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Measuring machine learning harms from stereotypes: requires understanding who is being harmed by which errors in what ways
However, certain stereotype-violating errors are more experientially harmful for men, potentially due to perceived threats to masculinity.
Measuring Implicit Bias in Explicitly Unbiased Large Language Models
Large language models (LLMs) can pass explicit bias tests but still harbor implicit biases, similar to humans who endorse egalitarian beliefs yet exhibit subtle biases.
Warmth and competence in human-agent cooperation
Participants' perceptions of warmth and competence predict their stated preferences for different agents, above and beyond objective performance metrics.
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