The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Gender and Dialect Bias in YouTube’s Automatic Captions
Open accessAbstract
This project evaluates the accuracy of YouTube's automatically-generated captions across two genders and five dialects of English.Speakers' dialect and gender was controlled for by using videos uploaded as part of the "accent tag challenge", where speakers explicitly identify their language background.The results show robust differences in accuracy across both gender and dialect, with lower accuracy for 1) women and 2) speakers from Scotland.This finding builds on earlier research finding that speaker's sociolinguistic identity may negatively impact their ability to use automatic speech recognition, and demonstrates the need for sociolinguistically-stratified validation of systems.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“These models can perform well on average but poorly on some users (Tatman, 2017; Caldas et al., 2018; Li et al., 2019b; Koenecke et al., 2020).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Tatman, 2017
In the citing paper
“Performance disparities across individuals have been observed in a wide range of tasks and applications, including in natural language processing (Geva et al., 2019), automatic speech recognition (Koenecke et al., 2020; Tatman, 2017), federated learning (Li et al., 2019b; Caldas et al., 2018), and medical imaging (Badgeley et al., 2019).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Tatman, 2017
In the citing paper
“These models can perform well on average but poorly on some individuals (Tatman, 2017; Caldas et al., 2018; Li et al., 2019b; Koenecke et al., 2020).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Tatman, 2017
Citations
Cites
“Librispeech: an ASR corpus based on public domain audio books,”
Panayotov, V., Chen, G., Povey, D., & Khudanpur, S. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2015.7178964 · Cited by 6,072
Praat, a system for doing phonetics by computer
Boersma, P. (2002) · UvA-DARE (University of Amsterdam) · Cited by 4,417
TIMIT Acoustic-Phonetic Continuous Speech Corpus
Garofolo, J. S., Lamel, L. F., Fisher, W. M., Pallett, D. S., Dahlgren, N. L., Zue, V. W., & Fiscus, J. G. (2020) · Americanae (AECID Library) · doi:10.35111/17gk-bn40 · Cited by 2,509
Torralba, A., & Efros, A. A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995347 · Cited by 2,440
SWITCHBOARD: telephone speech corpus for research and development
Godfrey, J. J., Holliman, E. C., & McDaniel, J. (1992) · [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1992.225858 · Cited by 2,134 · cited 1×
“Switchboard (Godfrey et al., 1992) undersamples women, Southerns and non-college-educated speakers.”
BackgroundSex, covert prestige and linguistic change in the urban British English of Norwich
TRUDGILL, P. W. (1972) · Language in Society · doi:10.1017/s0047404500000488 · Cited by 1,515
The whole woman: Sex and gender differences in variation
Eckert, P. (1989) · Language Variation and Change · doi:10.1017/s095439450000017x · Cited by 1,021
Rapid adaptation to foreign-accented English
Clarke, C. M., & Garrett, M. F. (2004) · The Journal of the Acoustical Society of America · doi:10.1121/1.1815131 · Cited by 626
The effect of experience on the perception and representation of dialect variants
Sumner, M., & Samuel, A. G. (2009) · Journal of Memory and Language · doi:10.1016/j.jml.2009.01.001 · Cited by 364
The social impact of natural language processing
Hovy, D., & Spruit, S. L. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p16-2096 · Cited by 329
Flagged first, then by citations
