Normative Ethics Principles for Responsible AI Systems: Taxonomy and Future Directions

The rapid adoption of artificial intelligence (AI) necessitates careful analysis of its ethical implications. In addressing ethics and fairness implications, it is important to examine the whole range of ethically relevant features rather than looking at individual agents alone. This can be accomplished by shifting perspective to the systems in which agents are embedded, which is encapsulated in the macro ethics of sociotechnical systems (STS). Through the lens of macro ethics, the governance of systems - which is where participants try to promote outcomes and norms which reflect their values - is key. However, multiple-user social dilemmas arise in an STS when stakeholders of the STS have different value preferences or when norms in the STS conflict. To develop equitable governance which meets the needs of different stakeholders, and resolve these dilemmas in satisfactory ways with a higher goal of fairness, we need to integrate a variety of normative ethical principles in reasoning. Normative ethical principles are understood as operationalizable rules inferred from philosophical theories. A taxonomy of ethical principles is thus beneficial to enable practitioners to utilise them in reasoning. This work develops a taxonomy of normative ethical principles which can be operationalized in the governance of STS. We identify an array of ethical principles, with 25 nodes on the taxonomy tree. We describe the ways in which each principle has previously been operationalized, and suggest how the operationalization of principles may be applied to the macro ethics of STS. We further explain potential difficulties that may arise with each principle. We envision this taxonomy will facilitate the development of methodologies to incorporate ethical principles in reasoning capacities for governing equitable STS.