The evidence behind the guides

Sources

Find a study, check what it supports, and follow it back to a concern.

TopicCollection

2 sources

Guide citations are still under editorial review.

Suresh and Guttag · EAAMO 2021 Research framework

A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle (original source, new tab)

Framework distinguishes historical, representation, measurement, aggregation, learning, evaluation, and deployment bias. These categories can overlap.

Rabanser, Günnemann and Lipton · NeurIPS 2019 Research study

Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift (original source, new tab)

Evaluates methods for detecting distribution changes; a detected shift is not automatically a harmful performance change.

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