Counterfactual Situation Testing: Uncovering Discrimination under Fairness given the Difference

• URL: http://arxiv.org/abs/2302.11944v3
• Date: Mon, 16 Oct 2023 15:08:43 GMT
• Title: Counterfactual Situation Testing: Uncovering Discrimination under Fairness given the Difference
• Authors: Jose M. Alvarez and Salvatore Ruggieri
• Abstract summary: We present counterfactual situation testing (CST), a causal data mining framework for detecting discrimination in classifiers. CST aims to answer in an actionable and meaningful way the question "what would have been the model outcome had the individual, or complainant, been of a different protected status?"
• Score: 26.695316585522527
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present counterfactual situation testing (CST), a causal data mining framework for detecting discrimination in classifiers. CST aims to answer in an actionable and meaningful way the intuitive question "what would have been the model outcome had the individual, or complainant, been of a different protected status?" It extends the legally-grounded situation testing of Thanh et al. (2011) by operationalizing the notion of fairness given the difference using counterfactual reasoning. For any complainant, we find and compare similar protected and non-protected instances in the dataset used by the classifier to construct a control and test group, where a difference between the decision outcomes of the two groups implies potential individual discrimination. Unlike situation testing, which builds both groups around the complainant, we build the test group on the complainant's counterfactual generated using causal knowledge. The counterfactual is intended to reflect how the protected attribute when changed affects the seemingly neutral attributes used by the classifier, which is taken for granted in many frameworks for discrimination. Under CST, we compare similar individuals within each group but dissimilar individuals across both groups due to the possible difference between the complainant and its counterfactual. Evaluating our framework on two classification scenarios, we show that it uncovers a greater number of cases than situation testing, even when the classifier satisfies the counterfactual fairness condition of Kusner et al. (2017).

Related papers

• Probing Network Decisions: Capturing Uncertainties and Unveiling Vulnerabilities Without Label Information [19.50321703079894]
  We present a novel framework to uncover the weakness of the classifier via counterfactual examples. We test the performance of our prober's misclassification detection and verify its effectiveness on the image classification benchmark datasets.
  arXiv  Detail & Related papers  (2025-03-12T05:05:58Z)
• Counterfactual Situation Testing: From Single to Multidimensional Discrimination [22.596961524387233]
  We present counterfactual situation testing (CST), a causal data mining framework for detecting individual discrimination. We consider single (e.g.,gender) and multidimensional (e.g.,gender and race) discrimination testing.
  arXiv  Detail & Related papers  (2025-02-03T11:38:48Z)
• Evaluating the Fairness of Discriminative Foundation Models in Computer Vision [51.176061115977774]
  We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP) We then systematically evaluate existing methods for mitigating bias in these models with respect to our taxonomy. Specifically, we evaluate OpenAI's CLIP and OpenCLIP models for key applications, such as zero-shot classification, image retrieval and image captioning.
  arXiv  Detail & Related papers  (2023-10-18T10:32:39Z)
• The Flawed Foundations of Fair Machine Learning [0.0]
  We show that there is a trade-off between statistically accurate outcomes and group similar outcomes in any data setting where group disparities exist. We introduce a proof-of-concept evaluation to aid researchers and designers in understanding the relationship between statistically accurate outcomes and group similar outcomes.
  arXiv  Detail & Related papers  (2023-06-02T10:07:12Z)
• Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition [56.69875958980474]
  This work considers approaches to defending learned systems and how security defenses result in performance inequities across different sub-populations. We find that many methods that have been proposed can cause direct harm, like false rejection and unequal benefits from robustness training. We present a comparison of equality between two rejection-based defenses: randomized smoothing and neural rejection, finding randomized smoothing more equitable due to the sampling mechanism for minority groups.
  arXiv  Detail & Related papers  (2023-02-17T16:19:26Z)
• Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness under Unawareness setting [6.004889078682389]
  Current AI regulations require discarding sensitive features in the algorithm's decision-making process to prevent unfair outcomes. We propose a way to reveal the potential hidden bias of a machine learning model that can persist even when sensitive features are discarded.
  arXiv  Detail & Related papers  (2023-02-16T10:36:18Z)
• Towards Fair Classification against Poisoning Attacks [52.57443558122475]
  We study the poisoning scenario where the attacker can insert a small fraction of samples into training data. We propose a general and theoretically guaranteed framework which accommodates traditional defense methods to fair classification against poisoning attacks.
  arXiv  Detail & Related papers  (2022-10-18T00:49:58Z)
• Error Parity Fairness: Testing for Group Fairness in Regression Tasks [5.076419064097733]
  This work presents error parity as a regression fairness notion and introduces a testing methodology to assess group fairness. It is followed by a suitable permutation test to compare groups on several statistics to explore disparities and identify impacted groups. Overall, the proposed regression fairness testing methodology fills a gap in the fair machine learning literature and may serve as a part of larger accountability assessments and algorithm audits.
  arXiv  Detail & Related papers  (2022-08-16T17:47:20Z)
• Measuring Fairness of Text Classifiers via Prediction Sensitivity [63.56554964580627]
  ACCUMULATED PREDICTION SENSITIVITY measures fairness in machine learning models based on the model's prediction sensitivity to perturbations in input features. We show that the metric can be theoretically linked with a specific notion of group fairness (statistical parity) and individual fairness.
  arXiv  Detail & Related papers  (2022-03-16T15:00:33Z)
• Statistical discrimination in learning agents [64.78141757063142]
  Statistical discrimination emerges in agent policies as a function of both the bias in the training population and of agent architecture. We show that less discrimination emerges with agents that use recurrent neural networks, and when their training environment has less bias.
  arXiv  Detail & Related papers  (2021-10-21T18:28:57Z)
• Nested Counterfactual Identification from Arbitrary Surrogate Experiments [95.48089725859298]
  We study the identification of nested counterfactuals from an arbitrary combination of observations and experiments. Specifically, we prove the counterfactual unnesting theorem (CUT), which allows one to map arbitrary nested counterfactuals to unnested ones.
  arXiv  Detail & Related papers  (2021-07-07T12:51:04Z)
• On the Fairness of Causal Algorithmic Recourse [36.519629650529666]
  We propose two new fairness criteria at the group and individual level. We show that fairness of recourse is complementary to fairness of prediction. We discuss whether fairness violations in the data generating process revealed by our criteria may be better addressed by societal interventions.
  arXiv  Detail & Related papers  (2020-10-13T16:35:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.