Tensions Between the Proxies of Human Values in AI
This addresses the problem of misaligned AI accountability for practitioners and society, but it is incremental as it critiques existing approaches without proposing a new solution.
The paper argues that current mathematical definitions for AI accountability pillars like privacy, fairness, and transparency are flawed and create tensions when applied in isolation or together, pushing for a redirection to consider broader consequences and sociotechnical frameworks in practice.
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.