Construct · 1 fields · 1 citations

aggregation bias: a standalone construct

One name. One construct. A landmark on the map.

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Algorithmic fairness

harm arising when one model is used for groups whose data relate inputs and outcomes differently, so the model fits no group well.

In algorithmic fairness, aggregation bias refers to harm arising when one model is used for groups whose data relate inputs and outcomes differently, so the model fits no group well. This meaning comes from Suresh (2021) in Equity and Access in Algorithms, Mechanisms, and Optimization.[1]

Unit
Recovery means
Measured by

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Nearby constructs

historical bias1 fields · 1 citationsanchor · a standalone construct representation bias1 fields · 1 citationsanchor · a standalone construct bias6 fields · 8 citationsjingle · one name, six constructs

References

Suresh, H., & Guttag, J. (2021). A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle. Equity and Access in Algorithms, Mechanisms, and Optimization. https://doi.org/10.1145/3465416.3483305

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