6. Citation and bibliography

6.1. Citation

If you use FairLangProc in your research, please cite our paper, FairLangProc: A Python package for fairness in NLP.

6.1.1. BibTeX

@misc{pérezperalta2025fairlangprocpythonpackagefairness,
      title={FairLangProc: A Python package for fairness in NLP},
      author={Arturo Pérez-Peralta and Sandra Benítez-Peña and Rosa E. Lillo},
      year={2025},
      eprint={2508.03677},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2508.03677},
}

6.1.2. APA Style

Pérez-Peralta, A., Benítez-Peña, S., & Lillo, R. E. (2025). FairLangProc: A Python package for fairness in NLP. arXiv preprint arXiv:2508.03677.

6.1.3. IEEE Style

1. Pérez-Peralta, S. Benítez-Peña, and R. E. Lillo, “FairLangProc: A Python package for fairness in NLP,” arXiv preprint arXiv:2508.03677, 2025.

6.1.4. MLA Style

Pérez-Peralta, Arturo, et al. “FairLangProc: A Python package for fairness in NLP.” arXiv preprint arXiv:2508.03677 (2025).

6.2. Related Publications

If you use specific datasets, metrics, or algorithms from FairLangProc, please also cite the original papers:

6.2.1. Datasets

• BBQ (Bias Benchmark for Question Answering): Parrish, A., et al. (2021). BBQ: A hand-built bias benchmark for question answering. arXiv:2110.08193.

• BEC-Pro (Bias Evaluation Corpus for Professions): Bartl, M., et al. (2020). Uncovering Hidden Bias in NLP. arXiv:2010.14534.

• BOLD (Bias in Open-Ended Language Generation): Dhamala, J., et al. (2021). BOLD: A large-scale bias benchmark for language model evaluation. ACM FAccT.

• BUG (Bias evaluation Universal Benchmark): Levy, S., et al. (2021). A Unified Approach to Benchmarking and Mitigating Bias. arXiv:2109.03858.

• CrowS-Pairs: Nangia, N., et al. (2020). CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models. EMNLP.

• GAP (Gender Ambiguous Pronouns): Webster, K., et al. (2018). Mind the GAP: A systematic approach to measuring gender bias in coreference resolution. ACL.

• HolisticBias: Smith, E. M., et al. (2022). HolisticBias: Sensitivity analysis across sociodemographic identity. arXiv:2205.09209.

• HONEST (HOlingistic Neutrality Evalutation for gender-based Stereotypes): Nozza, D., et al. (2021). HONEST: Measuring Hurtful Language in Gendered Audio. NAACL.

• StereoSet: Nadeem, M., et al. (2020). StereoSet: Measuring stereotypical bias in language models. arXiv:2004.09456.

• UnQover: Li, T., et al. (2020). UnQovering Unintended Bias in Language Models through Compositional Reasoning. arXiv:2010.02428.

• WinoBias+: Vanmassenhove, E., et al. (2021). Toward Controllable Gender Bias Mitigation in Neural Machine Translation. arXiv:2109.06105.

• WinoBias: Zhao, J., et al. (2018). Gender Bias in Coreference Resolution. ACL.

• WinoGender: Rudinger, R., et al. (2018). Gender Bias in Neural Coreference Resolution. ACL.

6.2.2. Metrics

• WEAT (Word Embedding Association Test): Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science.

• LPBS (Log Probability Bias Score): Kurita, K., et al. (2019). Measuring bias in contextualized word representations. ACL.

• CBS (Categorical Bias Score): Ahn, H., et al. (2021). Why Do Pretrained Language Models Cross-persona Conversational Tasks? EMNLP.

• CPS (CrowS-Pairs Score): Nangia, N., et al. (2020). CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models. EMNLP.

• AUL (All Unmasked Log-likelihood): Kaneko, M., et al. (2021). Gender and Racial Bias in Language Models. arXiv:2104.07496.

• DR (Demographic Representation): Liang, P. P., et al. (2022). Towards Fairness in Text Generation. arXiv:2211.09110.

• SA (Stereotypical Association): Liang, P. P., et al. (2022). Towards Fairness in Text Generation. arXiv:2211.09110.

• HONEST (HOlingistic Neutrality Evalutation for gender-based Stereotypes): Nozza, D., Bianchi, F., & Hovy, D. (2021). HONEST: Measuring Hurtful Language in Gendered Audio. NAACL.

6.2.3. Algorithms

Pre-processing:

• CDA (Counterfactual Data Augmentation): Webster, K., et al. (2020). Measuring and Reducing the Correlation Between Word Embeddings and Gendered Constructs. ACL.

• Projection-based Debiasing: Bolukbasi, M., et al. (2016). Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. NeurIPS.

• BLIND (Bias removaL wIth No Demographics): Orgad, H., et al. (2023). BLIND: Bias Removal via Debiasing at Inference Time. ACL.

In-processing:

• ADELE (Adapter-based DEbiasing of LanguagE models): Lauscher, A., et al. (2021). AdapterFusion: Non-invasive Transfer of Knowledge for Text Classification. arXiv:2109.03646.

• EAR (Entropy Attention Regularizer): Attanasio, G., et al. (2022). SENTeX: BERT-based Sentiment Analysis for Italian. arXiv:2203.09192.

• Embedding-based Regularizer: Liu, Y., et al. (2020). Fairness without Demographics through Adversarial Learning. arXiv:1910.10486.

• Selective Unfreezing: Gira, M., et al. (2022). Selective Knowledge Transfer for Democratizing NLP. ACL.

Intra-processing:

• MoDDiffy (Modular Debiasing with Diff Subnetworks): Hauzenberger, L., et al. (2023). Modular Debiasing for Language Models. ACL Findings.

• EAT (Entropy Attention Temperature scaling): Zayed, A., et al. (2023). DeBiasing: Learning about Bias Mitigation from Plain Models. arXiv:2305.13088.