  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.
