Position: Tensions Between the Proxies of Human Values in AI

Teresa Datta, Daniel Nissani, Max Cembalest, Akash Khanna, Haley Massa, John P Dickerson

Keywords: position, fairness, privacy, transparency, XAI, tension, human values, bias, interpretability, explainability

Abstract: Motivated by mitigating potentially harmful impacts of technologies, the AI community has formulated and accepted mathematical definitions for certain pillars of accountability: e.g. privacy, fairness, and model transparency. Yet, we argue this is fundamentally misguided because these definitions are imperfect, siloed constructions of the human values they hope to proxy, while giving the guise that those values are sufficiently embedded in our technologies. Under popularized methods, tensions arise when practitioners attempt to achieve each pillar of fairness, privacy, and transparency in isolation or simultaneously. In this position paper, we push for redirection. We argue that the AI community needs to consider all the consequences of choosing certain formulations of these pillars---not just the technical incompatibilities, but also the effects within the context of deployment. We point towards sociotechnical research for frameworks for the latter, but push for broader efforts into implementing these in practice.

TL;DR: There are insurmountable tensions in the current implementations of “accountable AI” — we argue that AI community needs to consider alternative formulations of fairness, privacy, and transparency based on the context in which technology is situated.