Decision-aid or Controller? Steering Human Decision Makers with Algorithms
This work addresses the challenge of algorithmic manipulation in decision-making for fields like AI ethics and policy, though it is incremental by building on game theory models.
The paper tackles the problem of algorithms influencing human decisions by shifting from optimizing prediction accuracy to controlling final decisions, showing that perfect control is possible under certain conditions and that strategic human responses lead to coarse information sharing in equilibria.
Algorithms are used to aid human decision makers by making predictions and recommending decisions. Currently, these algorithms are trained to optimize prediction accuracy. What if they were optimized to control final decisions? In this paper, we study a decision-aid algorithm that learns about the human decision maker and provides ''personalized recommendations'' to influence final decisions. We first consider fixed human decision functions which map observable features and the algorithm's recommendations to final decisions. We characterize the conditions under which perfect control over final decisions is attainable. Under fairly general assumptions, the parameters of the human decision function can be identified from past interactions between the algorithm and the human decision maker, even when the algorithm was constrained to make truthful recommendations. We then consider a decision maker who is aware of the algorithm's manipulation and responds strategically. By posing the setting as a variation of the cheap talk game [Crawford and Sobel, 1982], we show that all equilibria are partition equilibria where only coarse information is shared: the algorithm recommends an interval containing the ideal decision. We discuss the potential applications of such algorithms and their social implications.