Algorithmic Assistance with Recommendation-Dependent Preferences

Understand

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 react not only 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 model of joint human-machine decision-making.