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A mixed-stakeholder workshop study exploring how community representatives, police officers, and academics negotiate risk boundaries for AI policing use cases, focusing on racial bias. Findings show broad openness to AI adoption except for recidivism risk assessment, with deliberations centering on practical effectiveness and equitable benefit.
A solo evaluation of six frontier LLMs on 8 bias benchmarks finds that most models lean left politically, and Grok's self-reported right-leaning stance is inconsistent with its left-leaning behavior. Refusal rates vary, with GPT-5.4 refusing 20% of race-related questions.
This paper audits 14 large language models for hiring discrimination using a paired-resume methodology, finding that older models exhibit pro-White bias while newer models show null or pro-Black bias, indicating a reversal in algorithmic hiring bias across model generations.
Stanford HAI reports that AI hiring tools can yield racial bias and systemic rejection due to algorithmic monocultures, where similar models lead to widespread discrimination.