How AI can learn from the law: putting humans in the loop only on appeal.

While the literature on putting a "human in the loop" in artificial intelligence (AI) and machine learning (ML) has grown significantly, limited attention has been paid to how human expertise ought to be combined with AI/ML judgments. This design question arises because of the ubiquity and quantity of algorithmic decisions being made today in the face of widespread public reluctance to forgo human expert judgment. To resolve this conflict, we propose that human expert judges be included via appeals processes for review of algorithmic decisions. Thus, the human intervenes only in a limited number of cases and only after an initial AI/ML judgment has been made. Based on an analogy with appellate processes in judiciary decision-making, we argue that this is, in many respects, a more efficient way to divide the labor between a human and a machine. Human reviewers can add more nuanced clinical, moral, or legal reasoning, and they can consider case-specific information that is not easily quantified and, as such, not available to the AI/ML at an initial stage. In doing so, the human can serve as a crucial error correction check on the AI/ML, while retaining much of the efficiency of AI/ML's use in the decision-making process. In this paper, we develop these widely applicable arguments while focusing primarily on examples from the use of AI/ML in medicine, including organ allocation, fertility care, and hospital readmission.

Autonomy in consumer choice.

We propose that autonomy is a crucial aspect of consumer choice. We offer a definition that situates autonomy among related constructs in philosophy and psychology, contrast actual with perceived autonomy in consumer contexts, examine the resilience of perceived autonomy, and sketch out an agenda for research into the role of perceived autonomy in an evolving marketplace increasingly characterized by automation.

Procrastination, deadlines, and performance: self-control by precommitment.

Procrastination is all too familiar to most people. People delay writing up their research (so we hear!), repeatedly declare they will start their diets tomorrow, or postpone until next week doing odd jobs around the house. Yet people also sometimes attempt to control their procrastination by setting deadlines for themselves. In this article, we pose three questions: (a) Are people willing to self-impose meaningful (i.e., costly) deadlines to overcome procrastination? (b) Are self-imposed deadlines effective in improving task performance? (c) When self-imposing deadlines, do people set them optimally, for maximum performance enhancement? A set of studies examined these issues experimentally, showing that the answer is "yes" to the first two questions, and "no" to the third. People have self-control problems, they recognize them, and they try to control them by self-imposing costly deadlines. These deadlines help people control procrastination, hit they are not as effective as some externally imposed deadlines in improving task performance.