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Handling Bias in Toxic Speech Detection: A Survey

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Mirages. On Anthropomorphism in Dialogue Systems

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Automated Emotion Recognition in the Workplace: How Proposed Technologies Reveal Potential Futures of Work

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Quantifying Bias in Automatic Speech RecognitionPreprint

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A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings

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Dataset for Identification of Homophobia and Transophobia in Multilingual YouTube CommentsPreprint

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An annotated dataset of literary entities

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Returning the N to NLP: Towards Contextually Personalized Classification Models

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The Dawn of the Human-Machine Era: A forecast of new and emerging language technologies

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"Honestly, I Think TikTok has a Vendetta Against Black Creators": Understanding Black Content Creator Experiences on TikTok

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Designing an Online Infrastructure for Collecting AI Data From People With Disabilities

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Eschewing Gender Stereotypes in Voice Assistants to Promote Inclusion

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Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle

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Controllable, frightening, or fun? Exploring the gendered dynamics of smart home technology preferences in the United Kingdom

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The Struggle for AI’s Recognition: Understanding the Normative Implications of Gender Bias in AI with Honneth’s Theory of Recognition

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Gender Representation in French Broadcast Corpora and Its Impact on ASR Performance

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Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions

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Data Representativeness in Accessibility Datasets: A Meta-Analysis

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Hey ASR System! Why Aren’t You More Inclusive?

Ngueajio, M. K., & Washington, G. J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21707-4_30 · Cited by 43 · see citing works

Decoding disparities: evaluating automatic speech recognition system performance in transcribing Black and White patient verbal communication with nurses in home healthcare

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Reducing sentiment polarity for demographic attributes in word embeddings using adversarial learning

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AI Consent Futures: A Case Study on Voice Data Collection with Clinicians

Wilcox, L. G., Brewer, R. N., & Díaz, F. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610107 · Cited by 37 · see citing works

Understanding the Representation and Representativeness of Age in AI Data Sets

Park, J., Bernstein, M. S., Brewer, R. N., Kamar, E., & Morris, M. (2021) · AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3461702.3462590 · Cited by 51 · see citing works

Fair Enough: Searching for Sufficient Measures of Fairness

Majumder, S., Chakraborty, J., Bai, G. R., Stolee, K. T., & Menzies, T. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3585006 · Cited by 36 · see citing works

From Plane Crashes to Algorithmic Harm: Applicability of Safety Engineering Frameworks for Responsible ML

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Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection Encoding

Tan, S., Joty, S., Varshney, L. R., & Kan, M. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.455 · Cited by 35 · see citing works

Personal Bias in Prediction of Emotions Elicited by Textual Opinions

Miłkowski, P., Gruza, M., Kanclerz, K., Kazienko, P., Grimling, D., & Kocoń, J. (2021) · doi:10.18653/v1/2021.acl-srw.26 · Cited by 34 · see citing works

Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning

Yin, D., Li, L. H., Hu, Z.-N., Peng, N., & Chang, K.-W. (2021) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.162 · Cited by 78 · see citing works

Mitigating Gender Bias in Natural Language Processing: Literature ReviewPreprint

Sun, T., Gaut, A., Tang, S., Huang, Y., ElSherief, M., Zhao, J., Mirza, D., Belding, E., Chang, K.-W., & Wang, W. Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.08976 · Cited by 109 · see citing works

Search-based fairness testing for regression-based machine learning systems

Perera, A., Aleti, A., Tantithamthavorn, C. K., Jiarpakdee, J., Turhan, B., Kühn, L., & Walker, K. (2022) · Empirical Software Engineering · doi:10.1007/s10664-022-10116-7 · Cited by 33 · see citing works

Linguistic justice as a framework for designing, developing, and managing natural language processing tools

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Intrinsic bias metrics do not correlate with application bias

Goldfarb-Tarrant, S., Marchant, R., Muñoz Sánchez, R., Pandya, M., & Lopez, A. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.150 · Cited by 33 · see citing works

Can Whisper Perform Speech-Based In-Context Learning?

