Algorithmic Assistance with Recommendation-Dependent Preferences
- URL: http://arxiv.org/abs/2208.07626v3
- Date: Fri, 19 Jan 2024 16:52:27 GMT
- Title: Algorithmic Assistance with Recommendation-Dependent Preferences
- Authors: Bryce McLaughlin and Jann Spiess
- Abstract summary: We consider the effect and design of algorithmic recommendations when they affect choices.
We show that recommendation-dependent preferences create inefficiencies where the decision-maker is overly responsive to the recommendation.
- Score: 2.864550757598007
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: When an algorithm provides risk assessments, we typically think of them as
helpful inputs to human decisions, such as when risk scores are presented to
judges or doctors. However, a decision-maker may not only react to the
information provided by the algorithm. The decision-maker may also view the
algorithmic recommendation as a default action, making it costly for them to
deviate, such as when a judge is reluctant to overrule a high-risk assessment
for a defendant or a doctor fears the consequences of deviating from
recommended procedures. To address such unintended consequences of algorithmic
assistance, we propose a principal-agent model of joint human-machine
decision-making. Within this model, we consider the effect and design of
algorithmic recommendations when they affect choices not just by shifting
beliefs, but also by altering preferences. We motivate this assumption from
institutional factors, such as a desire to avoid audits, as well as from
well-established models in behavioral science that predict loss aversion
relative to a reference point, which here is set by the algorithm. We show that
recommendation-dependent preferences create inefficiencies where the
decision-maker is overly responsive to the recommendation. As a potential
remedy, we discuss algorithms that strategically withhold recommendations, and
show how they can improve the quality of final decisions.
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