Construct · 1 fields · 1 citations

fairness impossibility theorem: a standalone construct

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Algorithmic fairness

the result that when group base rates differ, a risk score cannot be equally calibrated and have equal false positive and false negative rates across groups at the same time.

In algorithmic fairness, fairness impossibility theorem refers to the result that when group base rates differ, a risk score cannot be equally calibrated and have equal false positive and false negative rates across groups at the same time. This meaning comes from Chouldechova (2017) in Big Data.[1]

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Nearby constructs

predictive parity1 fields · 1 citationsanchor · a standalone construct base3 fields · 4 citationsjingle · one word, three constructs big data1 fields · 1 citationsanchor · a standalone construct

References

Chouldechova, A. (2017). Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments. Big Data, 5(2), 153-163. https://doi.org/10.1089/big.2016.0047

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