mlsanityData to 14 Oct 2026

NBIAS: A Natural Language Processing Framework for Bias Identification in Text

arXiv 2308.01681 ↗First posted 3 Aug 2023cs.CL cs.AIcited by 23

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Abstract (arXiv)

Bias in textual data can lead to skewed interpretations and outcomes when the data is used. These biases could perpetuate stereotypes, discrimination, or other forms of unfair treatment. An algorithm trained on biased data may end up making decisions that disproportionately impact a certain group of people. Therefore, it is crucial to detect and remove these biases to ensure the fair and ethical use of data. To this end, we develop a comprehensive and robust framework NBIAS that consists of four main layers: data, corpus construction, model development and an evaluation layer. The dataset is constructed by collecting diverse data from various domains, including social media, healthcare, and job hiring portals. As such, we applied a transformer-based token classification model that is able to identify bias words/ phrases through a unique named entity BIAS. In the evaluation procedure, we incorporate a blend of quantitative and qualitative measures to gauge the effectiveness of our models. We achieve accuracy improvements ranging from 1% to 8% compared to baselines. We are also able to generate a robust understanding of the model functioning. The proposed approach is applicable to a variety of biases and contributes to the fair and ethical use of textual data.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Builds on 35 of its 79 references that are in the map. First posted on arXiv 3 Aug 2023.

Builds on

35 of 79 references.

References · 35

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,312
  4. 2016Semantics derived automatically from language corpora contain human-like biasescited by 901
  5. 2015Named Entity Recognition with Bidirectional LSTM-CNNscited by 201
  6. 2020Social Biases in NLP Models as Barriers for Persons with Disabilitiescited by 182
  7. 2019Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddingscited by 147
  8. 2021MultiVerS: Improving scientific claim verification with weak supervision and full-document contextcited by 143
  9. 2021A Unified Generative Framework for Various NER Subtaskscited by 141
  10. 2023M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detectioncited by 121
  11. 2019Automatically Neutralizing Subjective Bias in Textcited by 105
  12. 2021On Measures of Biases and Harms in NLPcited by 81
23 more
  1. 2019TENER: Adapting Transformer Encoder for Named Entity Recognitioncited by 69
  2. 2018Few-shot classification in Named Entity Recognition Taskcited by 64
  3. 2022Theories of "Gender" in NLP Bias Researchcited by 62
  4. 2022Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Expertscited by 56
  5. 2018Multimodal Named Entity Recognition for Short Social Media Postscited by 43
  6. 2022T-NER: An All-Round Python Library for Transformer-based Named Entity Recognitioncited by 37
  7. 2019Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yor`ub'a and Twicited by 33
  8. 2021Unpacking the Interdependent Systems of Discrimination: Ableist Bias in NLP Systems through an Intersectional Lenscited by 30
  9. 2022Dbias: Detecting biases and ensuring Fairness in news articlescited by 28
  10. 2019Semi-Supervised Learning with Scarce Annotationscited by 25
  11. 2020Named Entity Recognition for Social Media Texts with Semantic Augmentationcited by 23
  12. 2020Biomedical named entity recognition using BERT in the machine reading comprehension frameworkcited by 22
  13. 2022Decomposed Meta-Learning for Few-Shot Named Entity Recognitioncited by 22
  14. 2023An Annotated Dataset for Explainable Interpersonal Risk Factors of Mental Disturbance in Social Media Postscited by 16
  15. 2021Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preservingcited by 11
  16. 2021Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognitioncited by 10
  17. 2023How Far Can It Go?: On Intrinsic Gender Bias Mitigation for Text Classificationcited by 10
  18. 2021Combating Unknown Bias with Effective Bias-Conflicting Scoring and Gradient Alignmentcited by 8
  19. 2021Marked Attribute Bias in Natural Language Inferencecited by 6
  20. 2022How people talk about each other: Modeling Generalized Intergroup Bias and Emotioncited by 5
  21. 2022Power of Explanations: Towards automatic debiasing in hate speech detectioncited by 5
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  23. 2019Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolutioncited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 11; 2025: 6; 2026: 3.

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  1. 2024Akal Badi ya Bias: An Exploratory Study of Gender Bias in Hindi Language Technologycited by 17
  2. 2024MBIAS: Mitigating Bias in Large Language Models While Retaining Contextcited by 15
  3. 2026Quantifying and Mitigating Self-Preference Bias of LLM Judgescited by 10
  4. 2024FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?cited by 9
  5. 2024Fake News Detection: Comparative Evaluation of BERT-like Models and Large Language Models with Generative AI-Annotated Datacited by 5
  6. 2023Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysiscited by 4
  7. 2024Adaptable and Reliable Text Classification using Large Language Modelscited by 4
  8. 2024FakeWatch: A Framework for Detecting Fake News to Ensure Credible Electionscited by 3
  9. 2024BEADs: Bias Evaluation Across Domainscited by 3
  10. 2023FakeWatch ElectionShield: A Benchmarking Framework to Detect Fake News for Credible US Electionscited by 2
  11. 2024REFINE-LM: Mitigating Language Model Stereotypes via Reinforcement Learningcited by 2
  12. 2024Desert Camels and Oil Sheikhs: Arab-Centric Red Teaming of Frontier LLMscited by 1
11 more
  1. 2025FairSense-AI: Responsible AI Meets Sustainabilitycited by 1
  2. 2025Evaluating LLMs for Demographic-Targeted Social Bias Detection: A Comprehensive Benchmark Studycited by 1
  3. 2025AI Text Detectors and the Misclassification of Slightly Polished Arabic Textcited by 1
  4. 2023She had Cobalt Blue Eyes: Prompt Testing to Create Aligned and Sustainable Language Modelscited by 0
  5. 2024Towards Fairer Health Recommendations: finding informative unbiased samples via Word Sense Disambiguationcited by 0
  6. 2024Responsible AI in NLP: GUS-Net Span-Level Bias Detection Dataset and Benchmark for Generalizations, Unfairness, and Stereotypescited by 0
  7. 2025Cross-cultural value alignment frameworks for responsible AI governance: Evidence from China-West comparative analysiscited by 0
  8. 2025Textual Data Bias Detection and Mitigation -- An Extensible Pipeline with Experimental Evaluationcited by 0
  9. 2025A Multifaceted Analysis of Social Biases in Large Language Modelscited by 0
  10. 2026Cross-Lingual Probing and Community-Grounded Analysis of Gender Bias in Low-Resource Bengalicited by 0
  11. 2026Investigating Disability Representations in Text-to-Image Modelscited by 0