Mitigating Gender Bias in Natural Language Processing: Literature Review

Generative Adversarial Nets

Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works

GAN（Generative Adversarial Nets）

淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works

Deep Contextualized Word Representations

Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983 · see citing works

Measuring individual differences in implicit cognition: The implicit association test.

Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.74.6.1464 · Cited by 11,720 · see citing works

Science faculty’s subtle gender biases favor male students

Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J., & Handelsman, J. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1211286109 · Cited by 3,150 · see citing works

Semantics derived automatically from language corpora contain human-like biases

Caliskan, A., Bryson, J. J., & Narayanan, A. (2017) · Science · doi:10.1126/science.aal4230 · Cited by 2,887 · see citing works

Equality of Opportunity in Supervised Learning

Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536 · see citing works

Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter

Talat, Z., & Hovy, D. (2016) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/n16-2013 · Cited by 1,919 · see citing works

Certifying and Removing Disparate Impact

Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783311 · Cited by 1,827 · see citing works

Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016) · arXiv · doi:10.48550/arxiv.1607.06520 · Cited by 1,615 · see citing works

Mitigating Unwanted Biases with Adversarial Learning

Zhang, B. H., Lemoine, B., & Mitchell, M. (2018) · Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3278721.3278779 · Cited by 1,387 · see citing works

Word embeddings quantify 100 years of gender and ethnic stereotypes

Garg, N., Schiebinger, L., Jurafsky, D., & Zou, J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1720347115 · Cited by 1,065 · see citing works

Inherent trade-offs in the fair determination of risk scores

Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.itcs.2017.43 · Cited by 943 · cited 2× · see citing works

Inherent Trade-Offs in the Fair Determination of Risk Scores

Kleinberg, J., Mullainathan, S., & Raghavan, M. (2016) · arXiv · doi:10.48550/arxiv.1609.05807 · Cited by 941 · see citing works

End-to-end Neural Coreference Resolution

Lee, K., He, L., Lewis, M., & Zettlemoyer, L. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1018 · Cited by 869 · see citing works

Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science

Bender, E. M., & Friedman, B. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00041 · Cited by 854 · cited 1× · see citing works

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

Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1323 · Cited by 829 · cited 5× · see citing works

Non-binary or genderqueer genders

Richards, C., Bouman, W. P., Seal, L., Barker, M. J., Nieder, T. O., & T’Sjoen, G. (2016) · International Review of Psychiatry · doi:10.3109/09540261.2015.1106446 · Cited by 782 · see citing works

Three naive Bayes approaches for discrimination-free classification.

Calders, T., & Verwer, S. (2010) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0190-x · Cited by 764 · see citing works

Gender bias in coreference resolution: Evaluation and debiasing methods

Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) · doi:10.18653/v1/n18-2003 · Cited by 684 · see citing works

Measuring and Mitigating Unintended Bias in Text Classification

Dixon, L., Li, J., Sorensen, J., Thain, N., & Vasserman, L. (2018) · Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3278721.3278729 · Cited by 674 · see citing works

Poincaré Embeddings for Learning Hierarchical Representations

Nickel, M., & Kiela, D. (2017) · arXiv · doi:10.48550/arxiv.1705.08039 · Cited by 667 · see citing works

Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior

Founta, A.-M., Djouvas, C., Chatzakou, D., Ilias Leontiadis, A., Blackburn, J., Stringhini, G., Vakali, A. I., Sirivianos, M., & Kourtellis, N. (2018) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v12i1.14991 · Cited by 605 · see citing works

On Fairness and CalibrationPreprint

Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J. M., & Weinberger, K. Q. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.02012 · Cited by 509 · see citing works

On Measuring Social Biases in Sentence Encoders

May, C., Wang, A., Bordia, S., Bowman, S. R., & Rudinger, R. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1063 · Cited by 461 · see citing works

Gender Bias in Coreference Resolution

Rudinger, R., Naradowsky, J., Leonard, B., & Van Durme, B. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) · doi:10.18653/v1/n18-2002 · Cited by 443 · see citing works

