The Social Impact of Natural Language Processing

Principles of Biomedical Ethics

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Availability: A heuristic for judging frequency and probability

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Reproduction in Education, Society and Culture

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Indexical order and the dialectics of sociolinguistic life

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Experimental evidence of massive-scale emotional contagion through social networks

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The Affect Heuristic

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The Normative Structure of Science

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Models of ecological rationality: The recognition heuristic.

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Voice, Footing, Enregisterment

Agha, A. (2005) · Journal of Linguistic Anthropology · doi:10.1525/jlin.2005.15.1.38 · Cited by 1,326

Finding Deceptive Opinion Spam by Any Stretch of the Imagination

Ott, M., Choi, Y., Cardie, C., & Hancock, J. T. (2011) · arXiv · doi:10.48550/arxiv.1107.4557 · Cited by 1,008

A Quantitative Study of Data in the NLP community

Mieskes, M. (2017) · doi:10.18653/v1/w17-1603 · Cited by 27

Writer Profiling Without the Writer’s Text

Jurgens, D., Tsvetkov, Y., & Jurafsky, D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67256-4_43 · Cited by 28

Compositional Demographic Word Embeddings

WELCH, C. A., Kummerfeld, J. K., Pérez‐Rosas, V., & Mihalcea, R. F. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.334 · Cited by 26

Mental Health Disorder Identification From Motivational Conversations

Saha, T., Reddy, S. M., Saha, S., & Bhattacharyya, P. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3143763 · Cited by 29

SwahBERT: Language Model of Swahili

Martin, G. L., Mswahili, M. E., Jeong, Y.-S., & Young-Seob, 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.23 · Cited by 30

Ethics in Linguistics

D’Arcy, A., & Bender, E. M. (2022) · Annual Review of Linguistics · doi:10.1146/annurev-linguistics-031120-015324 · Cited by 32

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

Reliability Testing for Natural Language Processing Systems

Tan, S., Joty, S., Baxter, K., Taeihagh, A., Bennett, G. A., & Kan, M. (2021) · doi:10.18653/v1/2021.acl-long.321 · Cited by 31

Assessing dual use risks in AI research: necessity, challenges and mitigation strategies

Brenneis, A. (2024) · Research Ethics · doi:10.1177/17470161241267782 · Cited by 36

Mapping Climate Themes From 2008-2021—An Analysis of Business News Using Topic Models

Umamaheswaran, S., Dar, V., Sharma, E., & Kurian, J. S. (2023) · IEEE Access · doi:10.1109/access.2023.3256530 · Cited by 23

How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact

Jin, Z., Chauhan, G., Tse, B., Sachan, M., & Mihalcea, R. F. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.273 · Cited by 25

Enlarging the model of the human at the heart of human-centered AI: A social self-determination model of AI system impact

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

Text-Based Detection and Understanding of Changes in Mental Health

Li, Y., Mihalcea, R. F., & Wilson, S. R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01159-8_17 · Cited by 27

Educators’ perceptions and willingness to integrate Generative Artificial Intelligence in teaching and research: evidence from Kenyan higher education

Wainaina, P. K., & Sun, Y. (2025) · Discover Education · doi:10.1007/s44217-025-00820-z · Cited by 38

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

Integrating Ethics into the NLP Curriculum

Bender, E. M., Hovy, D., & Schofield, A. (2020) · doi:10.18653/v1/2020.acl-tutorials.2 · 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

Aligning generalization between humans and machines

Ilievski, F., Hammer, B., van Harmelen, F., Paaßen, B., Saralajew, S., Schmid, U., Biehl, M. L., Bolognesi, M., Dong, X., Gashteovski, K., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01109-4 · Cited by 37

Integrating Manual and Automatic Annotation for the Creation of Discourse Network Data Sets

Haunss, S., Kuhn, J., Padó, S., Blessing, A., Blokker, N., Dayanık, E., & Lapesa, G. (2020) · Politics and Governance · doi:10.17645/pag.v8i2.2591 · Cited by 23

A Large-Scale Dataset for Motivational Dialogue System: An Application of Natural Language Generation to Mental Health

Saha, T., Chopra, S., Saha, S., Bhattacharyya, P., & Kumar, P. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533924 · Cited by 22

A Large-Scale English Multi-Label Twitter Dataset for Cyberbullying and Online Abuse Detection

Salawu, S., Lumsden, J., & He, Y. (2021) · Workshop on Online Abuse and Harms (WOAH) · doi:10.18653/v1/2021.woah-1.16 · Cited by 19

VALUE: Understanding Dialect Disparity in NLU

Ziems, C., Chen, J., Harris, C., Anderson, J., & Yang, D. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.258 · Cited by 43

