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Found 4 papers, 0 papers with code
Summon a Demon and Bind it: A Grounded Theory of LLM Red Teaming in the Wild
As a result, this paper presents a grounded theory of how and why people attack large language models: LLM red teaming in the wild.
Building Human Values into Recommender Systems: An Interdisciplinary Synthesis
We collect a set of values that seem most relevant to recommender systems operating across different domains, then examine them from the perspectives of current industry practice, measurement, product design, and policy approaches.
What are you optimizing for? Aligning Recommender Systems with Human Values
We describe cases where real recommender systems were modified in the service of various human values such as diversity, fairness, well-being, time well spent, and factual accuracy.
Designing Recommender Systems to Depolarize
Polarization is implicated in the erosion of democracy and the progression to violence, which makes the polarization properties of large algorithmic content selection systems (recommender systems) a matter of concern for peace and security.
