Share this page:

Measuring Fairness of Text Classifiers via Prediction Sensitivity

Satyapriya Krishna, Rahul Gupta, Apurv Verma, Jwala Dhamala, Yada Pruksachatkun, and Kai-Wei Chang, in ACL, 2022.

Download the full text

Abstract

With the rapid growth in language processing applications, fairness has emerged as an important consideration in data-driven solutions. Although various fairness definitions have been explored in the recent literature, there is lack of consensus on which metrics most accurately reflect the fairness of a system. In this work, we propose a new formulation : ACCUMULATED PREDICTION SENSITIVITY, which measures fairness in machine learning models based on the model’s prediction sensitivity to perturbations in input features. The metric attempts to quantify the extent to which a single prediction depends on a protected attribute, where the protected attribute encodes the membership status of an individual in a protected group. We show that the metric can be theoretically linked with a specific notion of group fairness (statistical parity) and individual fairness. It also correlates well with humans’ perception of fairness. We conduct experiments on two text classification datasets : JIGSAW TOXICITY, and BIAS IN BIOS, and evaluate the correlations between metrics and manual annotations on whether the model produced a fair outcome. We observe that the proposed fairness metric based on prediction sensitivity is statistically significantly more correlated with human annotation than the existing counterfactual fairness metric.

Bib Entry

@inproceedings{krishna2022measuring,
  title = {Measuring Fairness of Text Classifiers via Prediction Sensitivity},
  author = {Krishna, Satyapriya and Gupta, Rahul and Verma, Apurv and Dhamala, Jwala and Pruksachatkun, Yada and Chang, Kai-Wei},
  booktitle = {ACL},
  year = {2022}
}

Related Publications

  1. Measuring Fairness of Text Classifiers via Prediction Sensitivity

    Satyapriya Krishna, Rahul Gupta, Apurv Verma, Jwala Dhamala, Yada Pruksachatkun, and Kai-Wei Chang, in ACL, 2022.
    Full Text Abstract BibTeX Details 
    With the rapid growth in language processing applications, fairness has emerged as an important consideration in data-driven solutions. Although various fairness definitions have been explored in the recent literature, there is lack of consensus on which metrics most accurately reflect the fairness of a system. In this work, we propose a new formulation : ACCUMULATED PREDICTION SENSITIVITY, which measures fairness in machine learning models based on the model’s prediction sensitivity to perturbations in input features. The metric attempts to quantify the extent to which a single prediction depends on a protected attribute, where the protected attribute encodes the membership status of an individual in a protected group. We show that the metric can be theoretically linked with a specific notion of group fairness (statistical parity) and individual fairness. It also correlates well with humans’ perception of fairness. We conduct experiments on two text classification datasets : JIGSAW TOXICITY, and BIAS IN BIOS, and evaluate the correlations between metrics and manual annotations on whether the model produced a fair outcome. We observe that the proposed fairness metric based on prediction sensitivity is statistically significantly more correlated with human annotation than the existing counterfactual fairness metric.
    @inproceedings{krishna2022measuring,
  title = {Measuring Fairness of Text Classifiers via Prediction Sensitivity},
  author = {Krishna, Satyapriya and Gupta, Rahul and Verma, Apurv and Dhamala, Jwala and Pruksachatkun, Yada and Chang, Kai-Wei},
  booktitle = {ACL},
  year = {2022}
}
    Details

  2. Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification

    Yada Pruksachatkun, Satyapriya Krishna, Jwala Dhamala, Rahul Gupta, and Kai-Wei Chang, in ACL-Finding, 2021.
    Full Text Code Abstract BibTeX Details 
    Existing bias mitigation methods to reduce disparities in model outcomes across cohorts have focused on data augmentation, debiasing model embeddings, or adding fairness-based optimization objectives during training. Separately, certified word substitution robustness methods have been developed to decrease the impact of spurious features and synonym substitutions on model predictions. While their end goals are different, they both aim to encourage models to make the same prediction for certain changes in the input. In this paper, we investigate the utility of certified word substitution robustness methods to improve equality of odds and equality of opportunity on multiple text classification tasks. We observe that certified robustness methods improve fairness, and using both robustness and bias mitigation methods in training results in an improvement in both fronts.
    @inproceedings{pruksachatkun2021robustness,
  title = {Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification},
  author = {Pruksachatkun, Yada and Krishna, Satyapriya and Dhamala, Jwala and Gupta, Rahul and Chang, Kai-Wei},
  booktitle = {ACL-Finding},
  year = {2021}
}
    Details

