Finale Doshi-Velez
in Black box
Finale Doshi-Velez, professor at Harvard's School of Engineering and Applied Sciences, working on interpretability and machine learning in healthcare. With Been Kim she wrote Towards A Rigorous Science of Interpretable Machine Learning (2017), one of the field's standard reference points on how to define and evaluate interpretability.
Stake§
Academic and reputational — being the citation reached for when noting that interpretability lacks a definition is itself a substantial professional return. No commercial interest in the argument.
Doshi-Velez's contribution to this topic is to make "interpretability" measurable rather than assumed. The paper proposes a three-part scheme for evaluating it — application-grounded (real users on real tasks), human-grounded (simplified tasks with lay people), and functionally-grounded (a formal proxy with no humans in the loop) — which gave the field a shared way to ask whether an interpretability claim had been tested at all. It runs in parallel with Lipton's deflation of the same term.