Measuring and Mitigating Unintended Bias in Text Classification

An introduction to ROC analysis

Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879

Equality of Opportunity in Supervised Learning

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

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

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

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

Ex Machina: Personal Attacks Seen at Scale

Wulczyn, E., Thain, N., & Dixon, L. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052591 · Cited by 636

Inherent Tradeoffs in the Fair Determination of Risk Scores

Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603197 · Cited by 350

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

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

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

Reading Between the Demographic Lines: Resolving Sources of Bias in Toxicity ClassifiersPreprint

Reichert, E., Qiu, H., & Bayrooti, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.16402 · Cited by 8

Fair Hate Speech Detection through Evaluation of Social Group CounterfactualsPreprint

Davani, A. M., Omrani, A., Kennedy, B., Atari, M., Ren, X., & Dehghani, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12779 · Cited by 6

What do Bias Measures MeasurePreprint

Dev, S., Sheng, E., Zhao, J., Sun, J., Hou, Y., Sanseverino, M., Kim, J., Peng, N., & Chang, K.-W. (2021) · arXiv (Cornell University) · Cited by 6

TAR on Social Media: A Framework for Online Content ModerationPreprint

Yang, E., Lewis, D. D., & Frieder, O. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.12752 · Cited by 6

Statistical discrimination in learning agentsPreprint

Duéñez‐Guzmán, E. A., McKee, K. R., Mao, Y., Coppin, B., Chiappa, S., Vezhnevets, A. S., Bakker, M. A., Bachrach, Y., Sadedin, S., Isaac, W. S., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.11404 · Cited by 11

Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media

(2020) · doi:10.18653/v1/2020.socialnlp-1 · Cited by 6

Bias Mitigation for Toxicity Detection via Sequential Decisions

Cheng, L., Mosallanezhad, A., Silva, Y. N., Hall, D. L., & Liu, H. (2022) · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3477495.3531945 · Cited by 8

Crowdsourced Fact-Checking or Biased Commentary? Analyzing Political Bias in Twitter's Community Notes

Kuuse, S. F., Kangur, U., Chakraborty, R., & Sharma, R. R. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3717533 · Cited by 6

NLPGuard: A Framework for Mitigating the Use of Protected Attributes by NLP Classifiers

Greco, S., Zhou, K., Capra, L., Cerquitelli, T., & Quercia, D. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3686924 · Cited by 6

Detecting Bias in LLMs' Natural Language Inference Using Metamorphic Testing

Li, Z., Chen, J., Chen, H., Xu, L., & Guo, W. (2024) · IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) · doi:10.1109/qrs-c63300.2024.00015 · Cited by 5

Youth language and emerging slurs: tackling bias in BERT-based hate speech detection

Fillies, J., & Paschke, A. (2025) · AI and Ethics · doi:10.1007/s43681-025-00701-z · Cited by 6

Responsible guidelines for authorship attribution tasks in NLP

Saxena, V., Tamò‐Larrieux, A., DIJCK, G. V., & Spanakis, G. (2025) · Ethics and Information Technology · doi:10.1007/s10676-025-09821-w · Cited by 7

Developing continuous toxicity detection against increasing types of perturbed toxic text

Kang, H., Chen, J., Li, Y., Miao, X., Xu, M., Zhou, S., Zhong, M. J., Zhu, Y., & Qian, T. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104470 · Cited by 5

Fine Grained Classification of Personal Data Entities with Language Models

Nagpal, A., Dasgupta, R., & Ganesan, B. (2022) · Joint International Conference on Data Science & Management of Data (CODS-COMAD) · doi:10.1145/3493700.3493707 · Cited by 5

GrammarSHAP: An Efficient Model-Agnostic and Structure-Aware NLP Explainer

Mosca, E., Demirtürk, D., Mülln, L., Raffagnato, F., & Groh, G. (2022) · doi:10.18653/v1/2022.lnls-1.2 · Cited by 5

HARALD: Augmenting Hate Speech Data Sets with Real Data

Ilan, T., & Vilenchik, D. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.165 · Cited by 5

Towards Procedural Fairness: Uncovering Biases in How a Toxic Language Classifier Uses Sentiment Information

Nejadgholi, I., Balkır, E., Fraser, K., & Kiritchenko, S. (2022) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2022.blackboxnlp-1.18 · Cited by 10

