Decoding decision delegation to artificial intelligence: A mixed-methods study on the preferences of decision-makers and decision-affected in surrogate decision contexts

Abstract

Organizations are increasingly opting to use decision-making systems based on artificial intelligence (AI) to increase decision quality and speed and free up resources. However, delegating decisions to AI is challenging, especially in contexts that go beyond classical optimization problems. Current research has so far largely neglected to include the viewpoints of the decision-affected and of surrogate decision-makers in the discussion. To address these gaps, in this study we rely on two experimental designs and complement our findings with qualitative interview data to shed light on both sides: the perspective of those affected by the decision and that of the decision maker in surrogate and ethically complex decision contexts. Findings reveal that the individual decision-making perspective may lead to opposing perceptions. Whereas the willingness to delegate an ethically challenging decision to AI in a surrogate decision context is lower than that in a non-surrogate context (decision-maker perspective), people affected by the decision do not generally prefer humans to AI (decision-affected perspective). By these findings, we contribute to the literature on AI-enabled decision-making and decision delegation to non-human entities.

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