# Dbias: Detecting biases and ensuring Fairness in news articles

arXiv: https://arxiv.org/abs/2208.05777 · HTML: https://mlsanity.com/paper/2208.05777/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2022.

## Built on by (most cited first, 28 of 28)

- [VISAR: A Human-AI Argumentative Writing Assistant with Visual Programming and Rapid Draft Prototyping](https://mlsanity.com/paper/2304.07810/) (2023)
- [NBIAS: A Natural Language Processing Framework for Bias Identification in Text](https://mlsanity.com/paper/2308.01681/) (2023)
- [Recent Developments in Recommender Systems: A Survey](https://mlsanity.com/paper/2306.12680/) (2023)
- [Towards Auditing Large Language Models: Improving Text-based Stereotype Detection](https://mlsanity.com/paper/2311.14126/) (2023)
- [The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias](https://mlsanity.com/paper/2312.16148/) (2023)
- [FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?](https://mlsanity.com/paper/2401.11033/) (2024)
- [Fairness in Machine Learning meets with Equity in Healthcare](https://mlsanity.com/paper/2305.07041/) (2023)
- [Large Language Models' Detection of Political Orientation in Newspapers](https://mlsanity.com/paper/2406.00018/) (2024)
- [Bias-Aware Agent: Enhancing Fairness in AI-Driven Knowledge Retrieval](https://mlsanity.com/paper/2503.21237/) (2025)
- [Political Alignment in Large Language Models: A Multidimensional Audit of Psychometric Identity and Behavioral Bias](https://mlsanity.com/paper/2601.06194/) (2026)
- [Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysis](https://mlsanity.com/paper/2310.00347/) (2023)
- [A Revisit of Fake News Dataset with Augmented Fact-checking by ChatGPT](https://mlsanity.com/paper/2312.11870/) (2023)
- [MAGPIE: Multi-Task Media-Bias Analysis Generalization for Pre-Trained Identification of Expressions](https://mlsanity.com/paper/2403.07910/) (2024)
- [A Catalog of Fairness-Aware Practices in Machine Learning Engineering](https://mlsanity.com/paper/2408.16683/) (2024)
- [Neutralizing the Narrative: AI-Powered Debiasing of Online News Articles](https://mlsanity.com/paper/2504.03520/) (2025)
- [Stereotype Detection in LLMs: A Multiclass, Explainable, and Benchmark-Driven Approach](https://mlsanity.com/paper/2404.01768/) (2024)
- [Mitigating Bias in Conversations: A Hate Speech Classifier and Debiaser with Prompts](https://mlsanity.com/paper/2307.10213/) (2023)
- [Identifying Fairness Issues in Automatically Generated Testing Content](https://mlsanity.com/paper/2404.15104/) (2024)
- [Revealing Hidden Bias in AI: Lessons from Large Language Models](https://mlsanity.com/paper/2410.16927/) (2024)
- [FairSense-AI: Responsible AI Meets Sustainability](https://mlsanity.com/paper/2503.02865/) (2025)
- [Bias Mitigation Agent: Optimizing Source Selection for Fair and Balanced Knowledge Retrieval](https://mlsanity.com/paper/2508.18724/) (2025)
- [DocNet: Semantic Structure in Inductive Bias Detection Models](https://mlsanity.com/paper/2406.10965/) (2024)
- [Epistemological Bias As a Means for the Automated Detection of Injustices in Text](https://mlsanity.com/paper/2407.06098/) (2024)
- [Intertwined Biases Across Social Media Spheres: Unpacking Correlations in Media Bias Dimensions](https://mlsanity.com/paper/2408.15406/) (2024)
- [Social Media Informatics for Sustainable Cities and Societies: An Overview of the Applications, associated Challenges, and Potential Solutions](https://mlsanity.com/paper/2412.03600/) (2024)
- [Textual Data Bias Detection and Mitigation -- An Extensible Pipeline with Experimental Evaluation](https://mlsanity.com/paper/2512.10734/) (2025)
- [Balancing Domestic and Global Perspectives: Evaluating Dual-Calibration and LLM-Generated Nudges for Diverse News Recommendation](https://mlsanity.com/paper/2603.05780/) (2026)
- [A Framework for Identifying, Categorizing, and Explaining Bias in AI-Generated Code](https://mlsanity.com/paper/2609.30642/) (2026)

## Abstract (arXiv)

Because of the increasing use of data-centric systems and algorithms in machine learning, the topic of fairness is receiving a lot of attention in the academic and broader literature. This paper introduces Dbias (https://pypi.org/project/Dbias/), an open-source Python package for ensuring fairness in news articles. Dbias can take any text to determine if it is biased. Then, it detects biased words in the text, masks them, and suggests a set of sentences with new words that are bias-free or at least less biased. We conduct extensive experiments to assess the performance of Dbias. To see how well our approach works, we compare it to the existing fairness models. We also test the individual components of Dbias to see how effective they are. The experimental results show that Dbias outperforms all the baselines in terms of accuracy and fairness. We make this package (Dbias) as publicly available for the developers and practitioners to mitigate biases in textual data (such as news articles), as well as to encourage extension of this work.