arXiv:2606.02883v1 Announce Type: cross Abstract: Recommender systems have grown from content-organization tools into sophisticated systems that shape daily behavior. By controlling what we see, they shape what we perceive, raising concerns about filter bubbles, radicalization, polarization, and social inequality. Large language models (LLMs) enable more powerful personalization, intensifying these dynamics. Yet most recommenders are tuned for engagement or limited accuracy metrics, with little attention to broader social implications, e.g. how personalization reshapes exposure in socially con
The increasing sophistication of LLMs and growing public awareness of their influence on information consumption are driving the need for more ethical and nuanced recommender systems.
This development indicates a technical pathway to address critical social concerns around algorithmic bias and filter bubbles, moving beyond pure engagement metrics to incorporate broader societal objectives.
Recommender systems are shifting from solely optimizing for engagement to explicitly operationalizing nuanced objectives like diversity, fairness, and reduced polarization through LLM-assisted reranking.
- · Ethical AI developers
- · Users seeking diverse information
- · Social scientists
- · Platforms prioritizing societal impact
- · Platforms optimizing purely for engagement
- · Content generating filter bubbles
- · Simple accuracy metric-focused recommenders
Recommender systems will begin to incorporate more complex, 'human-like' values into their ranking algorithms.
This could lead to a measurable reduction in filter bubbles and platform-fueled polarization, fostering more diverse information consumption.
Improved social outcomes from recommendation engines could build greater public trust in AI, potentially influencing regulatory approaches.
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Read at arXiv cs.AI