arXiv:2605.25258v1 Announce Type: cross Abstract: Recommender systems generally optimises user engagement, but this approach is dangerous in mental health contexts. When vulnerable users show signs of suicidal ideation, standard algorithms often trap them in echo chambers of harmful content, worsening their psychological state. In response, we introduce RankAid, a re-ranking method that prioritises clinical safety alongside predictive relevance. It works as an add-on layer to existing models: it penalises risky items and boosts therapeutic content depending on the user's current level of vulne
The increasing prevalence and impact of AI-driven recommender systems, coupled with growing awareness of their potential for harm in mental health contexts, necessitates immediate solutions.
This work represents a critical step towards developing ethically aligned AI, addressing the inherent tension between engagement optimization and user well-being, particularly for vulnerable populations.
Existing recommender systems can now integrate safety mechanisms that prioritize user mental health outcomes over pure engagement, potentially guiding the design of future AI applications.
- · Vulnerable internet users
- · Ethical AI developers
- · Mental health platforms
- · Content moderation companies
- · Platforms prioritizing engagement above all
- · Purely profit-driven algorithms
Recommender systems begin incorporating explicit harm reduction parameters, improving user safety.
Increased public and regulatory pressure for AI systems across various domains to include 'do no harm' principles by design.
Development of a new standard for AI ethics that balances engagement, utility, and inherent safety, shifting investment towards 'safe AI' technologies.
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