

• Building bridges: Creating opportunities for positive cross-group interaction

• Fostering resilience: Empowering users to support each other

value: science&knowledge

rights: Equality

An AI should be trained on the basics of equality before being let loose on social media platforms. It should contain limitations in its basic structures like valuing objectivity and evidence-based approaches as well as always holding on to fundamental rights. An AI should not violate laws and conducts that apply in the context and since social media platforms all have guidelines on what is not allowed on there the AI should also be held accountable to those. As long as it stays within those guidelines it is what it is. If someone from outside a company would train an AI to post and it would start putting out hateful messages it would hopefully also be caught by the safety-systems active by the platform

Value: ALTRUISM

Data : HEALTH RECORDS

Example: Algorithmic justice?

When machine learning (ML) algorithms are used in justice systems and have access to public health records, several ethical challenges emerge, especially around equality, fairness, and accountability. I think the main issue is personal data privacy,

As health records contain sensitive personal data. Using them without proper consent or safeguards risks violating privacy rights, then there is also concern about data ownership—who controls the data and how it's used. Algorithms trained on data from well-resourced populations may not perform well for underserved groups, worsening health disparities. For instance, AI tools might prioritize care for insured patients while overlooking those in marginalized communities.

We need to ensure there is human oversite and review on the outcome of algorithimic justis and while building the algorithms

we should incorporate following key principals

1 Ethics-by-Design: Build fairness and inclusivity into the algorithm from the start.

2 Inclusive Data Practices: Ensure diverse representation in training data to reduce bias.

3 Explainability: Develop models that can justify their decisions in human terms.