Cited by
Sorted by citations
Items 101–200 of 338
Unacceptable, where is my privacy? Exploring Accidental Triggers of Smart SpeakersPreprint
Schönherr, L., Golla, M., Eisenhofer, T., Wiele, J., Kolossa, D., & Holz, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.00508 · Cited by 24
Kafle, S., Glasser, A., Al-khazraji, S., Berke, L., Seita, M., & Huenerfauth, M. (2020) · ACM SIGACCESS Accessibility and Computing · doi:10.1145/3386296.3386300 · Cited by 22
AID: An Automated Detector for Gender-Inclusivity Bugs in OSS Project Pages
Chatterjee, A., Guizani, M., Stevens, C., Emard, J., May, M. E., Burnett, M., & Ahmed, I. (2021) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse43902.2021.00128 · Cited by 22
Bingley, W., Haslam, S. A., Steffens, N. K., Gillespie, N. A., Worthy, P., Curtis, C., Lockey, S., Bialkowski, A., Ko, R. K. L., & Wiles, J. (2023) · New Ideas in Psychology · doi:10.1016/j.newideapsych.2023.101025 · Cited by 31
What’s This? A Voice and Touch Multimodal Approach for Ambiguity Resolution in Voice Assistants
Lee, J., Rodríguez, S., Natarrajan, R., Chen, J. M., Deep, H., & Kirlik, A. (2021) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3462244.3479902 · Cited by 21
Utilizing subjectivity level to mitigate identity term bias in toxic comments classification
Zhao, Z., Zhang, Z., & Hopfgartner, F. (2022) · Online Social Networks and Media · doi:10.1016/j.osnem.2022.100205 · Cited by 26
Choi, D., Lee, U., & Hong, H. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517574 · Cited by 23
Onitilo, A. A., Shour, A. R., Puthoff, D. S., Tanimu, Y., Joseph, A., & Sheehan, M. T. (2023) · PLOS ONE · doi:10.1371/journal.pone.0272545 · Cited by 21
Emotion Impacts Speech Recognition Performance
Munot, R., & Nenkova, A. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop (NAACL SRW) · doi:10.18653/v1/n19-3003 · Cited by 21
Investigating the Impact of Gender Representation in ASR Training Data: a Case Study on Librispeech
Garnerin, M., Rossato, S., & Besacier, L. (2021) · Workshop on Gender Bias in Natural Language Processing (GeBNLP) · doi:10.18653/v1/2021.gebnlp-1.10 · Cited by 21
Cultural Considerations in AI Systems for the Global South: A Systematic Review
Anuyah, O., Wan, R., Adejoro, C. O., YEH, T. P., Metoyer, R., & Badillo-Urquiola, K. A. (2023) · African Human Computer Interaction Conference (AfriCHI) · doi:10.1145/3628096.3629046 · Cited by 25
Parviz, M. (2024) · Estudios Ingleses de la Universidad Complutense · doi:10.5209/cjes.97261 · Cited by 29
Johnson, B., & Brun, Y. (2022) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3510454.3516830 · Cited by 20
Selective Classification Can Magnify Disparities Across GroupsPreprint
Jones, E., Sagawa, S., Koh, P. W., Kumar, A., & Liang, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.14134 · Cited by 34
Transforming Speech-Language Pathology with AI: Opportunities, Challenges, and Ethical Guidelines
Georgiou, G. P. (2025) · Healthcare · doi:10.3390/healthcare13192460 · Cited by 25
A Comprehensive Study on Bias in Artificial Intelligence Systems
Kartal, E. (2022) · International Journal of Intelligent Information Technologies · doi:10.4018/ijiit.309582 · Cited by 27
Identity and Status: When Counterspeech Increases Hate Speech Reporting and Why
Kim, J. Y., Sim, J., & Cho, D. (2022) · Information Systems Frontiers · doi:10.1007/s10796-021-10229-2 · Cited by 19
Contributing to Accessibility Datasets: Reflections on Sharing Study Data by Blind People
Kamikubo, R., Lee, K., & Kacorri, H. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581337 · Cited by 21
