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Research & Resources

Our work is informed by leading research in the field of responsible AI.

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RAIL in the Wild

Operationalizing Responsible AI Evaluation Using Anthropic's Value Dataset

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Transparency in AI: Beyond Explainability

Explores transparency frameworks for AI systems, emphasizing accountability and user trust.

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Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

Seminal work identifying gender stereotypes in word embeddings and proposing methods to debias them.

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Detecting and Mitigating Harmful Outputs in Generative AI

Techniques to identify and reduce harmful content generated by advanced AI models.

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Bias in Multimodal AI Systems

Analysis of bias propagation in multimodal AI systems combining text, image, and audio data.

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