arXiv:2605.31291v1 Announce Type: cross Abstract: Recommender systems may operate under multiple, competing objectives. For example, audience reach, cultural values, public service mandate, and operational constraints must be balanced in editorial decisions of public service media. Existing approaches relying on fixed combinations of objectives or Pareto-based optimisation do not adapt to changing priorities across situations. In this paper, we propose Contextual Scalarisation Thompson Sampler (CSTS), a multi-objective contextual bandit method that learns to weight objectives as a function of
The increasing complexity of AI systems and their integration into public-facing applications necessitates more sophisticated methods for managing multiple, often conflicting, objectives.
This development addresses a critical challenge in AI deployment, enabling systems to dynamically adapt to nuanced priorities, especially in public interest domains like media and governance.
AI-driven decision-making can now incorporate more adaptive and context-aware multi-objective optimization, moving beyond fixed or Pareto-based approaches.
- · Public Service Media
- · AI Ethics Researchers
- · Recommender System Developers
- · AI Governance Bodies
- · Legacy Fixed-Objective Optimization AI systems
- · Content Platforms focused solely on engagement metrics
Recommender systems become more aligned with societal values and regulatory requirements.
Public trust in AI-driven media and information platforms may increase due to more balanced outputs.
This could set a new standard for 'responsible AI' in content distribution, influencing broader AI development paradigms.
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Read at arXiv cs.LG