Ten Simple Rules for organizing a non–real-time web conference
Arnal, A., Epifanio, I., Gregori, P., & Martínez, V. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007667 · Cited by 16
Hutiri, W. T., Ding, A. Y., Kawsar, F., & Mathur, A. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3591867 · Cited by 18
Pande, A., & Mishra, D. (2024) · Behaviour and Information Technology · doi:10.1080/0144929x.2024.2344726 · Cited by 19
Nacimiento-García, E., Díaz-Kaas-Nielsen, H. S., & González, C. S. G. (2024) · Applied Sciences · doi:10.3390/app14114734 · Cited by 15
Read, G. J. M., Revell, K. M. A., Parnell, K. J., Lockton, D., & Salmon, P. M. (2021) · Applied Ergonomics · doi:10.1016/j.apergo.2021.103634 · Cited by 15
Coats, S. (2023) · De Gruyter eBooks · doi:10.1515/9783111017433-005 · Cited by 14
Agreement and disagreement between major emotion recognition systems
Vogel, C. M., & Ahmad, K. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110759 · Cited by 14
Censorship of Online Encyclopedias
Yang, E., & Roberts, M. E. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445916 · Cited by 20
Modeling Language Usage and Listener Engagement in Podcasts
Reddy, S., Lazarova, M., Yu, Y., & Jones, R. (2021) · doi:10.18653/v1/2021.acl-long.52 · Cited by 16
Model-Based Approach for Measuring the Fairness in ASR
Liu, Z., Veliche, I.-E., & Peng, F.-C. (2021) · IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp43922.2022.9747654 · Cited by 30
A LEGAL FRAMEWORK FOR ARTIFICIAL INTELLIGENCE FAIRNESS REPORTING
Yap, J. Q., & Lim, E. W. K. (2022) · The Cambridge Law Journal · doi:10.1017/s0008197322000460 · Cited by 20
Articulation Rate in American English in a Corpus of YouTube Videos
Coats, S. (2019) · Language and Speech · doi:10.1177/0023830919894720 · Cited by 14
Rissanen Data Analysis: Examining Dataset Characteristics via Description LengthPreprint
Perez, E., Kiela, D., & Cho, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.03872 · Cited by 13
Gender Differences and Bias in Artificial Intelligence
Franzoni, V. (2023) · Intelligent systems reference library · doi:10.1007/978-3-031-21606-0_2 · Cited by 13
Representational ethical model calibration
Carruthers, R., Straw, I., Ruffle, J. K., Herron, D., Nelson, A., Bzdok, D., Fernández-Reyes, D., Rees, G. E., & Nachev, P. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00716-4 · Cited by 15
Design and Evaluation of Voice User Interfaces: What Should One Consider?
Klein, A. M., Kölln, K., Deutschländer, J., & Rauschenberger, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35921-7_12 · Cited by 13
Improving humanitarian needs assessments through natural language processing
Kreutzer, T., Vinck, P., Pham, P. N., An, A., Appel, L., DeLuca, E., Tang, G., Alzghool, M., Hachhethu, K., Morris, B., et al. (2019) · IBM Journal of Research and Development · doi:10.1147/jrd.2019.2947014 · Cited by 13
Coto‐Solano, R., Stanford, J. N., & Reddy, S. K. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.662097 · Cited by 12
A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems
Boito, M. Z., Besacier, L., Tomashenko, N., & Estève, Y. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-353 · Cited by 23
Evaluating Gender Bias in Speech Translation
Costa‐jussà, M. R., Basta, C., & Gállego, G. I. (2022) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/586hoxzn9ig8 · Cited by 12
Breeding Gender-aware Direct Speech Translation Systems
Gaido, M., Savoldi, B., Bentivogli, L., Negri, M., & Turchi, M. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.350 · Cited by 18
Progressive Disclosure: Designing for Effective TransparencyPreprint
