Autonomy

• Giving rules to oneself. (Between slavery of living by others’ rules, and chaos of life without rules.)
• People can escape their own past decisions. (Dopamine rushes from Instagram Likes don’t trap users in trivial pursuits.)
• People can make decisions. (Amazon displays products to provide choice, not to nudge choosing.)
• People can experiment with new decisions: users have access to opportunities and possibilities (professional, romantic, cultural, intellectual) outside those already established by their personal data.

Dignity

• Users hold intrinsic value: they are subjects/ends, not objects/means.
• People treated as individually responsible for – and made responsible for – their choices. (Freedom from patronization.)
• The AI/human distinction remains clear. (Is the voice human, or a chatbot?)

Privacy

• Control over access to one's own personal information along the entire value chain.
• Legitimate interest of the data controller in the information.
• Protections installed against accidental data exposure and cyber-attack.
• People can maintain multiple, independent personal information identities. (Work-life, family-life, romantic-life, can remain unmixed online.)
  • How Facebook Outs Sex Workers - Gizmodo
  • Facebook’s Zuckerberg: "Having two identities for yourself is an example of a lack of integrity"

How much intimate information about myself will I expose for the right job offer, or an accurate romantic match?

Originally, health insurance enabled adventurous activities (like skiing the double black diamond run) by promising to pay the emergency room bill if things went wrong. Today, dynamic AI insurance converts personal information into consumer rewards by lowering premiums in real time for those who avoid risks like the double black diamond. What changed?

An AI chatbot mitigates depression when patients believe they are talking with a human. Should the design – natural voice, and human conversational indicators like the occasional cough – encourage that misperception?

If my tastes, fears and urges are perfectly satisfied by predictive analytics, I become a contented prisoner inside my own data set: I always get what I want, even before I realize that I want it. How – and should – AI platforms be perverted to create opportunities and destinies outside those accurately modeled for who my data says I am?

• Controlling Polarization in Personalization: An Algorithmic Framework
• Inside the Big Data Experience Machine

What’s worth more: freedom and dignity, or contentment and health?

Fairness

• Equals treated equally and unequals treated unequally. (Classical approach, Aristotle.)
• Equalities measured as opportunities or outcomes?
• Bias/discrimination suppressed in information gathering and application: Recognition of individual, cultural, and historical biases inhabiting apparently neutral data.
• Data bias amplification (unbalanced outcomes feeding back into processes) is mitigated. Example: an AI resume filter privileging a specific trait creates outcomes that in turn support the privileging.

Solidarity

• No one left behind: training data inclusive so that AI functions for all.
• Max/Min distribution of AI benefits: the most to those who have least (Rawls).
• Stakeholder participation in AI design/implementation. (What do the other drivers think of the driverless car passing on the left?)

Sustainability

• Established metrics for socio-economic flourishing at the business/community intersection include infant mortality and healthcare, hunger and water, sewage and infrastructure, poverty and wealth, education and technology access. (17 UN Sustainable Development Goals.)

Which is primary: equal opportunity for individuals, or equal outcomes for race, gender and similar identity groups?

AI catering to individualized tastes, vulnerabilities, and urges effectively diminishes awareness of the others’ tastes, vulnerabilities and urges – users are decreasingly exposed to their music, their literature, their values and beliefs. On the social level, is it better for people to be content, or to be together?

An AI detects breast cancer from scans earlier than human doctors, but it trained on data from white women. Should the analyzing pause until data can be accumulated – and efficacy proven – for all races?

Those positioned to exploit AI technology will exchange mundane activities for creative, enriching pursuits, while others inherit joblessness and tedium. Or so it is said. Who decides what counts as creative, interesting and worthwhile versus mundane, depressing and valueless – and do they have a responsibility to uplift their counterparts?

What counts as fair? Aristotle versus Rawls.

Is equality about verbs (what you can do), or nouns (who you are, what you have)?

In the name of solidarity, how much do individuals sacrifice for the community?

Performance

• Accuracy & Efficiency
• Personalized quality and convenience.

Safety

• Secure, resilient and empowered with fallbacks to mitigate failures.
• Human oversight: Designer or a deployment supervisor holds power to control AI actions.

Explainability & Accountability

• Explainability: Ensure AI decisions can be understood and traced by human intelligence, which may require debilitating AI accuracy/efficiency. (As a condition of the possibility of producing knowledge exclusively through correlation, AI may not be explainable.) Interpretability – calculating the weights assigned to datapoints in AI processing – may substitute for unattainable explainability. (Auditors understand what data most influences outputs, even if they cannot perceive why.)
• Redress

A chatbot responds to questions about history, science and the arts instantly, and so delivers civilization’s accumulated knowledge with an efficiency that withers the ability to research and to discover for ourselves (Why exercise thinking when we have easy access to everything we want to know?) Is perfect knowledge worth intellectual stagnation?

Compared to deaths per car trip today, how great an increment would be acceptable to switch to all-driverless cars, ones prone to the occasional glitch and consequent, senseless wreck?

If an AI picks stocks, predicts satisfying career choices, or detects cancer, but only if no one can understand how the machine generates knowledge, should it be used?

• The Delphic dilemma

What’s worth more, understanding or knowledge? (Knowing, or knowing why you know?)

Which is primary, making AI better, or knowing who to blame, and why, when it fails?

What, and how much will we risk for better accuracy and efficiency?

What counts as risk, and who takes it?

A driverless car AI system refines its algorithms by imitating driving habits of the human owner (driving distance between cars, accelerating, breaking, turning radiuses). The car later crashes. Who is to blame?

Ethics Guidelines for Trustworthy AI
AI High Level Expert Group, European Commission
https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai

Report of the German Data Ethics Commission
https://www.bmi.bund.de/EN/topics/it-internet-policy/data-ethics-commission/data-ethics-commission-node.html

European Ethical Charter on the Use of Artificial Intelligence in Judicial Systems and their Environment
European Commission for the Efficiency of Justice
https://rm.coe.int/ethical-charter-en-for-publication-4-december-2018/16808f699c

Ethical and Societal Implications of Data and AI
Nuffield Foundation
https://www.nuffieldfoundation.org/sites/default/files/files/Ethical-and-Societal-Implications-of-Data-and-AI-report-Sheffield-Foundat.pdf

The Five Principles Key to Any Ethical Framework for AI
New Statesman, Luciano Floridi and Lord Clement-Jones
https://tech.newstatesman.com/policy/ai-ethics-framework

Postscript on Societies of Control
October 1997, Deleuze
/Library: Deleuze, Foucault, Discipline, Control.pdf

A Declaration of the Independence of Cyberspace
John Perry Barlow
https://www.eff.org/cyberspace-independence