‑

• 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.
• 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

• Equal opportunity for individuals. (Classical approach.)
• Equal outcomes for groups, especially those historically marginalized. (Social justice approach.)
• 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.

Fairness as solidarity or social justice

• 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.
• Stakeholder participation in AI design/implementation. (What do the other drivers think of the driverless car passing on the left?)

Social

• 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, convenience, pleasure.
• Secure, resilient and empowered with fallbacks to mitigate failures.

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.)
• Transparency about the limitations of explanations: If the AI is a blackbox, is the opacity clear?
• Human oversight: Designer or a deployment supervisor holds power to control - or cede control to - AI actions.
• Redress for users implies an ability to contest AI decisions.

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 a decrease would be required to switch to only 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?

• Does the AI empower users to do new things?
• Does it provide opportunities that were previously unthinkable?
• Does it provide opportunities that were previously unavailable?
• Does the AI short-circuit human self-determination?
• Have measures been taken to mitigate the risk of manipulation, and to eliminate dark patterns?
• Has the risk of addiction been minimized?
• If the AI system could generate over-reliance by end-users, are procedures in place to avoid end-user over-reliance?
• Could the AI system affect human autonomy by interfering with the end-user’s decision-making process in any other unintended and undesirable way?
• Are users respected as the reason for the AI? Does the machine primarily serve the users' projects and goals, or does it employ users as tools or instruments in external projects?
• Are mechanisms established to inform users about the full range of purposes, and the limitations of the decisions generated by the AI system?
• Are the technical limitations and potential risks of the AI communicated to users, such as its level of accuracy and/ or error rates?
• Does the AI system simulate social interaction with or between end-users or subjects (chatbots, robo-lawyers and similar)?
• Could the AI system generate confusion about whether users are interacting with a human or AI system? Are end-users informed that they are interacting with an AI system?

• Is the AI system trained or developed by using or processing personal data?
• Do users maintain control over access to their personal information? Is it within their power to control what is known and shared and where it is shared?
• Is data minimization, in particular personal data, in effect?
• Is the AI system aligned with relevant standards (e.g. ISO, IEEE) or widely adopted protocols for (daily) data management and governance?
• Are the following measures, or non-European equivalents, established?
• Data Protection Impact Assessment (DPIA).
• Designate a Data Protection Officer (DPO) and include them at an early state in the development, procurement or use phase of the AI system.
• Oversight mechanisms for data processing (including limiting access to qualified personnel, mechanisms for logging data access, and for making modifications).
• Measures to achieve privacy-by-design and default (e.g. encryption, pseudonymization, aggregation, anonymisation).
• The right to withdraw consent, the right to object, and the right to be forgotten implemented into the AI's development.
• Have privacy and data protection implications been considered for data collected, generated, or processed over the course of the AI system's life cycle?

• Are equals treated equally and unequals treated unequally by the AI? (Aristotle’s definition of fairness.)
• Have procedures been established to avoid creating or reinforcing unfair bias in the AI system for input data, as well as for the algorithm design?
• Is your statistical definition of fairness commonly used? Were other definitions of fairness considered?
• Was a quantitative analysis or metric developed to test the applied definition of fairness?
• Was the diversity and representativeness of end-users and subjects in the data considered?
• Were tests applied for specific target groups, or problematic use cases?
• Did you consult with the impacted communities about fairness definitions, for example representatives of the elderly, or persons with disabilities?
• Were publicly available technical tools that are state-of-the-art researched to improve understanding of the data, model, and performance?
• Did you assess and put in place processes to test and monitor for potential biases during the entire lifecycle of the AI system (e.g. biases due to possible limitations stemming from the composition of the used data sets (lack of diversity, non-representativeness)?
• Is there a mechanism for flagging issues related to bias, discrimination, or poor performance of the AI?
• Are clear steps and ways of communicating established for how and to whom such issues can be raised?

• Is the AI designed so that no one is left behind?
• Does the AI deliver the maximum advantage to those users who are most disadvantaged? (Does the most go to those who have least? John Rawls’ definition of Solidarity/Justice.)
• Is the AI adequate to the variety of preferences and abilities in society?
• Were mechanisms considered to include the participation of the widest possible range of stakeholders in the AI’s design and development?
• Were Universal Design principles taken into account during every step of the planning and development?
• Did you assess whether the AI system's user interface is usable by those with special needs or disabilities, or those at risk of exclusion?
• Were end-users or subjects in need for assistive technology consulted during the planning and development phase of the AI system?
• Was the impact of the AI on all potential subjects taken into account?
• Could there be groups who might be disproportionately affected by the outcomes of the AI system?

• For societies around the world, does the AI advance toward, or recede from the United Nations’ Sustainable Development Goals? For elaboration of the particular goals, see: United Nations’ Sustainable Development Goals
1: No poverty
2: Zero hunger
3: Good health and well-being
4: Quality education
5: Gender equality
6: Clean water and sanitation
7: Affordable and clean energy
8: Decent work and economic growth
9: Resilient industry, innovation, and infrastructure
10: Reducing inequalities
11: Sustainable cities and communities
12: Responsible consumption and production
13: Climate change
14: Life below water
15: Life on land
16: Peace and Justice Strong Institutions
17: Partnerships for the Goals

• Has a definition of what counts as performance been articulated for the AI: Accuracy? Rapidity? Efficiency? Convenience? Pleasure?
• Is there a clear and distinct performance metric?
• Does the metric correspond with human experience?
• Does the AI outperform humans? Other machines?

• Could the AI significantly harm human life in case of design or technical faults, defects, outages, attacks, misuse, inappropriate or malicious use?
• Can responsibility for the development, deployment, use and output of AI systems be attributed?
  • Can you explain the AI's decisions to users? Are you transparent about the limitations of explanations?
• Are accessible mechanisms for accountability in place to ensure contestability and/or redress when adverse or unjust impacts occur?
  • Have redress-by-design mechanisms been put in place?
• Did you establish mechanisms that facilitate the AI system’s auditability (e.g. traceability of the development process, the sourcing of training data and the logging of the AI system’s processes, outcomes, positive and negative impact)?
• Are there verification and validation methods, and documentation (e.g. logging) to evaluate and ensure different aspects of the AI system’s reliability and reproducibility?
• Did you consider establishing an AI ethics review board or a similar mechanism to discuss the overall accountability and ethics practices?
• Does review go beyond the development phase?
• Did you establish a process for third parties (e.g. suppliers, end-users, subjects, distributors/vendors or workers) to report potential vulnerabilities, risks, or biases in the AI system?
• Are failsafe fallback plans defined and tested to address AI system errors of whatever origin, and are governance procedures in place to trigger them?
  • Have the humans in-the-loop (human intervention in every decision of the system), or on-the-loop (human monitoring and potential intervention in the system’s operation), been given specific training on how to exercise oversight?
  • Is there a ‘stop button’ or procedure to safely abort an operation when needed?
• Is there a process to continuously measure and assess risks?
• Were potential negative consequences from the AI system learning novel or unusual methods to score well on its objective function considered?
• Can the AI system's operation invalidate the data or assumptions it was trained on? Could this lead to adverse effects?
• Is there a proper procedure for handling the cases where the AI system yields results with a low confidence score?
• How exposed is the AI system to cyber-attacks?
• Were different types of vulnerabilities and potential entry points for attacks considered, such as:
• Data poisoning (i.e. manipulation of training data).
• Model evasion (i.e. classifying the data according to the attacker's will).
• Model inversion (i.e. infer the model parameters)
• Did you red-team and/or penetration test the system?
• Is the AI system certified for cybersecurity and compliant with applicable security standards?