Springer, A., & Whittaker, S. (2018) · arXiv.org · doi:10.48550/arxiv.1811.02164 · Cited by 24
Examining Racial Bias in an Online Abuse Corpus with Structural Topic ModelingPreprint
Davidson, T., & Bhattacharya, D. (2020) · arXiv.org · doi:10.48550/arxiv.2005.13041 · Cited by 17
Bias and stereotyping: Human and artificial intelligence (AI)
Kang, O., & Hirschi, K. (2025) · Annual Review of Applied Linguistics · doi:10.1017/s026719052500008x · Cited by 15
Seamless equal accuracy ratio for inclusive CTC speech recognition
Gao, H., Wang, X., Kang, S., Mina, R. J. L., Issa, D., Harvill, J. B., Sarı, L., Hasegawa‐Johnson, M., & Yoo, C. D. (2021) · Speech Communication · doi:10.1016/j.specom.2021.11.004 · Cited by 11
Harrington, L. (2023) · Frontiers in Communication · doi:10.3389/fcomm.2023.1165233 · Cited by 12
Exploring Data Augmentation in Bias Mitigation Against Non-Native-Accented Speech
Zhang, Y., Herygers, A., Patel, T. B., Yue, Z., & Scharenborg, O. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389756 · Cited by 10
Habler, F., Peisker, M., & Henze, N. (2019) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/3365610.3365627 · Cited by 10
Assessing pronunciation using dictation tools
Johnson, C., Cardoso, W., Zuercher, B., Brannen, K., & Springer, S. (2024) · Journal of Second Language Pronunciation · doi:10.1075/jslp.23033.joh · Cited by 9
Nabende, J. N., Kagumire, S., Kantono, C., & Nabende, P. (2025) · ACM Computing Surveys · doi:10.1145/3769089 · Cited by 9
Latent Space Smoothing for Individually Fair Representations
Peychev, M., Ruoss, A., Balunović, M., Baader, M., & Vechev, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19778-9_31 · Cited by 11
Chakraborty, J., Majumder, S., & Tu, H. (2022) · doi:10.1145/3524491.3527305 · Cited by 9
Dialect Diversity in Text Summarization on Twitter
Keswani, V., & Celis, L. E. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3450108 · Cited by 13
Exploring accidental triggers of smart speakers
Schönherr, L., Golla, M., Eisenhofer, T., Wiele, J., Kolossa, D., & Holz, T. (2021) · Computer Speech & Language · doi:10.1016/j.csl.2021.101328 · Cited by 46
Analyzing Hate Speech Data along Racial, Gender and Intersectional Axes
Maronikolakis, A., Baader, P., & Schütze, H. (2022) · Workshop on Gender Bias in Natural Language Processing (GeBNLP) · doi:10.18653/v1/2022.gebnlp-1.1 · Cited by 9
Sexismo no Brasil: análise de um Word Embedding por meio de testes baseados em associação implícita
Taso, F. T. D. S., dos Reis, V. Q., & Martinez, F. V. (2023) · Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL) · doi:10.5753/stil.2023.233845 · Cited by 8
Fucci, D., Gaido, M., Negri, M., Cettolo, M., & Bentivogli, L. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389767 · Cited by 8
Ezema, K., Chandler, C., Southwell, R. V., Cholendiran, N., & D’Mello, S. K. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714059 · Cited by 10
Algorithmic Fairness in Automatic Short Answer Scoring
Andersen, N., Mang, J., Goldhammer, F., & Zehner, F. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-025-00495-5 · Cited by 17
Technological effects on gender studies: An intersectional perspective
Işıklı, Ş., & FAZLIOĞLU, E. F. (2026) · Feminist Encounters A Journal of Critical Studies in Culture and Politics · doi:10.20897/femenc/17998 · Cited by 8
The identification of YouTube videos that feature the linguistic features of English informal speech
Cooper, C. R. (2023) · Applied Corpus Linguistics · doi:10.1016/j.acorp.2023.100068 · Cited by 8
Bigger Isn’t Better: The Ethical and Scientific Vices of Extra-Large Datasets in Language Models