  3. LOGAN: Local Group Bias Detection by Clustering

    Jieyu Zhao and Kai-Wei Chang, in EMNLP (short), 2020.
    Full Text Code Abstract BibTeX Details 
    Machine learning techniques have been widely used in natural language processing (NLP). However, as revealed by many recent studies, machine learning models often inherit and amplify the societal biases in data. Various metrics have been proposed to quantify biases in model predictions. In particular, several of them evaluate disparity in model performance between protected groups and advantaged groups in the test corpus. However, we argue that evaluating bias at the corpus level is not enough for understanding how biases are embedded in a model. In fact, a model with similar aggregated performance between different groups on the entire data may behave differently on instances in a local region. To analyze and detect such local bias, 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 and allows us to better analyze the biases in model predictions.
    @inproceedings{zhao2020logan,
  author = {Zhao, Jieyu and Chang, Kai-Wei},
  title = {LOGAN: Local Group Bias Detection by Clustering},
  booktitle = {EMNLP (short)},
  presentation_id = {https://virtual.2020.emnlp.org/paper_main.2886.html},
  year = {2020}
}
    Details

  4. Towards Understanding Gender Bias in Relation Extraction

    Andrew Gaut, Tony Sun, Shirlyn Tang, Yuxin Huang, Jing Qian, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, and William Yang Wang, in ACL, 2020.
    Full Text Abstract BibTeX Details 
    Recent developments in Neural Relation Extraction (NRE) have made significant strides towards automated knowledge base construction. While much attention has been dedicated towards improvements in accuracy, there have been no attempts in the literature to evaluate social biases exhibited in NRE systems. In this paper, we create WikiGenderBias, a distantly supervised dataset composed of over 45,000 sentences including a 10% human annotated test set for the purpose of analyzing gender bias in relation extraction systems. We find that when extracting spouse and hypernym (i.e., occupation) relations, an NRE system performs differently when the gender of the target entity is different. However, such disparity does not appear when extracting relations such as birth date or birth place. We also analyze two existing bias mitigation techniques, word embedding debiasing and data augmentation. Unfortunately, due to NRE models relying heavily on surface level cues, we find that existing bias mitigation approaches have a negative effect on NRE. Our analysis lays groundwork for future quantifying and mitigating bias in relation extraction.
    @inproceedings{gaut2020towards,
  author = {Gaut, Andrew and Sun, Tony and Tang, Shirlyn and Huang, Yuxin and Qian, Jing and ElSherief, Mai and Zhao, Jieyu and Mirza, Diba and Belding, Elizabeth and Chang, Kai-Wei and Wang, William Yang},
  title = {Towards Understanding Gender Bias in Relation Extraction},
  booktitle = {ACL},
  year = {2020},
  presentation_id = {https://virtual.acl2020.org/paper_main.265.html}
}
    Details

  5. Mitigating Gender Bias Amplification in Distribution by Posterior Regularization

    Shengyu Jia, Tao Meng, Jieyu Zhao, and Kai-Wei Chang, in ACL (short), 2020.
    Full Text Slides Video Code Abstract BibTeX Details 
    Advanced machine  learning  techniques  have boosted  the  performance  of  natural  language processing.  Nevertheless, recent studies, e.g., Zhao et al. (2017) show that these techniques inadvertently capture the societal bias hiddenin the corpus and further amplify it.  However,their analysis is conducted only on models’ top predictions.   In this paper,  we investigate thegender  bias  amplification  issue  from  the  distribution perspective and demonstrate that thebias is amplified in the view of predicted probability distribution over labels. We further propose a bias mitigation approach based on posterior regularization.   With little performance loss,  our method can almost remove the bias amplification  in  the  distribution. Our study sheds the light on understanding the bias amplification.
    @inproceedings{jia2020mitigating,
  author = {Jia, Shengyu and Meng, Tao and Zhao, Jieyu and Chang, Kai-Wei},
  title = {Mitigating Gender Bias Amplification in Distribution by Posterior Regularization},
  booktitle = {ACL (short)},
  year = {2020},
  presentation_id = {https://virtual.acl2020.org/paper_main.264.html}
}
    Details

