Date
Publisher
arXiv
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.
Study at a glance
- Question: How can policy, research, and education address the ethical and anthropological challenges of AI recommender systems?
- Setting and data: Not applicable; theoretical analysis with no empirical dataset analyzed.
- Method: Conceptual and policy analysis (theoretical framework development)
Why it matters
AI recommender systems actively shape user experiences, behaviors, and identity. Embedding ethical principles directly into algorithms remains insufficient to prevent privacy loss, behavioral manipulation, and risks to mental well-being.
Key findings
- Recommender systems reduce human complexity into quantifiable behavioral profiles, prioritizing engagement metrics over user well-being and eroding independent moral and cognitive discernment.
- Technical approaches such as algorethics and value alignment cannot resolve these harms alone without stable, universal ethical standards and standardized evaluation criteria.
- Mitigating algorithmic harms requires a mutually reinforcing framework combining robust policy regulation, interdisciplinary research, and school-based AI literacy education that empowers users.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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