Goetze, T. S., & Abramson, D. (2021) · ACM Web Science Conference (WebSci) · doi:10.1145/3462741.3466809 · Cited by 8
Augmenting Semantic Lexicons Using Word Embeddings and Transfer Learning
Alshaabi, T., Oort, C. M. V., Fudolig, M. I., Arnold, M., Danforth, C. M., & Dodds, P. S. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.783778 · Cited by 11
Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia
Ibraheem, S., Zhou, G., & DeNero, J. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.11 · Cited by 13
Regional bias in monolingual English language models
Lyu, J., Dost, K., Koh, Y. S., & Wicker, J. S. (2024) · Machine Learning · doi:10.1007/s10994-024-06555-6 · Cited by 8
Listen and Speak Fairly: a Study on Semantic Gender Bias in Speech Integrated Large Language Models
Lin, Y., Lin, T.-Q., Yang, C.-K., Lu, K.-H., Chen, W.-C., Kuan, C.-Y., & Lee, H.-Y. (2024) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt61566.2024.10832317 · Cited by 9
Rewriting Algorithms for Just Recognition: From Digital Aural Redlining to Accent Activism
Eidsheim, N. S. (2023) · University of California Press eBooks · doi:10.1525/luminos.148.h · Cited by 7
Johnson, B., Bartola, J., Angell, R., Witty, S., Giguere, S., & Brun, Y. (2023) · EURO Journal on Decision Processes · doi:10.1016/j.ejdp.2023.100031 · Cited by 18
Slaughter, I., Greenberg, C. S., Schwartz, R., & Caliskan, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.602 · Cited by 12
A One-Shot Framework for Distributed Clustered Learning in Heterogeneous Environments
Armacki, A., Bajović, D., Jakovetić, D., & Kar, S. (2023) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2023.3343561 · Cited by 9
Assessment of Pepper Robot’s Speech Recognition System through the Lens of Machine Learning
Pande, A., & Mishra, D. (2024) · Biomimetics · doi:10.3390/biomimetics9070391 · Cited by 7
Automatic Speech Recognition in Diverse English Accents
Mohyuddin, H., & Kwak, D. (2023) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci62032.2023.00122 · Cited by 7
Towards Inclusive Software Engineering Through A/B Testing: A Case-Study at Windows
Niculescu, I., Hu, H. M., Gee, C., Chong, C., Dubey, S., & Li, P. L. (2021) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip52600.2021.00027 · Cited by 7
Text Analysis in Python for Social Scientists
Hovy, D. (2022) · Cambridge University Press eBooks · doi:10.1017/9781108960885 · Cited by 8
Indigenous Language Revitalization and the Dilemma of Gender Bias
Hansal, O., Le, N. T., & Sadat, F. (2022) · Workshop on Gender Bias in Natural Language Processing (GeBNLP) · doi:10.18653/v1/2022.gebnlp-1.25 · Cited by 7
Computational sociophonetics using automatic speech recognition
Coto‐Solano, R. (2022) · Language and Linguistics Compass · doi:10.1111/lnc3.12474 · Cited by 8
Towards Making Virtual Human-Robot Interaction a Reality
Higgins, P., Kebe, G. Y., Berlier, A. J., Darvish, K., Engel, D., Ferraro, F., & Matuszek, C. (2021) · Maryland Shared Open Access Repository (USMAI Consortium) · doi:10.13016/m2lhch-cuzp · Cited by 7
Johnson, B., Bartola, J., Angell, R., Keith, K. A., Witty, S., Giguere, S., & Brun, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.09951 · Cited by 7
Inclusive Recommender Systems: Addressing Needs of Users with ADHD
Khan, S., Iqbal, M., Knijnenburg, B. P., & Dixon, E. (2025) · ACM Transactions on Recommender Systems · doi:10.1145/3769427 · Cited by 9
Ma, M., Glass, L., & Stanford, J. N. (2024) · Linguistics Vanguard · doi:10.1515/lingvan-2023-0073 · Cited by 6
Making Transparency Clear: The Dual Importance of Explainability and Auditability.
