Evaluating Debiasing Techniques for Intersectional Biases

• URL: http://arxiv.org/abs/2109.10441v1
• Date: Tue, 21 Sep 2021 22:01:28 GMT
• Title: Evaluating Debiasing Techniques for Intersectional Biases
• Authors: Shivashankar Subramanian, Xudong Han, Timothy Baldwin, Trevor Cohn, Lea Frermann
• Abstract summary: bias is pervasive in NLP models, motivating the development of automatic debiasing techniques. In this paper we argue that a truly fair model must consider gerrymandering' groups which comprise not only single attributes, but also intersectional groups.
• Score: 53.41549919978481
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bias is pervasive in NLP models, motivating the development of automatic debiasing techniques. Evaluation of NLP debiasing methods has largely been limited to binary attributes in isolation, e.g., debiasing with respect to binary gender or race, however many corpora involve multiple such attributes, possibly with higher cardinality. In this paper we argue that a truly fair model must consider `gerrymandering' groups which comprise not only single attributes, but also intersectional groups. We evaluate a form of bias-constrained model which is new to NLP, as well an extension of the iterative nullspace projection technique which can handle multiple protected attributes.

Related papers

• Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization [13.773597081543185]
  We introduce a novel debiasing regularization technique based on the class-wise variance of embeddings. Our method does not require attribute labels and targets any attribute, thus addressing the shortcomings of existing debiasing methods.
  arXiv  Detail & Related papers  (2024-09-29T03:56:50Z)
• Projective Methods for Mitigating Gender Bias in Pre-trained Language Models [10.418595661963062]
  Projective methods are fast to implement, use a small number of saved parameters, and make no updates to the existing model parameters. We find that projective methods can be effective at both intrinsic bias and downstream bias mitigation, but that the two outcomes are not necessarily correlated.
  arXiv  Detail & Related papers  (2024-03-27T17:49:31Z)
• 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)
• Counter-GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns [53.62845317039185]
  Bias-measuring datasets play a critical role in detecting biased behavior of language models. We propose a novel method to collect diverse, natural, and minimally distant text pairs via counterfactual generation. We show that four pre-trained language models are significantly more inconsistent across different gender groups than within each group.
  arXiv  Detail & Related papers  (2023-02-11T12:11:03Z)
• xFAIR: Better Fairness via Model-based Rebalancing of Protected Attributes [15.525314212209564]
  Machine learning software can generate models that inappropriately discriminate against specific protected social groups. We propose xFAIR, a model-based extrapolation method, that is capable of both mitigating bias and explaining the cause.
  arXiv  Detail & Related papers  (2021-10-03T22:10:14Z)
• Contrastive Learning for Fair Representations [50.95604482330149]
  Trained classification models can unintentionally lead to biased representations and predictions. Existing debiasing methods for classification models, such as adversarial training, are often expensive to train and difficult to optimise. We propose a method for mitigating bias by incorporating contrastive learning, in which instances sharing the same class label are encouraged to have similar representations.
  arXiv  Detail & Related papers  (2021-09-22T10:47:51Z)
• A Generative Approach for Mitigating Structural Biases in Natural Language Inference [24.44419010439227]
  In this work, we reformulate the NLI task as a generative task, where a model is conditioned on the biased subset of the input and the label. We show that this approach is highly robust to large amounts of bias. We find that generative models are difficult to train and they generally perform worse than discriminative baselines.
  arXiv  Detail & Related papers  (2021-08-31T17:59:45Z)
• LOGAN: Local Group Bias Detection by Clustering [86.38331353310114]
  We argue that evaluating bias at the corpus level is not enough for understanding how biases are embedded in a model. We propose LOGAN, a new bias detection technique based on clustering. Experiments on toxicity classification and object classification tasks show that LOGAN identifies bias in a local region.
  arXiv  Detail & Related papers  (2020-10-06T16:42:51Z)
• Towards Robustifying NLI Models Against Lexical Dataset Biases [94.79704960296108]
  This paper explores both data-level and model-level debiasing methods to robustify models against lexical dataset biases. First, we debias the dataset through data augmentation and enhancement, but show that the model bias cannot be fully removed via this method. The second approach employs a bag-of-words sub-model to capture the features that are likely to exploit the bias and prevents the original model from learning these biased features.
  arXiv  Detail & Related papers  (2020-05-10T17:56:10Z)

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.