A Linear Programming Formulation for Global Inference in Natural Language Tasks

Roth, D., & Yih, W.-T. (2004) · Cited by 441 · see citing works

Assessing gender bias in machine translation: a case study with Google Translate

Prates, M. O. R., Avelar, P. H., & Lamb, L. C. (2020) · Neural Computing and Applications · doi:10.1007/s00521-019-04144-6 · Cited by 410 · see citing works

Women Also Snowboard: Overcoming Bias in Captioning Models

Hendricks, L. A., Burns, K., Saenko, K., Darrell, T., & Rohrbach, A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01219-9_47 · Cited by 404 · see citing works

Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems

Kiritchenko, S., & Mohammad, S. (2018) · Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/s18-2005 · Cited by 384 · see citing works

Learning Gender-Neutral Word Embeddings

Zhao, J., Zhou, Y., Li, Z., Wang, W., & Chang, K.-W. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1521 · Cited by 376 · see citing works

The Frontiers of Fairness in Machine LearningPreprint

Chouldechova, A., & Roth, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.08810 · Cited by 372 · see citing works

Avoiding Discrimination through Causal Reasoning

Kilbertus, N., Rojas-Carulla, M., Parascandolo, G., Hardt, M., Janzing, D., & Schölkopf, B. (2017) · arXiv · doi:10.48550/arxiv.1706.02744 · Cited by 358 · see citing works

Gender Bias in Contextualized Word Embeddings

Zhao, J., Wang, T., Yatskar, M., Cotterell, R., Ordóñez, V., & Chang, K.-W. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1064 · Cited by 341 · see citing works

Gender and Dialect Bias in YouTube’s Automatic Captions

Tatman, R. (2017) · Proceedings of the First ACL Workshop on Ethics in Natural Language Processing · doi:10.18653/v1/w17-1606 · Cited by 338 · see citing works

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 · see citing works

Reducing Gender Bias in Abusive Language Detection

Park, J. H., Shin, J., & Fung, P. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1302 · Cited by 323 · see citing works

Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings

Manzini, T., Chong, L. Y., Black, A. W., & Tsvetkov, Y. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1062 · Cited by 284 · see citing works

Data Decisions and Theoretical Implications when Adversarially Learning Fair RepresentationsPreprint

Beutel, A., Chen, J., Zhao, Z., & Chi, E. H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.00075 · Cited by 282 · see citing works

Fairness Without Demographics in Repeated Loss MinimizationPreprint

Hashimoto, T., Srivastava, M., Namkoong, H., & Liang, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.08010 · Cited by 263 · see citing works

Adversarial Removal of Demographic Attributes from Text Data

Elazar, Y., & Goldberg, Y. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1002 · Cited by 261 · see citing works

Situation Recognition: Visual Semantic Role Labeling for Image Understanding

Yatskar, M., Zettlemoyer, L., & Farhadi, A. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.597 · Cited by 253 · see citing works

Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove ThemPreprint

Gonen, H., & Goldberg, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.03862 · Cited by 242 · see citing works

Mind the GAP: A balanced corpus of gendered ambiguous pronouns

Webster, K., Recasens, M., Axelrod, V., & Baldridge, J. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00240 · Cited by 223 · see citing works

Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels

Misra, I., Zitnick, C. L., Mitchell, M., & Girshick, R. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.320 · Cited by 210 · see citing works

Getting Gender Right in Neural Machine Translation

Vanmassenhove, E., Hardmeier, C., & Way, A. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1334 · Cited by 170 · see citing works

Towards Robust and Privacy-preserving Text Representations

Li, Y., Baldwin, T., & Cohn, T. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-2005 · Cited by 158 · see citing works

Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove ThemPreprint

Gonen, H., & Goldberg, Y. (2019) · arXiv (Cornell University) · Cited by 156 · see citing works

A Tutorial on Dual Decomposition and Lagrangian Relaxation for Inference in Natural Language Processing

