Respect for Human Autonomy in Recommender Systems

TL;DR

This paper emphasizes the importance of operationalizing respect for human autonomy in recommender systems by leveraging established psychological theories to ensure ethical AI design.

Contribution

It proposes a novel framework to formalize respect for human autonomy in recommender systems using experimental psychology principles.

Findings

Highlights the influence of recommender systems on human autonomy

Suggests a formalization approach based on self-determination theory

Lays groundwork for designing autonomy-respecting 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.