Guiding Principles for Participatory Design-inspired Natural Language Processing

Caselli, T., Cibin, R., Conforti, C., Encinas, E., & Teli, M. (2021) · Workshop on NLP for Positive Impact (NLP4PI) · doi:10.18653/v1/2021.nlp4posimpact-1.4 · Cited by 19

Say the Right Thing Right: Ethics Issues in Natural Language Generation Systems

Smiley, C., Schilder, F., Plachouras, V., & Leidner, J. L. (2017) · doi:10.18653/v1/w17-1613 · Cited by 18

Masking Actor Information Leads to Fairer Political Claims Detection

Dayanık, E., & Padó, S. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.404 · Cited by 17

A critical reflection on the use of toxicity detection algorithms in proactive content moderation systems

Warner, M., Strohmayer, A., Higgs, M., & Coventry, L. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103468 · Cited by 22

Exploring the ethical landscape of AI in academic writing

Ateriya, N., Sonwani, N. S., Thakur, K. S., Kumar, A., & Verma, S. K. (2025) · Egyptian Journal of Forensic Sciences · doi:10.1186/s41935-025-00453-1 · Cited by 37

Multi-VALUE: A Framework for Cross-Dialectal English NLP

Ziems, C., Held, W., Yang, J., Dhamala, J., Gupta, R., & Yang, D. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.44 · Cited by 29

CrotoneMilano for AMI at Evalita2018. A performant, cross-lingual misogyny detection system.

Basile, A., & Rubagotti, C. (2018) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.4734 · Cited by 17

The Role of Protected Class Word Lists in Bias Identification of Contextualized Word Representations

Sedoc, J., & Ungar, L. (2019) · Workshop on Gender Bias in Natural Language Processing (GeBNLP) · doi:10.18653/v1/w19-3808 · Cited by 19

Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications

Zhou, K., Blodgett, S. L., Trischler, A., Daumé, H., Suleman, K., & Olteanu, A. (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.24 · Cited by 23

A Murder and Protests, the Capitol Riot, and the Chauvin Trial: Estimating Disparate News Media Stance

Dutta, S., Li, B., Nagin, D. S., & KhudaBukhsh, A. R. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/702 · Cited by 18

The Social and the Neural Network: How to Make Natural Language Processing about People again

Hovy, D. (2018) · doi:10.18653/v1/w18-1106 · Cited by 15

Towards Best Practices for Leveraging Human Language Processing Signals for Natural Language Processing

Hollenstein, N., Barrett, M., & Beinborn, L. (2020) · Research at the University of Copenhagen (University of Copenhagen) · Cited by 15

Evaluating Machines by their Real-World Language UsePreprint

Zellers, R., Holtzman, A., Clark, E., Qin, L., Farhadi, A., & Choi, Y. (2020) · arXiv (Cornell University) · Cited by 15

Deep Learning for Text Style Transfer: A SurveyPreprint

Jin, D., Jin, Z., Hu, Z., Olga Vechtomova, A., & Mihalcea, R. F. (2020) · arXiv (Cornell University) · doi:10.3929/ethz-b-000543432 · Cited by 15

Prognosis of exploration on Chat GPT with artificial intelligence ethics

Vidhya, N. G., Devi, D. M. R., Nithya, A., & Manju, T. (2023) · Brazilian Journal of Science · doi:10.14295/bjs.v2i9.372 · Cited by 15

A decade of gender bias in machine translation

Savoldi, B., Bastings, J., Bentivogli, L., & Vanmassenhove, E. (2025) · Patterns · doi:10.1016/j.patter.2025.101257 · Cited by 17

TuringAdvice: A Generative and Dynamic Evaluation of Language Use

Zellers, R., Holtzman, A., Clark, E., Qin, L., Farhadi, A., & Choi, Y. (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.386 · Cited by 30

Towards Mental Health Analysis in Social Media for Low-resourced Languages

Garg, M. (2023) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3638761 · Cited by 17

It’s the Meaning That Counts: The State of the Art in NLP and Semantics

Hershcovich, D., & Donatelli, L. E. (2021) · Künstliche Intell. · doi:10.1007/s13218-021-00726-6 · Cited by 16

Contrastive Conditioning for Assessing Disambiguation in MT: A Case Study of Distilled Bias

Vamvas, J., & Sennrich, R. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.803 · Cited by 17

Intersectional Bias in Causal Language ModelsPreprint

Magee, L., Ghahremanlou, L., Soldatić, K., & Robertson, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.07691 · Cited by 42

Bridging Fairness and Environmental Sustainability in Natural Language Processing

Hessenthaler, M., Strubell, E., Hovy, D., & Lauscher, A. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.533 · Cited by 16

Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing

Kaffee, L.-A., Arora, A., Talat, Z., & Augenstein, I. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.932 · Cited by 18

