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BirkhoffG
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Jan 19, 2024
BirkhoffG
commented
Jan 19, 2024
BirkhoffG
commented
Jan 19, 2024
| Recourse and counterfactual explanation methods concentrate on the generation of new instances that lead to contrastive predicted outcomes [@verma2020counterfactual;@karimi2020survey;@stepin2021survey]. Given their ability to provide actionable recourse, these explanations are often favored by human end-users [@binns2018s;@miller2019explanation;@Bhatt20explainable]. | ||
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| Despite progress made in counterfactual explanation research [@wachter2017counterfactual;@mothilal2020explaining;@ustun2019actionable;@upadhyay2021towards;@vo2023feature;@guo2021counternet;@guo2023rocoursenet], current research practices often restrict the evaluation of recourse explanation methods on medium-sized datasets (with under 50k data points). |
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Start with many of these methods are slow
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I am going to leave this paragraph as-is because it is more about the slowness of implementation in those libraries. It is not because these methods are slow. It is mostly because they are implemented inefficiently.
BirkhoffG
commented
Jan 19, 2024
BirkhoffG
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Jan 19, 2024
BirkhoffG
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Jan 19, 2024
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