Journal ArticleOPEN ACCESS

Detecting Systematic Deviations in Data and Models

Computer (2023) 56(2) 82-92
DOI: 10.1109/MC.2022.3213209
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Abstract

Trustworthy artificial intelligence researchers should seek to better detect and characterize systematic deviations in data and models (that is, bias). This article provides data scientists with motivation, theory, code, and examples on how to perform disciplined discovery of systematic deviations in data and models at the subset level.

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Speakman, S., Tadesse, G. A., Cintas, C., Ogallo, W., Akumu, T., & Oshingbesan, A. (2023). Detecting Systematic Deviations in Data and Models. Computer, 56(2), 82–92. https://doi.org/10.1109/MC.2022.3213209

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