Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifold (2022.findings-acl)
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| Challenge: | a prototypical NLP experiment trains a standard architecture on labeled English data . a recent study shows that research often goes beyond the square one setup . |
| Approach: | They argue that the prototypical NLP experiment trains a standard architecture on labeled English data and optimizes for accuracy without accounting for other dimensions such as fairness, interpretability, or computational efficiency. |
| Outcome: | The proposed model steers and biases the research dynamics in the NLP community, the authors argue . they show that the prototype biased recent NLP research on English data is true . |
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| Challenge: | Modern NLP systems exhibit a range of biases, which a growing literature on model debiasing attempts to correct. |
| Approach: | They propose to clarify the current situation and plot a course for meaningful progress in fair learning by making clear inter-relations among the current gamut of methods and their relation to fairness theory. |
| Outcome: | The proposed approach addresses the practical problem of model selection, which involves a trade-off between fairness and accuracy and has led to systemic issues in fairness research. |
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 . |
Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020.acl-main)
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| Challenge: | 146 papers analyzing "bias" in NLP systems lack normative reasoning, we find . authors propose three recommendations for work analyzing “bias” in Nlp systems . |
| Approach: | They propose three recommendations for analyzing "bias" in NLP systems . they propose to focus on what kinds of system behaviors are harmful, in what ways, to whom, and why . |
| Outcome: | The proposed methods for measuring or mitigating “bias” are poorly matched to their motivations and do not engage critically with literature outside of NLP. |
Cognitive Effects and Biases in Large Language Models (2026.eacl-tutorials)
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| Challenge: | This tutorial bridges psychology and NLP to clarify cognitive effects and biases in large language models. |
| Approach: | This tutorial bridges psychology and NLP to clarify cognitive effects and biases in large language models. |
| Outcome: | This tutorial bridges psychology and NLP to clarify cognitive effects and biases in large language models. |
Re-contextualizing Fairness in NLP: The Case of India (2022.aacl-main)
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| Challenge: | Recent research has revealed undesirable biases in NLP data and models . however, these efforts focus of social disparities in the West and are not directly portable to other geo-cultural contexts. |
| Approach: | They propose a framework to re-contextualize NLP fairness research for the Indian context . they build resources for fairness evaluation in the Indian and delve deeper into social stereotypes for Region and Religion . |
| Outcome: | The proposed framework can be generalized to other geo-cultural contexts. |
Should We Ban English NLP for a Year? (2022.emnlp-main)
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| Challenge: | aaron carroll: two thirds of NLP research is devoted to developing technology for speakers of English . carroll says this bias feeds into consumer technologies to widen existing inequality gaps . he says we need to consider more concrete measures to mitigate climate change . |
| Approach: | a new paper argues that NLP is contributing to global inequalities through a digital language divide . a carbon tax, cap-and-trade and car-free Sundays are examples of measures to mitigate climate change . |
| Outcome: | a new paper argues that NLP is contributing to global inequalities through a digital language divide . a carbon tax, cap-and-trade and car-free Sundays are examples of measures to mitigate climate change . |
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. |
NLP Needs Diversity outside of ‘Diversity’ (2025.findings-emnlp)
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| Challenge: | a new position paper argues that diversity in NLP is concentrated on a small number of areas surrounding fairness . |
| Approach: | a new position paper argues that diversity in NLP is disproportionately concentrated on fairness areas. |
| Outcome: | a new position paper argues that diversity in NLP is disproportionately concentrated on fairness areas. |
Not All Claims are Created Equal: Choosing the Right Statistical Approach to Assess Hypotheses (2020.acl-main)
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| Challenge: | Empirical research in natural language processing has adopted a narrow set of principles for assessing hypotheses . alternative approaches to assess hypothese rely on p-value computation, which suffers from several known issues. |
| Approach: | They propose to compare different methods for assessing hypotheses . they argue that practitioners should first decide their target hypothesis before choosing a method . |
| Outcome: | The proposed method differs from other methods, but is not widely used in NLP . the proposed method is based on a p-value computation, but has a small gap in accuracy . |
Benchmarking Intersectional Biases in NLP (2022.naacl-main)
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| Challenge: | Recent work on fairness of machine learning models has focused on how to debias, but research on the fairness and performance of biased/debiased models on downstream prediction tasks has been limited. |
| Approach: | They assess intersectional bias - fairness across multiple demographic dimensions . they highlight possible causes and make recommendations for future NLP debiasing research. |
| Outcome: | The proposed approaches fare well in terms of fairness-accuracy trade-off, but are unable to effectively alleviate bias in downstream tasks. |