Using Edge Cases to Disentangle Fairness and Solidarity in AI Ethics

Fairness and solidarity are distinct concepts that are often conflated in AI ethics discussions. The paper demonstrates that while these principles frequently overlap and can appear interchangeable in practice, they have fundamentally different meanings and justifications that become apparent in edge cases.

Edge cases reveal the true differences between fairness and solidarity. By examining irregular scenarios in medical AI ethics and the well-known COMPAS versus ProPublica dispute in criminal justice, the paper shows how these two principles can lead to contradictory conclusions, thereby clarifying what each principle actually requires.

Applying fairness versus solidarity produces different outcomes in real-world AI systems. The paper uses specific cases in medical ethics and judicial decision-making to demonstrate that choosing one principle over the other has concrete implications for how AI systems should be designed and deployed, suggesting that organizations cannot simply treat these principles as interchangeable.

Current AI ethics frameworks may lack clarity about which principle should guide decisions. The paper implies that the field's reliance on overlapping principles creates practical obscurity, meaning practitioners may not fully understand which ethical foundation justifies their choices when fairness and solidarity diverge.

A clearer model for distinguishing these principles can help resolve ongoing debates in AI ethics. The paper proposes that understanding the distinction between fairness and solidarity provides a framework for settling contemporary disputes about how AI systems should treat individuals and groups.