Springer, A., & Whittaker, S. (2019) · IUI Workshops · Cited by 5
Markl, N., & Lai, C. (2021) · Cited by 6
Assessing Gender Bias in Wikipedia: Inequalities in Article Titles
Faleńska, A., & Çetinoğlu, Ö. (2021) · Workshop on Gender Bias in Natural Language Processing (GeBNLP) · doi:10.18653/v1/2021.gebnlp-1.9 · Cited by 6
I Dream of Siri: Magic and Female Voice Assistants
Toncic, J. (2021) · Catalyst Feminism Theory Technoscience · doi:10.28968/cftt.v7i2.34906 · Cited by 6
Ethics consideration sections in natural language processing papers
Benotti, L., & Blackburn, P. R. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.299 · Cited by 6
A Corpus of Regional American Language from YouTube
Coats, S. (2019) · Digital Humanities in the Nordic and Baltic Countries Publications · doi:10.5617/dhnbpub.11083 · Cited by 6
Sex and gender bias in natural language processing
Cirillo, D., Gonen, H., Santus, E., Valencia, A., Costa‐jussà, M. R., & Villegas, M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-821392-6.00009-1 · Cited by 8
Mitigation of Unintended Biases against Non-Native English Texts in Sentiment Analysis.
Zhiltsova, A., Caton, S. J., & Mulway, C. (2019) · IASC · Cited by 7
Robust Causal Inference Under Covariate Shift via Worst-Case Subpopulation Treatment EffectsPreprint
Jeong, S., & Namkoong, H. (2020) · arXiv (Cornell University) · Cited by 6
Gender bias in natural language processing
Basta, C. (2022) · UPCommons institutional repository (Universitat Politècnica de Catalunya) · doi:10.5821/dissertation-2117-379361 · Cited by 6
Dong, X., Zhu, Z., Wang, Z., Teleki, M., & Caverlee, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.390 · Cited by 14
Bias in Federated Learning: Factors, Effects, Mitigations, and Open Issues
Benmalek, M., & Seddiki, A. (2024) · Ingénierie des systèmes d information · doi:10.18280/isi.290605 · Cited by 6
GPT-4-Trinis: assessing GPT-4’s communicative competence in the English-speaking majority world
Jackson, S., Beekhuizen, B. F., Zhao, Z., & McEwen, R. N. (2024) · AI & Society · doi:10.1007/s00146-024-01945-9 · Cited by 5
Fairness Certification for Natural Language Processing and Large Language Models
Freiberger, V., & Buchmann, E. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-66329-1_39 · Cited by 8
The Challenges of Designing a Robot for a Satisfaction Survey: Surveying Humans Using a Social Robot
Heath, S., Liddle, J., & Wiles, J. (2019) · International Journal of Social Robotics · doi:10.1007/s12369-019-00604-0 · Cited by 5
Li, P. L., Chai, X., Campbell, F., Liao, J., Abburu, N., Kang, M., Niculescu, I., Brake, G., Patil, S., Dooley, J., et al. (2021) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip52600.2021.00026 · Cited by 5
Metadata
- Authors
- Tatman, R.
- Affiliations
- University of Washington (US)
- Corresponding author
- Tatman, Rachael
- Venue
- Proceedings of the First ACL Workshop on Ethics in Natural Language Processing
- Published
- 2017
- Pages
- 53-59
- OpenAlex
- W2607719644
- Cited by
- 338
- References
- 36
- Citations by year
- 2026: 34 · 2025: 33 · 2024: 35 · 2023: 50 · 2022: 56 · 2021: 62 · 2020: 33 · 2019: 21 · 2018: 11 · 2017: 3
- FWCI
- 8.48
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Funders
- National Science Foundation (1256082)
- University of Washington
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Social Sciences › Linguistics and Language › Linguistic Variation and Morphology
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- speech recognition, speech recognition accuracy, gender bias, Scottish English, accent, sociolinguistic identity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
