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Register a new userThe H-index can be easily manipulated
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Added by
Krzysztof R. Apt
Publication date
2013
fields
Informatics Engineering
and research's language is
English
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We prove two complexity results about the H-index concerned with the Google scholar merge operation on ones scientific articles. The results show that, although it is hard to merge ones articles in an optimal way, it is easy to merge them in such a way that ones H-index increases. This suggests the need for an alternative scientific performance measure that is resistant to this type of manipulation.
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Counterfactual explanations are emerging as an attractive option for providing recourse to individuals adversely impacted by algorithmic decisions. As they are deployed in critical applications (e.g. law enforcement, financial lending), it becomes important to ensure that we clearly understand the vulnerabilities of these methods and find ways to address them. However, there is little understanding of the vulnerabilities and shortcomings of counterfactual explanations. In this work, we introduce the first framework that describes the vulnerabilities of counterfactual explanations and shows how they can be manipulated. More specifically, we show counterfactual explanations may converge to drastically different counterfactuals under a small perturbation indicating they are not robust. Leveraging this insight, we introduce a novel objective to train seemingly fair models where counterfactual explanations find much lower cost recourse under a slight perturbation. We describe how these models can unfairly provide low-cost recourse for specific subgroups in the data while appearing fair to auditors. We perform experiments on loan and violent crime prediction data sets where certain subgroups achieve up to 20x lower cost recourse under the perturbation. These results raise concerns regarding the dependability of current counterfactual explanation techniques, which we hope will inspire investigations in robust counterfactual explanations.
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