mlsanityData to 14 Oct 2026

Addressing Bias in LLMs: Strategies and Application to Fair AI-based Recruitment

arXiv 2506.11880 ↗First posted 13 Jun 2025cs.AI cs.CLcited by 13

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Abstract (arXiv)

The use of language technologies in high-stake settings is increasing in recent years, mostly motivated by the success of Large Language Models (LLMs). However, despite the great performance of LLMs, they are are susceptible to ethical concerns, such as demographic biases, accountability, or privacy. This work seeks to analyze the capacity of Transformers-based systems to learn demographic biases present in the data, using a case study on AI-based automated recruitment. We propose a privacy-enhancing framework to reduce gender information from the learning pipeline as a way to mitigate biased behaviors in the final tools. Our experiments analyze the influence of data biases on systems built on two different LLMs, and how the proposed framework effectively prevents trained systems from reproducing the bias in the data.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 31 of its 49 references that are in the map. First posted on arXiv 13 Jun 2025.

Builds on

31 of 49 references.

References · 31

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  5. 2017Axiomatic Attribution for Deep Networkscited by 3,384
  6. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 956
  7. 2016Semantics derived automatically from language corpora contain human-like biasescited by 901
  8. 2019The Woman Worked as a Babysitter: On Biases in Language Generationcited by 541
  9. 2019On Measuring Social Biases in Sentence Encoderscited by 398
  10. 2019Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Settingcited by 349
  11. 2018Learning Not to Learn: Training Deep Neural Networks with Biased Datacited by 255
  12. 2017Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypescited by 253
19 more
  1. 2019Evaluating Gender Bias in Machine Translationcited by 249
  2. 2018Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systemscited by 234
  3. 2023MART: Improving LLM Safety with Multi-round Automatic Red-Teamingcited by 163
  4. 2018Assessing Gender Bias in Machine Translation -- A Case Study with Google Translatecited by 121
  5. 2016Measuring Fairness in Ranked Outputscited by 81
  6. 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challengescited by 56
  7. 2019What's in a Name? Reducing Bias in Bios without Access to Protected Attributescited by 51
  8. 2020SensitiveLoss: Improving Accuracy and Fairness of Face Representations with Discrimination-Aware Deep Learningcited by 50
  9. 2019What does it mean to solve the problem of discrimination in hiring? Social, technical and legal perspectives from the UK on automated hiring systemscited by 38
  10. 2023Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitmentcited by 33
  11. 2020InsideBias: Measuring Bias in Deep Networks and Application to Face Gender Biometricscited by 31
  12. 2020Bias in Multimodal AI: Testbed for Fair Automatic Recruitmentcited by 21
  13. 2022The four-fifths rule is not disparate impact: a woeful tale of epistemic trespassing in algorithmic fairnesscited by 21
  14. 2019Algorithmic Discrimination: Formulation and Exploration in Deep Learning-based Face Biometricscited by 19
  15. 2019SensitiveNets: Learning Agnostic Representations with Application to Face Imagescited by 18
  16. 2023Leveraging Large Language Models for Topic Classification in the Domain of Public Affairscited by 13
  17. 2023Measuring Bias in AI Models: An Statistical Approach Introducing N-Sigmacited by 11
  18. 2020FairCVtest Demo: Understanding Bias in Multimodal Learning with a Testbed in Fair Automatic Recruitmentcited by 7
  19. 2020Learning Emotional-Blinded Face Representationscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 10.

13 papers.

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  1. 2025PBa-LLM: Privacy- and Bias-aware NLP using Named-Entity Recognition (NER)cited by 11
  2. 2025Privacy-Aware Detection of Fake Identity Documents: Methodology, Benchmark, and Improved Algorithms (FakeIDet2)cited by 7
  3. 2026Is My Vision-Language Data in Your AI? Membership Inference Test (MINT) Demo 2cited by 7
  4. 2026Auditing Training Data in Domain-adapted LLMs: LoRA-MINTcited by 4
  5. 2026Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyondcited by 4
  6. 2025Active Membership Inference Test (aMINT): Enhancing Model Auditability with Multi-Task Learningcited by 3
  7. 2026AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanationscited by 3
  8. 2026Comparative Study of Domain-adapted VLMs for General Document Visual Question Answeringcited by 3
  9. 2026Unraveling Machine Behavior by Multi-Level Bias Analysis and Detection: Methodology and Application to Computer Visioncited by 3
  10. 2026Overview of the TalentCLEF 2026: Skill and Job Title Intelligence for Human Capital Managementcited by 0
  11. 2026Evaluating and Mitigating Gender Bias in Pre-trained Embeddings for ML-based Recruitmentcited by 0
  12. 2026AIriskEval-edu Demo: Auditing of Pedagogical Risks in Educational Explanationscited by 0
1 more
  1. 2026inLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMscited by 0