Respect for Human Autonomy in Recommender Systems

Abstract

Recommender systems can influence human behavior in significant ways, in some cases making people more machine-like. In this sense, recommender systems may be deleterious to notions of human autonomy. Many ethical systems point to respect for human autonomy as a key principle arising from human rights considerations, and several emerging frameworks for AI include this principle. Yet, no specific formalization has been defined. Separately, self-determination theory shows that autonomy is an innate psychological need for people, and moreover has a significant body of experimental work that formalizes and measures level of human autonomy. In this position paper, we argue that there is a need to specifically operationalize respect for human autonomy in the context of recommender systems. Moreover, that such an operational definition can be developed based on well-established approaches from experimental psychology, which can then be used to design future recommender systems that respect human autonomy.

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