Supervising Automated Decisions
Tatiana Cutts · Cambridge University Press eBooks · 2023
There is a broad consensus that human supervision holds the key to sound automated decision-making: if a decision-making policy uses the predictive outputs of a statistical algorithm, but those outputs form only part of a decision that is made ultimately by a human actor, use of those outputs will not (per se) fall foul of the requirements for due process in public and private decision-making. Thus, the focus in academic and judicial spheres has been on making sure that humans are equipped and willing to wield this ultimate decision-making power. Yet, proprietary software obscures the reasons for any given prediction; this is true both for machine learning and deterministic algorithms. And without these reasons, the decision-maker cannot accord appropriate weight to that prediction in their reasoning process. Thus, a policy of using opaque statistical software to make decisions about how to treat others is unjustified, however involved humans are along the way.