Beyond Algorethics: Addressing the Ethical and Anthropological Challenges of AI Recommender Systems

TL;DR

This paper explores the ethical and anthropological issues of AI recommender systems, proposing a human-centered framework that combines policy, research, and education to promote user well-being and autonomy.

Contribution

It introduces a three-dimensional framework for human-centered RSs, integrating policies, interdisciplinary research, and education to address ethical challenges.

Findings

Current RSs reduce human complexity and exploit vulnerabilities.

Existing ethical approaches like algorethics are insufficient.

A combined policy, research, and education strategy can improve RSs.

Abstract

This paper examines the ethical and anthropological challenges posed by AI-driven recommender systems (RSs), which increasingly shape digital environments and social interactions. By curating personalized content, RSs do not merely reflect user preferences but actively construct experiences across social media, entertainment platforms, and e-commerce. Their influence raises concerns over privacy, autonomy, and mental well-being, while existing approaches such as "algorethics" - the effort to embed ethical principles into algorithmic design - remain insufficient. RSs inherently reduce human complexity to quantifiable profiles, exploit user vulnerabilities, and prioritize engagement over well-being. The paper advances a three-dimensional framework for human-centered RSs, integrating policies and regulation, interdisciplinary research, and education. These strategies are mutually reinforcing: research provides evidence for policy, policy enables safeguards and standards, and education equips users to engage critically. By connecting ethical reflection with governance and digital literacy, the paper argues that RSs can be reoriented to enhance autonomy and dignity rather than undermine them.