Construct · 1 fields · 1 citations

delayed impact of fair machine learning: a standalone construct

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Algorithmic fairness

static fairness criteria can harm the long-term well-being of the groups they protect once the feedback of decisions is considered.

In algorithmic fairness, delayed impact of fair machine learning refers to static fairness criteria can harm the long-term well-being of the groups they protect once the feedback of decisions is considered. This meaning comes from Liu (2018) in arXiv.[1]

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Nearby constructs

fairness4 fields · 4 citationsjingle · one word, four constructs feedback4 fields · 5 citationsjingle · one word, four constructs harm2 fields · 2 citationsjingle · one word, two carriers

References

Liu, L. T., Dean, S., Rolf, E., Simchowitz, M., & Hardt, M. (2018). Delayed Impact of Fair Machine Learning. arXiv. https://doi.org/10.48550/arxiv.1803.04383

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