Rethinking Machine Learning Benchmarks in the Context of Professional Codes of Conduct

Henderson, P., Hu, J., Diab, M., & Pineau, J. (2024) · ACM Symposium on Computer Science and Law (CSLAW) · doi:10.1145/3614407.3643708 · Cited by 5

Using Off-the-Shelf Harmful Content Detection Models: Best Practices for Model Reuse

Schöpke-Gonzalez, A., Wu, S., Kumar, S., & Hemphill, L. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711099 · Cited by 5

Measuring Bias and Fairness in Multiclass Classification

Blakeney, C., Atkinson, G., Huish, N., Yan, Y., Metsis, V., & Zong, Z. (2022) · IEEE International Conference on Networking, Architecture and Storage (NAS) · doi:10.1109/nas55553.2022.9925287 · Cited by 6

AI ethics for earth sciences

Rivas, P., Thompson, C., Tafur, B., Khanal, B., Ayoade, O., Jui, T. D., Sooksatra, K., Orduz, J., & Bejarano, G. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91737-7.00007-4 · Cited by 5

A Keyword Based Approach to Understanding the Overpenalization of Marginalized Groups by English Marginal Abuse Models on Twitter

Yee, K., Sebag, A. S., Redfield, O., Eck, M., Sheng, E., & Belli, L. S. (2023) · Workshop on Trustworthy Natural Language Processing (TrustNLP) · doi:10.18653/v1/2023.trustnlp-1.10 · Cited by 5

What Is Your Favorite Gender, MLM? Gender Bias Evaluation in Multilingual Masked Language Models

Yu, J., Kim, S. U., Choi, J., & Choi, J. D. (2024) · Information · doi:10.3390/info15090549 · Cited by 7

WAC: A Corpus of Wikipedia Conversations for Online Abuse DetectionPreprint

Cécillon, N., & Labatut, V. (2019) · arXiv (Cornell University) · doi:10.6084/m9.figshare.11302385 · Cited by 5

Investigating Bias In Automatic Toxic Comment Detection: An Empirical StudyPreprint

Kumar, A., & Kumar, P. M. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.06487 · Cited by 5

Sociolinguistic Properties of Word EmbeddingsPreprint

Arseniev‐Koehler, A., & Foster, J. G. (2020) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/b8kud · Cited by 7

Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP)

(2023) · doi:10.18653/v1/2023.c3nlp-1 · Cited by 5

Are Pretrained Multilingual Models Equally Fair Across Languages?Preprint

Piqueras, L. C., & Søgaard, A. Ø. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2210.05457 · Cited by 6

Localized Concept Erasure for Text-to-Image Diffusion Models Using Training-Free Gated Low-Rank Adaptation

Lee, B. H., Lim, S., & Chun, S. Y. (2025) · 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/cvpr52734.2025.01733 · Cited by 7

Transcriptomics in Aquaculture: Decoding Gene Expression for Improved Food Security

Iqbal, G., Mushtaq, S., Singh, A., Singh, L. S., Piyushbhai, M. K., Qazi, D., & Malik, M. A. (2026) · Springer eBooks · doi:10.1007/978-3-032-14971-8_13 · Cited by 4

Fairness in machine learning: definition, testing, debugging, and application

Gao, X., Shen, C. Q., Jiang, W., Lin, C., Li, Q., Wang, Q., Li, Q., & Guan, X. (2024) · Science China Information Sciences · doi:10.1007/s11432-023-4060-x · Cited by 4

Ethical Dimensions in Data-Driven Business: Balancing Fairness, Accountability, Transparency, and Explainability

Jo, A. A., & Raj, E. D. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10724314 · Cited by 4

RESPECT: A framework for promoting inclusive and respectful conversations in online communications

Raza, S., Muaad, A. Y. M., Hasan, E., Garg, M., Al-Zanbouri, Z., & Bashir, S. R. (2025) · Natural Language Processing Journal · doi:10.1016/j.nlp.2025.100126 · Cited by 5

Asking an AI for salary negotiation advice is a matter of concern: Controlled experimental perturbation of ChatGPT for protected and non-protected group discrimination on a contextual task with no clear ground truth answers

Geiger, R. S., O’Sullivan, F., Wang, E., & Lo, J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0318500 · Cited by 6

Artificial Intelligence and unintended bias: A call for responsible innovation

Talati, D. V. (2021) · International Journal of Science and Research Archive · doi:10.30574/ijsra.2021.2.2.0110 · Cited by 5