Wang, S., Yang, C.-H. H., Wu, J. Y., & Zhang, C. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446502 · Cited by 32 · see citing works

Affordances and Constraints of Intelligent Personal Assistants for Second-Language Learning

Dizon, G. (2021) · RELC Journal · doi:10.1177/00336882211020548 · Cited by 32 · see citing works

Automatic Fairness Testing of Neural Classifiers Through Adversarial Sampling

Zhang, P., Wang, J., Sun, J., Wang, X., Dong, G., Wang, X., Dai, T., & Dong, J. S. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3101478 · Cited by 32 · see citing works

NLPositionality: Characterizing Design Biases of Datasets and Models

Santy, S., Liang, J. T., Bras, R. J. L., Reinecke, K., & Sap, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.505 · Cited by 45 · see citing works

Case Study: Deontological Ethics in NLP

Prabhumoye, S., Boldt, B., Salakhutdinov, R., & Black, A. W. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.297 · Cited by 32 · see citing works

Intersectional Bias in Hate Speech and Abusive Language DatasetsPreprint

Kim, J., Ortiz, C., Nam, S., Santiago, S., & Datta, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.05921 · Cited by 37 · see citing works

"Play PRBLMS": Identifying and Correcting Less Accessible Content in Voice Interfaces

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Augmented Datasheets for Speech Datasets and Ethical Decision-Making

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Assessing and Addressing Algorithmic Bias - But Before We Get TherePreprint

Garcia-Gathright, J. I., Springer, A., & Cramer, H. S. M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.03332 · Cited by 27 · see citing works

Responsible natural language processing: A principlist framework for social benefits

Behera, R. K., Bala, P. K., Rana, N. P., & Irani, Z. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.122306 · Cited by 26 · see citing works

How Might We Create Better Benchmarks for Speech Recognition?

Aksënova, A., van Esch, D., Flynn, J., & Golik, P. (2021) · Proceedings of the 1st Workshop on Benchmarking: Past, Present and Future · doi:10.18653/v1/2021.bppf-1.4 · Cited by 25 · see citing works

Multilingual hope speech detection in English and Dravidian languages

Chakravarthi, B. R. (2022) · International Journal of Data Science and Analytics · doi:10.1007/s41060-022-00341-0 · Cited by 25 · see citing works

Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective

Castaneda, J., Jover-Leal, A., Calvet, L., Torrado, S. Y., Juan, A. A., & Sáinz, M. (2022) · Algorithms · doi:10.3390/a15090303 · Cited by 25 · see citing works

Ethics in Linguistics

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Evaluating the Performance of State-of-the-Art ASR Systems on Non-Native English using Corpora with Extensive Language Background Variation

Hollands, S., Blackburn, D. J., & Christensen, H. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10433 · Cited by 24 · see citing works

Artificial Intelligence in Digital Marketing: Towards an Analytical Framework for Revealing and Mitigating Bias

Reed, C., Wynn, M. G., & Bown, R. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9020040 · Cited by 39 · see citing works

ALGORITHMIC BIAS IN AI-ENHANCED EDUCATION: CULTURAL DIMENSIONS AND PEDAGOGICAL IMPACT

Fadil, H. O. C. A., & Nuredin, A. (2025) · Symposium Proceedings · doi:10.55843/isl2025symp163h · Cited by 25 · see citing works

Fine-tuning neural machine translation on gender-balanced datasets

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Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML

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Effects of Talker Dialect, Gender & Race on Accuracy of Bing Speech and YouTube Automatic Captions

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Modeling Gender and Dialect Bias in Automatic Speech Recognition

Harris, C., Mgbahurike, C., Kumari, N., & Yang, D. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.890 · Cited by 20 · see citing works

Fair or Fare? Understanding Automated Transcription Error Bias in Social Media and Videoconferencing Platforms

Dubois, D. J., Holliday, N. R., Waddell, K., & Choffnes, D. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31320 · Cited by 2 · see citing works

Automatic speech recognition: system variability within a sociolinguistically homogeneous group of speakers

Harrington, L., & Hughes, V. (2023) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · Cited by 1 · see citing works

An evaluation of automatic speech recognition in the Spanish version of windows 7: effects of language variety, speaking style and gender

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