Explainability's Gain is Optimality's Loss? -- How Explanations Bias Decision-making. (arXiv:2206.08705v1 [cs.HC])

Web: http://arxiv.org/abs/2206.08705

Decisions in organizations are about evaluating alternatives and choosing the
one that would best serve organizational goals. To the extent that the
evaluation of alternatives could be formulated as a predictive task with
appropriate metrics, machine learning algorithms are increasingly being used to
improve the efficiency of the process. Explanations help to facilitate
communication between the algorithm and the human decision-maker, making it
easier for the latter to interpret and make decisions on the basis of
predictions by the former. …

arxiv bias decision explainability loss making