Applicability of artificial intelligence in neuropsychological rehabilitation of patients with brain injury

Medenica, V., Ivanović, L., & Milošević, N. (2024) · Applied Neuropsychology Adult · doi:10.1080/23279095.2024.2364229 · Cited by 15

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

Incorporating Stock Market Signals for Twitter Stance Detection

Conforti, C., Berndt, J., Pilehvar, M. T., Giannitsarou, C., Toxvaerd, F., & Collier, N. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.281 · Cited by 13

A Risk-Averse Mechanism for Suicidality Assessment on Social Media

Sawhney, R., Neerkaje, A. T., & Gaur, M. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-short.70 · Cited by 10

Fair and Argumentative Language Modeling for Computational Argumentation

Holtermann, C., Lauscher, A., & Ponzetto, S. P. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.541 · Cited by 16

Analyzing Stereotypes in Generative Text Inference Tasks

Sotnikova, A., Cao, Y., Daumé, H., & Rudinger, R. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.355 · Cited by 11

NaturalLanguageProcesing4All

Hjorth, A. (2021) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3446871.3469749 · Cited by 16

Semi-Supervised Topic Modeling for Gender Bias Discovery in English and Swedish

Devinney, H., Björklund, J., & Björklund, H. (2020) · DiVA at Umeå University (Umeå University) · Cited by 12

Text Style Transfer: A Review and Experiment Evaluation.Preprint

Hu, Z., Lee, R. K.-W., & Aggarwal, C. C. (2020) · arXiv (Cornell University) · Cited by 11

Enhancing Sentiment Analysis with GPT—A Comparison of Large Language Models and Traditional Machine Learning Techniques

Obinwanne, T., & Brandtner, P. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-7569-3_17 · Cited by 10

Building Representative Corpora from Illiterate Communities: A Reviewof Challenges and Mitigation Strategies for Developing Countries

Hirmer, S. A., Leonard, A., Tumwesige, J., & Conforti, C. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.186 · Cited by 14

Graph-Based Hierarchical Attention Network for Suicide Risk Detection on Social Media

Naseem, U., Kim, J., Khushi, M., & Dunn, A. G. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543873.3587587 · Cited by 10

How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation

Gaido, M., Savoldi, B., Bentivogli, L., Negri, M., & Turchi, M. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.313 · Cited by 16

Investigating Failures of Automatic Translation in the Case of Unambiguous GenderPreprint

Renduchintala, A., & Williams, A. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.48550/arxiv.2104.07838 · Cited by 28

Pretty Princess vs. Successful Leader: Gender Roles in Greeting Card Messages

Sun, J., Wu, T., Jiang, Y., Awalegaonkar, R., Lin, X., & Yang, D. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502114 · Cited by 9

StereoKG: Data-Driven Knowledge Graph Construction For Cultural Knowledge and Stereotypes

Deshpande, A., Ruiter, D., Mosbach, M., & Klakow, D. (2022) · Workshop on Online Abuse and Harms (WOAH) · doi:10.18653/v1/2022.woah-1.7 · Cited by 15

Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning

Lee, D., Son, S., Jeon, H., Kim, S., & Han, J. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599917 · Cited by 9

Use of Formal Ethical Reviews in NLP Literature: Historical Trends and Current Practices

Santy, S., Rani, A., & Choudhury, M. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.414 · Cited by 9

A Mobile-Based System for Preventing Online Abuse and Cyberbullying

Salawu, S., Lumsden, J., & He, Y. (2022) · International Journal of Bullying Prevention · doi:10.1007/s42380-021-00115-5 · Cited by 10

The Sins of the Parents Are to Be Laid Upon the Children: Biased Humans, Biased Data, Biased Models

Osborne, M. R., Omrani, A., & Dehghani, M. (2023) · Perspectives on Psychological Science · doi:10.1177/17456916231180099 · Cited by 9

DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules

Liu, Y., Held, W., & Yang, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.850 · Cited by 10

Good, but not always Fair: An Evaluation of Gender Bias for three Commercial Machine Translation Systems

Piazzolla, S. A., Savoldi, B., & Bentivogli, L. (2024) · HERMES - Journal of Language and Communication in Business · doi:10.7146/hjlcb.vi63.137553 · 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

The Ethics of Machine Translation

Nousias, A. (2023) · Machine translation · doi:10.1007/978-3-031-14689-3_3 · Cited by 8

Enhancing Translation for Indigenous Languages: Experiments with Multilingual Models

Tonja, A. L., Nigatu, H. H., Kolesnikova, O., Sidorov, G., Gelbukh, A. F., & Kalita, J. (2023) · Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP) · doi:10.18653/v1/2023.americasnlp-1.22 · Cited by 8

What about “em”? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns

Lauscher, A., Nozza, D., Miltersen, E. H., Crowley, A., & Hovy, D. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.23 · Cited by 10

Principled Frameworks for Evaluating Ethics in NLP SystemsPreprint

Prabhumoye, S., Mayfield, E., & Black, A. W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.06425 · Cited by 8

Automation of news production in Norway: Augmenting newsroom with artificial intelligence.

