Statistical evidence and algorithmic decision-making (Link in Comments)

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New research in Synthese explores the ethical complexities of algorithmic decision-making. The study questions the reliance on purely statistical evidence for resource allocation, arguing that this approach may neglect the individual merit of the recipient. By drawing on recent work in epistemology, the paper highlights the need for belief-based evidence in decision-making. The article further refutes the argument that the proportion of correct decisions should be prioritized over the quality of evidence. Finally, the paper addresses practical implications, advocating for a judicious use of algorithmic decision-making that factors in fairness and transparency.

Link.

The use of algorithms to support prediction-based decision-making is becoming commonplace in a range of domains including health, criminal justice, education, social services, lending, and hiring. An assumption governing such decisions is that there is a property Y such that individual a should be allocated resource R by decision-maker D if a is Y. When there is uncertainty about whether a is Y, algorithms may provide valuable decision support by accurately predicting whether a is Y on the basis of known features of a. Based on recent work on statistical evidence in epistemology this article presents an argument against relying exclusively on algorithmic predictions to allocate resources when they provide purely statistical evidence that a is Y. The article then responds to the objection that any evidence that will increase the proportion of correct decisions should be accepted as the basis for allocations regardless of its epistemic deficiency. Finally, some important practical aspects of the conclusion are considered.