Free will, temptation, and self-control: We must believe in free will, we have no choice (Isaac B. Singer).

Baumeister, Sparks, Stillman, and Vohs (2007) sketch a theory of free will as the humanability to exert self-control. Self-control can produce goal-directed behavior, which free will conceptualized as random behavior cannot. We question whether consumer psychology can shed light on the ontological question of whether free will exists. We suggest that it is more fruitful for consumer psychology to examine consumers' belief in freewill. Specifically, we propose that this belief arises from consumers' phenomenological experience of exercising self-control in the face of moral or intertemporal conflicts of will. Based on extant literature in philosophy, psychology, and economics, we offer both a narrower conceptualization of the nature of self-control problems and a more general conceptualization of self-control strategies, involving not only will power but also precommitment. We conclude with a discussion of the consequences of consumers' belief in free will.

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

Advances in Consumer Research

We examine how redistribution preferences affect the value of status consumption: people who favor less (more) fiscal redistribution value status consumption more as a meaningful signal because they consider income more deserved (i.e., a stronger belief in a just world). Data come from a national consumer survey and two experiments