A statistical framework providing quantitative bounds on the effectiveness of multiple collectives steering classifier behavior via coordinated data modifications.
Fairness and abstraction in sociotechnical systems
2 Pith papers cite this work. Polarity classification is still indexing.
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Proposes a value-encoding framework to characterize and counter homogenization in LLMs by formalizing it via normativity from queer theory and introducing xeno-reproduction tasks from feminist theory, illustrated with a gender-bias experiment on Claude 3.5 Haiku.
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A Statistical Framework for Algorithmic Collective Action with Multiple Collectives
A statistical framework providing quantitative bounds on the effectiveness of multiple collectives steering classifier behavior via coordinated data modifications.
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The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safety
Proposes a value-encoding framework to characterize and counter homogenization in LLMs by formalizing it via normativity from queer theory and introducing xeno-reproduction tasks from feminist theory, illustrated with a gender-bias experiment on Claude 3.5 Haiku.