Mitigating Bias in Hate Speech Detection With a Small Number of Expert Annotations: A Prompt-Based Learning Approach

Wei, D., Chau, M., & Li, Z. (2025) · MIS Quarterly · doi:10.25300/misq/2025/18416 · Cited by 4

Identifying and addressing unintended values when designing (with) Artificial Intelligence

Stoimenova, N., & Kleinsmann, M. S. (2020) · Proceedings of DRS · doi:10.21606/drs.2020.222 · Cited by 6

With Prejudice to None: A Few-Shot, Multilingual Transfer Learning Approach to Detect Social Bias in Low Resource Languages

Sahoo, N., Mallela, N., & Bhattacharyya, P. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.842 · Cited by 4

Knowledge Graphs: Semantics, Machine Learning, and Languages

Acosta Maribel, P. S. (2023) · Studies on the semantic web · doi:10.3233/ssw56 · Cited by 4

Improving Classifier Robustness through Active Generative Counterfactual Data Augmentation

Balashankar, A., Wang, X., Qin, Y., Packer, B., Thain, N., Ed, H., Chen, J., & Beutel, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.10 · Cited by 6

Understanding Individual Emotional Responses: Analyzing Variations and Introducing Personal Emotional Bias in Kannada Opinion Data Set

Kadakol, S., Sanjanasri, J. P., & Lal, G. J. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-9043-6_6 · Cited by 4

A systematic review of toxicity in large language models: definitions, datasets, detectors, detoxification methods and challenges

Villate-Castillo, G., Ser, J. D., & Sanz, B. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11548-z · Cited by 7

Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions

Al-Zanbouri, Z., Sharma, G., & Raza, S. (2024) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi61247.2024.00105 · Cited by 4

FairFlow: An Automated Approach to Model-Based Counterfactual Data Augmentation for NLP

Tokpo, E. K., & Calders, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70368-3_10 · Cited by 4

ID-XCB: Data-Independent Debiasing for Fair and Accurate Transformer-Based Cyberbullying Detection

Yi, P., & Zubiaga, A. (2025) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v19i1.35924 · Cited by 7

Reward Model Interpretability via Optimal and Pessimal Tokens

Christian, B., Kirk, H. R., Thompson, J. A. F., Summerfield, C., & Dumbalska, T. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732068 · Cited by 10

Application of Artificial Intelligence to Automate Construction Materials Data Classification

Chunyaem, W., Charoensiriwath, C., Panuwatwanich, K., Saito, S., & Padungtin, P. (2019) · Creative Construction Conference (CCC) · doi:10.3311/ccc2019-002 · Cited by 4

What BERTs and GPTs know about your brand? Probing contextual language models for affect associations

Srivastava, V. P., Pilli, S., Bhat, S., Pedanekar, N., & Karande, S. S. (2021) · Workshop on Knowledge Extraction and Integration for Deep Learning Architectures: Deep Learning Inside Out (DeeLIO) · doi:10.18653/v1/2021.deelio-1.12 · Cited by 4

Quantification and Mitigation of Directional Pairwise Class Confusion Bias in a Chatbot Intent Classification Model

Sayenju, S., Aygün, R. S., Boardman, J., Don, D. R., Zhang, Y., Franks, B., Johnston, S., Lee, G., Sullivan, D., & Modgil, G. (2022) · International Journal of Semantic Computing · doi:10.1142/s1793351x22500040 · Cited by 4

Darkness can not drive out darkness: Investigating Bias in Hate SpeechDetection Models

Elsafoury, F. (2022) · Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (ACL SRW) · doi:10.18653/v1/2022.acl-srw.4 · Cited by 4

A Weakly Supervised Classifier and Dataset of White Supremacist Language

Yoder, M. M., Diab, A., Brown, D., & Carley, K. M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.17 · Cited by 5

Sustaining Fairness via Incremental Learning

Chowdhury, S. B. R., & Chaturvedi, S. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i6.25833 · Cited by 5

CT-GAT: Cross-Task Generative Adversarial Attack based on Transferability

Lv, M., Dai, C., Li, K., Zhou, W. G., & Hu, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.340 · Cited by 4

Model and evaluation: towards fairness in multilingual text classification

Lin, N., He, J., Tang, Z., Fang, J., Zhou, D., & Yang, A. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02824-5 · Cited by 4

