  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.