  6. Mitigating Gender in Natural Language Processing: Literature Review

    Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Kai-Wei Chang, and William Yang Wang, in ACL, 2019.
    Full Text Slides Video Abstract BibTeX Details 
    As Natural Language Processing (NLP) and Machine Learning (ML) tools rise in popularity, it becomes increasingly vital to recognize the role they play in shaping societal biases and stereotypes. Although NLP models have shown success in modeling various applications, they propagate and may even amplify gender bias found in text corpora. While the study of bias in artificial intelligence is not new, methods to mitigate gender bias in NLP are relatively nascent. In this paper, we review contemporary studies on recognizing and mitigating gender bias in NLP. We discuss gender bias based on four forms of representation bias and analyze methods recognizing gender bias. Furthermore, we discuss the advantages and drawbacks of existing gender debiasing methods. Finally, we discuss future studies for recognizing and mitigating gender bias in NLP.
    @inproceedings{sun2019mitigating,
  author = {Sun, Tony and Gaut, Andrew and Tang, Shirlyn and Huang, Yuxin and ElSherief, Mai and Zhao, Jieyu and Mirza, Diba and Chang, Kai-Wei and Wang, William Yang},
  title = {Mitigating Gender in Natural Language Processing: Literature Review},
  booktitle = {ACL},
  vimeo_id = {384482151},
  year = {2019}
}
    Details

  7. Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods

    Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang, in NAACL (short), 2018.
    Full Text Poster Code Abstract BibTeX Details Top-10 cited paper at NAACL 18 
    In this paper, we introduce a new benchmark for co-reference resolution focused on gender bias, WinoBias. Our corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a neural coreference system all link gendered pronouns to pro-stereotypical entities with higher accuracy than anti-stereotypical entities, by an average difference of 21.1 in F1 score. Finally, we demonstrate a data-augmentation approach that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by these systems in WinoBias without significantly affecting their performance on existing datasets.
    @inproceedings{zhao2018gender,
  author = {Zhao, Jieyu and Wang, Tianlu and Yatskar, Mark and Ordonez, Vicente and Chang, Kai-Wei},
  title = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods},
  booktitle = {NAACL (short)},
  press_url = {https://www.stitcher.com/podcast/matt-gardner/nlp-highlights/e/55861936},
  year = {2018}
}
    Details

  8. Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

    Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang, in EMNLP, 2017.
    Full Text Slides Code Abstract BibTeX Details EMNLP 2017 Best Long Paper Award; Top-10 cited paper at EMNLP 17 
    Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web. Structured prediction models are used in these tasks to take advantage of correlations between co-occuring labels and visual input but risk inadvertently encoding social biases found in web corpora.
In this work, we study data and models associated with multilabel object classification and visual semantic role labeling. We find that (a) datasets for these tasks contain significant gender bias and (b) models trained on these datasets further amplify existing bias. For example, the activity cooking is over 33% more likely to involve females than males in a training set, but a trained model amplifies the disparity to 68% at test time. We propose to inject corpus-level constraints for calibrating existing structured prediction models and design an algorithm based on Lagrangian relaxation for the resulting inference problems. Our method results in no performance loss for the underlying recognition task but decreases the magnitude of bias amplification by 33.3% and 44.9% for multilabel classification and visual semantic role labeling, respectively.
    @inproceedings{zhao2017men,
  author = {Zhao, Jieyu and Wang, Tianlu and Yatskar, Mark and Ordonez, Vicente and Chang, Kai-Wei},
  title = {Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints},
  booktitle = {EMNLP},
  year = {2017}
}
    Details