Detecting and Mitigating Bias in Data Using Machine Learning with Pre-Training Metrics

Mandhala, V. N., Bhattacharyya, D., Midhunchakkaravarthy, D., & Kim, H.-J. (2022) · Ingénierie des systèmes d information · doi:10.18280/isi.270114 · Cited by 4

Efficiently Mitigating Classification Bias via Transfer Learning.Preprint

Jin, X., Barbieri, F., Davani, A. M., Kennedy, B., Neves, L., & Ren, X. (2020) · arXiv (Cornell University) · Cited by 4

Technologies of Trusted Artificial Intelligence

Avdoshin, S. M., Pesotskaya, E., & Patrushev, K. A. (2024) · INFORMACIONNYE TEHNOLOGII · doi:10.17587/it.30.400-410 · Cited by 3

Exploring the Boundaries of Content Moderation in Text-to-Image Generation

Riccio, P., Curto, G., & Oliver, N. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92089-9_11 · Cited by 5

NLP as language ideology: discursive and algorithmic constructions of ‘toxic’ language in machine learning research

Chronis, G. (2025) · AI & Society · doi:10.1007/s00146-025-02463-y · Cited by 4

How Generative AI Empowers Attackers and Defenders Across the Trust & Safety Landscape

Kelley, P. G., Rousso-Schindler, S., Shelby, R. M., Thomas, K., & Woodruff, A. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791363 · Cited by 3

Thesis Distillation: Investigating The Impact of Bias in NLP Models on Hate Speech Detection

Elsafoury, F. (2023) · doi:10.18653/v1/2023.bigpicture-1.5 · Cited by 3

Detecting Bias in Large Language Models: Fine-Tuned KcBERT

Lee, J., & Chung, T. (2025) · Lecture notes in computer science · doi:10.1007/978-981-97-8705-0_6 · Cited by 5

An Empirical Study of the Downstream Reliability of Pre-Trained Word Embeddings

Rios, A., & Lwowski, B. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.299 · Cited by 3

Identity Term Sampling for Measuring Gender Bias in Training Data

Sobhani, N. C., & Delany, S. J. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-26438-2_18 · Cited by 3

Uncovering and Categorizing Social Biases in Text-to-SQL

Liu, Y., Yan, G., Zhe, S., Chen, X., Ash, E., & Lou, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.759 · Cited by 3

Antisemitism in Online Communication

Becker, M. J., Ascone, L., Placzynta, K., & Vincent, C. (2024) · Open Book Publishers · doi:10.11647/obp.0406 · Cited by 3

Η χρήση της Τεχνητής Νοημοσύνης στις Ελληνικές Αίθουσες Σύνταξης

Panagopoulos, A. M., Kάπος, Π., & Τριανταφύλλου, Σ. (2025) · Ετήσιο Ελληνόφωνο Επιστημονικό Συνέδριο Εργαστηρίων Επικοινωνίας · doi:10.12681/cclabs.8034 · Cited by 3

Learning fair prediction models with an imputed sensitive variable: Empirical studies

Kim, Y., & Jeong, H. (2022) · Communications for Statistical Applications and Methods · doi:10.29220/csam.2022.29.2.251 · Cited by 3

Enriching Abusive Language Detection with Community Context

Saleem, H. M., Kurrek, J., & Ruths, D. A. (2022) · Workshop on Online Abuse and Harms (WOAH) · doi:10.18653/v1/2022.woah-1.13 · Cited by 3

Exposing Racial Dialect Bias in Abusive Language Detection: Can Explainability Play a Role?

Manerba, M. M., & Morini, V. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-23618-1_32 · Cited by 3

CoVariance-based Causal Debiasing for Entity and Relation Extraction

Ren, L., Liu, Y., Cao, Y., & Ouyang, C. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.173 · Cited by 4

A tool supported framework for the assessment of algorithmic accountability

Tagiou, E., Kanellopoulos, Y., Aridas, C. K., & Makris, C. H. (2019) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa.2019.8900715 · Cited by 3

What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations

Xie, Z., Brophy, J., Noack, A., You, W., Asthana, K., Perkins, C., Reis, S., Hammoudeh, Z., Lowd, D., & Singh, S. (2021) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2021.blackboxnlp-1.6 · Cited by 3

