Measuring Fairness with Biased Rulers: A Comparative Study on Bias Metrics for Pre-trained Language Models (2022.naacl-main)
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| Challenge: | An increasing awareness of biased patterns in natural language processing resources such as BERT has motivated many metrics to quantify ‘bias’ and ‘fairness’. |
| Approach: | They combine literature survey, correlation analysis and empirical evaluations to evaluate compatibility of fairness metrics for pre-trained language models and their downstream tasks. |
| Outcome: | The proposed measures are not compatible with each other and highly depend on (i) templates, (ii) attribute and target seeds and (iv) the choice of embeddings. |
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On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations (2022.acl-short)
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Yang Trista Cao, Yada Pruksachatkun, Kai-Wei Chang, Rahul Gupta, Varun Kumar, Jwala Dhamala, Aram Galstyan
| Challenge: | Recent natural language processing systems use large language models as the backbone . however, societal biases are encoded in these models and transferred to downstream applications . |
| Approach: | They propose to use two categories to measure fairness in natural language processing tasks . they find intrinsic and extrinsic metrics do not correlate in their original setting . |
| Outcome: | The proposed metrics do not correlate in their original setting, the authors show . they find that they are not accurate when correcting for metric misalignments and noise . |
Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics (2021.tacl-1)
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| Challenge: | Existing fairness metrics quantify the differences in a model’s behaviour across a range of demographic groups. |
| Approach: | They propose to unify existing fairness metrics and compare them to three generalized fairness measures to reveal the connections between them. |
| Outcome: | The proposed measures can be explained by differences in parameter choices, and the results are consistent with previous studies. |
Bias and Fairness in Natural Language Processing (D19-2)
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| Challenge: | a tutorial will review the history of bias and fairness studies in machine learning and language processing . |
| Approach: | This tutorial reviews the history of bias and fairness studies in machine learning and language processing . it presents recent community effort to quantify and mitigat bias in natural language processing models . |
| Outcome: | This tutorial reviews the history of bias and fairness studies in machine learning and language processing . it aims to quantify and mitigate bias in natural language processing models for a wide spectrum of tasks . |
Your fairness may vary: Pretrained language model fairness in toxic text classification (2022.findings-acl)
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| Challenge: | Pre-trained, bidirectional language models have revolutionized natural language processing research . authors show that focusing on accuracy measures alone can lead to models with wide variation in fairness characteristics . |
| Approach: | They propose to use two post-processing methods to improve model fairness without retraining . they use pretrained language models of varying sizes on two toxic text classification tasks . |
| Outcome: | The proposed methods improve model fairness without retraining . the results show that the fairness variation is more than just accuracy . |
BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text Generation (2022.emnlp-main)
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| Challenge: | Currently, pre-trained language model (PLM) based metrics are widely adopted in text generation tasks. |
| Approach: | They propose to use PLMs to encode stereotypical societal biases in PLM-based metrics . they show that popular metrics exhibit higher social bias than traditional metrics based on 6 attributes . |
| Outcome: | The proposed method shows that PLM-based metrics exhibit higher social bias than traditional metrics on 6 attributes. |
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models (2023.acl-long)
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| Challenge: | Hundreds of studies have highlighted ethical issues in NLP models . |
| Approach: | They propose to measure media biases in LMs trained on diverse data sources . they focus on hate speech and misinformation detection . |
| Outcome: | The proposed methods quantify the fairness of downstream NLP models trained on politically biased LMs. |
Intrinsic Bias Metrics Do Not Correlate with Application Bias (2021.acl-long)
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| Challenge: | a recent survey of bias in natural language processing found that a coreference system makes more errors in an anti-stereotypical coreferent than in a pro-sterereotype one. |
| Approach: | They compare intrinsic and extrinsic bias metrics across hundreds of trained models . they urge researchers to focus on extrindic measures of bias, not easy to measure . |
| Outcome: | a new intrinsic metric and an annotated test set on gender bias in hate speech are tested . authors urge researchers to focus on extrinsic measures of bias, and to make them more feasible . |
StereoSet: Measuring stereotypical bias in pretrained language models (2021.acl-long)
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| Challenge: | Existing literature on stereotypical biases in language models is limited . current evaluations focus on measuring bias without considering language modeling ability . |
| Approach: | They propose to measure stereotypical biases in four domains: gender, profession, race, and religion . they compare stereotypical and language modeling ability of popular models like BERT, GPT-2, RoBERTa and XLnet . |
| Outcome: | The proposed model shows strong stereotypical biases in gender, profession, race, and religion domains. |
Mind Your Bias: A Critical Review of Bias Detection Methods for Contextual Language Models (2022.findings-emnlp)
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| Challenge: | Existing methods for detection of biases in contextual language models are inconsistent and inconclusive. |
| Approach: | They propose to use word embedding association test to detect biases in contextual language models to compare them with other methods. |
| Outcome: | The proposed methods are inconsistent and inconclusive for language models with word embeddings. |
Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling (2022.coling-1)
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| Challenge: | Existing studies have highlighted a variety of biases in pre-trained language models . however, these studies focus on fine-grained analysis of educational corpora and text that is not English . |
| Approach: | They analyze bias across text and through multiple architectures on a corpus of 9,165 German peer-reviews collected from university students over five years. |
| Outcome: | The proposed dataset shows that pre-trained language models exhibit conceptual, racial, and gender biases. |