Smith-Loud, Jamila

Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing

Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

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Towards a critical race methodology in algorithmic fairness

Hanna, A., Denton, R., Smart, A., & Smith-Loud, J. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIRace, Genetics, and Society

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Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing

Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

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A toolbox for surfacing health equity harms and biases in large language models

Pfohl, S. R., Cole-Lewis, H., Sayres, R., Neal, D., Asiedu, M., Dieng, A., Tomasev, N., Rashid, Q. M., Azizi, S., Rostamzadeh, N., et al. (2024) · Nature Medicine

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

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