Pashevich, E. (2018) · NORA - Norwegian Open Research Archives · Cited by 8

Exploring the Impact of Explainability in Large Language Model (LLM) Applications on User Experience

Wang, Y., Fang, X., Xu, Z., Li, J., & Wang, L. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719941 · Cited by 9

Values, Ethics, Morals? On the Use of Moral Concepts in NLP Research

Vida, K., Simon, J., & Lauscher, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.368 · Cited by 16

Developing a Shared Task for Speech Processing on Endangered Languages

Levow, G., Ahn, E. P., & Bender, E. M. (2021) · doi:10.33011/computel.v1i.967 · Cited by 7

Automated Essay Scoring

Klebanov, B. B., & Madnani, N. (2021) · Synthesis lectures on human language technologies · doi:10.2200/s01121ed1v01y202108hlt052 · Cited by 7

Text Analysis in Python for Social Scientists

Hovy, D. (2022) · Cambridge University Press eBooks · doi:10.1017/9781108960885 · Cited by 8

Study on the Fairness of Speaker Verification Systems Across Accent and Gender Groups

Estévez, M., & Ferrer, L. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095150 · Cited by 7

Assessment and recommendation of neural networks and precise techniques for sentiment systems analysis

Pande, S. D., Altahan, B. R., Ahammad, S. H., Mane, A. S., Inthiyaz, S., Smirani, L. K., Hossain, M. A., & Rashed, A. N. Z. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-023-04643-4 · Cited by 7

A Multi-dimensional study on Bias in Vision-Language models

Ruggeri, G., & Nozza, D. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.403 · Cited by 13

Blockchain Technology and Cybersecurity in Fintech: Opportunities and Vulnerabilities

Ajayi, O. O., Alozie, C. E., Abieba, O. A., Akerele, J. I., & Collins, A. (2025) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit25111210 · Cited by 7

Integrating the Management of Personal Data Protection and Open Science with Research Ethics

Lewis, D., Moorkens, J., & Fatema, K. (2017) · doi:10.18653/v1/w17-1607 · 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 Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation

Attanasio, G., Plaza-del-Arco, F. M., Nozza, D., & Lauscher, A. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.243 · Cited by 8

Power Shift

Bender, E. M., & Grissom, A. (2024) · Oxford University Press eBooks · doi:10.1093/oso/9780197755303.003.0010 · Cited by 7

Predicting Authorship and Author Traits from Keystroke Dynamics

Plank, B. (2018) · doi:10.18653/v1/w18-1113 · Cited by 6

Explainable Artificial Intelligence (XAI) towards Model Personality in NLP task

Nurdin, N., & Adi, D. (2021) · IPTEK Journal of Engineering · doi:10.12962/j23378557.v7i1.a8989 · Cited by 6

Experimental Standards for Deep Learning in Natural Language Processing Research

Ulmer, D., Bassignana, E., Müller-Eberstein, M., Varab, D., Zhang, M., van der Goot, R., Hardmeier, C., & Plank, B. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.196 · Cited by 9

Twitter-based Polarised Embeddings for Abusive Language Detection

Graumas, L., David, R., & Caselli, T. (2019) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw.2019.8925049 · Cited by 7

Building Representative Corpora from Illiterate Communities: A Review of Challenges and Mitigation Strategies for Developing CountriesPreprint

Hirmer, S. A., Leonard, A., Tumwesige, J., & Conforti, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.02841 · Cited by 6

A Short Review of Ethical Challenges in Clinical Natural Language Processing

Suster, S., Tulkens, S., & Daelemans, W. (2017) · Proceedings of the First ACL Workshop on Ethics in Natural Language Processing · doi:10.18653/v1/w17-1610 · Cited by 6

Task-Agnostic Low-Rank Adapters for Unseen English Dialects

Xiao, Z., Held, W., Liu, Y., & Yang, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.487 · Cited by 12

Opinion: Mental health research: to augment or not to augment

Perivolaris, A., Rueda, A., Parkington, K. B., Soni, A., Rambhatla, S., Samavi, R., Jetly, R., Greenshaw, A. J., Zhang, Y., Cao, B., et al. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1539157 · Cited by 5

Socially Aware Language Technologies: Perspectives and Practices

Yang, D., Hovy, D., Jurgens, D., & Plank, B. (2025) · Computational Linguistics · doi:10.1162/coli_a_00556 · Cited by 18

Responsible guidelines for authorship attribution tasks in NLP

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