Simplicity Bias Leads to Amplified Performance Disparities

Bell, S. J., & Sagun, L. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594003 · Cited by 5

Knowledge Transfer Between Tasks and Languages in the Multi-task Encoder-agnostic Transformer-based Models

MIPT, Karpov, D., & Konovalov, V. (2023) · Computational Linguistics and Intellectual Technologies · doi:10.28995/2075-7182-2023-22-200-214 · Cited by 3

Interpreting Social Respect: A Normative Lens for ML ModelsPreprint

Hutchinson, B., Pittl, K. J., & Mitchell, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.07336 · Cited by 3

Geometry matters: Exploring language examples at the decision boundaryPreprint

Datta, D., Kumar, S., Barnes, L. E., & Fletcher, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.07212 · Cited by 3

FIND: Human-in-the-Loop Debugging Deep Text ClassifiersPreprint

Lertvittayakumjorn, P., Specia, L., & Toni, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.04987 · Cited by 13

Looking on Efficiency of Content Moderation Systems from the Lens of Reddit's Content Moderation Experience During COVID-19Preprint

Iqbal, W., Gareth Tyson, A., & Castro, I. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4007864 · Cited by 3

Advancing fairness in natural language processing : from traditional methods to explainability

Jourdan, F. (2024) · theses.fr (ABES) · doi:10.70675/b8535a05z82f1z46f7z9688z3cd35cea8381 · Cited by 3

Analysis and Mitigation of Religion Bias in Indonesian Natural Language Processing Datasets

Fauzan, M. A., & Saptawijaya, A. (2023) · Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) · doi:10.29207/resti.v7i4.5035 · Cited by 2

Latent Feature-based Data Splits to Improve Generalisation Evaluation: A Hate Speech Detection Case Study

Züfle, M., Dankers, V., & Titov, I. (2023) · GenBench Workshop on Generalisation (Benchmarking) in NLP (GenBench) · doi:10.18653/v1/2023.genbench-1.9 · Cited by 2

Feature Attribution-Guided Contrastive Learning: Mitigating Lexical Bias in Toxic Speech Detection

Peng, Z., Ren, Y., Wang, D., Zhang, L., Yuan, L., & Ji, Y. (2023) · International Conference on New Trends in Computational Intelligence (NTCI) · doi:10.1109/ntci60157.2023.10403665 · Cited by 2

FedEvalFair: A Privacy-Preserving and Statistically Grounded Federated Fairness Evaluation Framework

Wang, Z., Sun, H., & Zhi, Z. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681545 · Cited by 2

Bias mitigation in text classification through cGAN and LLMs

Kumar, G., & Singh, J. P. (2024) · Proceedings of the Indian National Science Academy · doi:10.1007/s43538-024-00371-1 · Cited by 2

Empowering Creators in the Fight Against Online Hate: A Qualitative Exploration of AI-Mediated Counterspeech Tools

Huang, P. Y., Deng, J., Yang, Y., & Williams, S. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757589 · Cited by 2

Automated Online Detection of Harmful and Dangerous Content in Social Networks: a Systematic Review

Ostrovska, O., & Lyashkevych, V. (2025) · Artificial Intelligence · doi:10.15407/jai2025.04.108 · Cited by 2

Large language models in radiogenomics: a comprehensive survey of applications from imaging to genetics

Cheema, M. N., Nazir, A., Harmanci, A. O., Harmanci, A. O., Cheema, Y., Masood, S., & Khokhar, F. A. (2026) · The Visual Computer · doi:10.1007/s00371-026-04385-2 · Cited by 2

Bias Detection and Mitigation in Sentiment Analysis

Dervişoğlu, H., & Amasyalı, M. F. (2021) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu52992.2021.9599010 · Cited by 2

Adversarial Removal of Population Bias in Genomics Phenotype Prediction

Zhao, H., & Wang, W. (2022) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw58026.2022.00052 · Cited by 2

The Psychological and Behavioral Mechanisms of Online Gambling Game Addiction: A Comparative Study of Cognitive Biases, Reward Systems, and Intervention Strategies

Parveen, N., Ahsen, S., Hassaan, H. M., Mirza, M. H., & Iqbal, S. (2024) · Journal of Policy Research · doi:10.61506/02.00297 · Cited by 2

Comparative analysis of moral decision-making and trust dynamics: human reasoning vs. ChatGPT-3 narratives

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