Rush, A. M., & Collins, M. J. (2012) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3680 · Cited by 107 · see citing works

National differences in gender–science stereotypes predict national sex differences in science and math achievementPreprint

Nosek, B. A., Smyth, F. L., Sriram, N., Lindner, N. M., Devos, T., Ayala, A., Bar‐Anan, Y., Bergh, R., Cai, H., Gonsalkorale, K., et al. (2017) · doi:10.31234/osf.io/sp8nc · Cited by 102 · see citing works

Homophily and the Glass Ceiling Effect in Social Networks

Avin, C., Keller, B., Lotker, Z., Mathieu, C., Peleg, D., & Pignolet, Y.-A. (2015) · Proceedings of the 2015 Conference on Innovations in Theoretical Computer Science · doi:10.1145/2688073.2688097 · Cited by 90 · see citing works

Obfuscating Gender in Social Media Writing

Reddy, S., & Knight, K. K. (2016) · Workshop on Natural Language Processing and Computational Social Science (NLP+CSS) · doi:10.18653/v1/w16-5603 · Cited by 86 · see citing works

Gender Bias in Contextualized Word EmbeddingsPreprint

Zhao, J., Wang, T., Yatskar, M., Cotterell, R., Ordóñez, V., & Chang, K.-W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.03310 · Cited by 71 · see citing works

Gender Bias in Neural Natural Language ProcessingPreprint

Lu, K., Mardziel, P., Wu, F., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.11714 · Cited by 70 · see citing works

Tie-breaker: Using language models to quantify gender bias in sports journalismPreprint

Fu, L., Danescu-Niculescu-Mizil, C., & Lee, L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.03895 · Cited by 29 · see citing works

Analyze, Detect and Remove Gender Stereotyping from Bollywood Movies

Madaan, N., Mehta, S., Agrawaal, T. S., et al. (2018) · Cited by 26 · see citing works

Distributional techniques for philosophical enquiry

Herbelot, A., von Redecker, E., & Müller, J. M. (2012) · Cited by 14 · see citing works

The glass ceiling in NLP

Schluter, N. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1301 · Cited by 14 · see citing works

Language (Technology) is Power: A Critical Survey of “Bias” in NLP

Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.485 · Cited by 1,050 · see citing works

Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare

Cirillo, D., Catuara-Solarz, S., Morey, C., Guney, E., Subirats, L., Mellino, S., Gigante, A., Valencia, A., Rementeria, M. J., Chadha, A. S., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0288-5 · Cited by 553 · see citing works

Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI

Jacovi, A., Marasović, A., Miller, T., & Goldberg, Y. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445923 · Cited by 545 · see citing works

Fairness in machine learning: A survey

Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646 · see citing works

Gender bias and stereotypes in Large Language Models

Kotek, H., Dockum, R., & Sun, D. (2023) · Proceedings of The ACM Collective Intelligence Conference · doi:10.1145/3582269.3615599 · Cited by 381 · see citing works

Biases in Large Language Models: Origins, Inventory, and Discussion

Navigli, R., Conia, S., & Ross, B. (2023) · Journal of Data and Information Quality · doi:10.1145/3597307 · Cited by 364 · see citing works

Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models

Ferrara, E. (2023) · arXiv · doi:10.48550/arxiv.2304.03738 · Cited by 282

Should ChatGPT be biased? Challenges and risks of bias in large language models

Ferrara, E. (2023) · First Monday · doi:10.5210/fm.v28i11.13346 · Cited by 282 · see citing works

BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation

Dhamala, J., Sun, T., Kumar, V., Krishna, S., Pruksachatkun, Y., Chang, K.-W., & Gupta, R. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445924 · Cited by 244 · see citing works

Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models

Schramowski, P., Brack, M., Deiseroth, B., & Kersting, K. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.02157 · Cited by 219 · see citing works

Bias of AI-generated content: an examination of news produced by large language models

Fang, X., Che, S., Mao, M., Zhang, H., Zhao, M., & Zhao, X. (2024) · Scientific Reports · doi:10.1038/s41598-024-55686-2 · Cited by 194 · see citing works

Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs

Tong, S., Liu, Z., Zhai, Y., Ma, Y., LeCun, Y., & Xie, S. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00914 · Cited by 189 · see citing works

Artificial Intelligence in mental health and the biases of language based models

Straw, I., & Callison-Burch, C. (2020) · PLOS ONE · doi:10.1371/journal.pone.0240376 · Cited by 164 · see citing works

From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models

Feng, S., Park, C. Y., Liu, Y., & Tsvetkov, Y. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.656 · Cited by 149 · see citing works

Fairness in Machine Learning: A Survey

Caton, S., & Haas, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04053 · Cited by 646 · see citing works

Societal Biases in Language Generation: Progress and Challenges

Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (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.330 · Cited by 131 · see citing works

SqueezeBERT: What can computer vision teach NLP about efficient neural networks?

Iandola, F., Shaw, A., Krishna, R., & Keutzer, K. (2020) · Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing · doi:10.18653/v1/2020.sustainlp-1.17 · Cited by 114 · see citing works

Challenges and Strategies in Cross-Cultural NLP

Hershcovich, D., Frank, S., Lent, H., de Lhoneux, M., Abdou, M., Brandl, S., Bugliarello, E., Cabello Piqueras, L., Chalkidis, I., Cui, R., et al. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.482 · Cited by 99 · see citing works

Measure and Improve Robustness in NLP Models: A Survey

Wang, X., Wang, H., & Yang, D. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.339 · Cited by 90 · see citing works

Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics

Czarnowska, P., Vyas, Y., & Shah, K. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00425 · Cited by 72 · see citing works

PANDORA Talks: Personality and Demographics on Reddit

Gjurković, M., Karan, M., Vukojević, I., Bošnjak, M., & Snajder, J. (2021) · Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media · doi:10.18653/v1/2021.socialnlp-1.12 · Cited by 58 · see citing works

Changing the World by Changing the Data

Rogers, 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.170 · Cited by 51 · see citing works

Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness

Lyu, L., He, X., & Li, Y. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.213 · Cited by 51 · see citing works

Mitigating Gender Bias in Captioning Systems

Tang, R., Du, M., Li, Y., Liu, Z., Zou, N., & Hu, X. (2021) · Proceedings of the Web Conference 2021 · doi:10.1145/3442381.3449950 · Cited by 50 · see citing works

Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey

Kumar, S., Balachandran, V., Njoo, L., Anastasopoulos, A., & Tsvetkov, Y. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.241 · Cited by 38 · see citing works

Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service”

Lewicki, K., Lee, M. S. A., Cobbe, J., & Singh, J. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581463 · Cited by 30 · see citing works

When Do Pre-Training Biases Propagate to Downstream Tasks? A Case Study in Text Summarization

Ladhak, F., Durmus, E., Suzgun, M., Zhang, T., Jurafsky, D., McKeown, K., & Hashimoto, T. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.234 · Cited by 30 · see citing works

Examining risks of racial biases in NLP tools for child protective services

Field, A., Coston, A., Gandhi, N., Chouldechova, A., Putnam-Hornstein, E., Steier, D., & Tsvetkov, Y. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3593013.3594094 · Cited by 18 · see citing works

Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions

Borah, A., & Mihalcea, R. (2024) · Findings of the Association for Computational Linguistics: EMNLP 2024 · doi:10.18653/v1/2024.findings-emnlp.545 · Cited by 15 · see citing works

Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

Using DataGrid Control to Realize DataBase of Querying in VB6.0

Hu, S. (2000) · Modern Computer

Study and Two Types of Typical Usage of DataGrid Web Server Control

Hai-tao, L. (2005) · Modern Computer

Achieving Parameter of DBSCAN Based on Datagrid

Xu, Q. (2010)

PACWON: A parallelizing compiler for workstations on a network

Das, D. K., & Das, P. P. (1998) · Journal of Systems Architecture · doi:10.1016/s1383-7621(97)00078-7