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<p>Whenever an AI model is used to predict a relevant (binary) outcome in AI-assisted decision making, it is widely agreed that, together with each prediction, the model should provide an AI confidence value. However, it has been unclear why decision makers have often difficulties to develop a good sense on when to trust a prediction using AI confidence values. Very recently, Corvelo Benz and Gomez Rodriguez have argued that, for rational decision makers, the utility of AI-assisted decision making is inherently bounded by the degree of alignment between the AI confidence values and the decision maker’s confidence on their own predictions. In this work, we empirically investigate to what extent the degree of alignment actually influences the utility of AI-assisted decision making. To this end, we design and run a large-scale human subject study (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_12205_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="57" /> </InlineMediaObject> <EquationSource Format="TEX">\(n = 703\)</EquationSource> </InlineEquation>) where participants solve a simple decision making task—an online card game—assisted by an AI model with a steerable degree of alignment. Our results show a positive association between the degree of alignment and the utility of AI-assisted decision making. In addition, our results also show that post-processing the AI confidence values to achieve multicalibration with respect to the participants’ confidence on their own predictions increases both the degree of alignment and the utility of AI-assisted decision making.</p>

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Human-alignment influences the utility of AI-assisted decision making

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月9日

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  • Nina L. Corvelo Benz,
  • Manuel Gomez Rodriguez

摘要

Whenever an AI model is used to predict a relevant (binary) outcome in AI-assisted decision making, it is widely agreed that, together with each prediction, the model should provide an AI confidence value. However, it has been unclear why decision makers have often difficulties to develop a good sense on when to trust a prediction using AI confidence values. Very recently, Corvelo Benz and Gomez Rodriguez have argued that, for rational decision makers, the utility of AI-assisted decision making is inherently bounded by the degree of alignment between the AI confidence values and the decision maker’s confidence on their own predictions. In this work, we empirically investigate to what extent the degree of alignment actually influences the utility of AI-assisted decision making. To this end, we design and run a large-scale human subject study ( \(n = 703\) ) where participants solve a simple decision making task—an online card game—assisted by an AI model with a steerable degree of alignment. Our results show a positive association between the degree of alignment and the utility of AI-assisted decision making. In addition, our results also show that post-processing the AI confidence values to achieve multicalibration with respect to the participants’ confidence on their own predictions increases both the degree of alignment and the utility of AI-assisted decision making.