# Addressing Biases in the Texts using an End-to-End Pipeline Approach

arXiv: https://arxiv.org/abs/2303.07024 · HTML: https://mlsanity.com/paper/2303.07024/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 13 of 13)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification](https://mlsanity.com/paper/1903.04561/) (2019)
- [Aequitas: A Bias and Fairness Audit Toolkit](https://mlsanity.com/paper/1811.05577/) (2018)
- [Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees](https://mlsanity.com/paper/1806.06055/) (2018)
- [On the Fairness of Disentangled Representations](https://mlsanity.com/paper/1905.13662/) (2019)
- [word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method](https://mlsanity.com/paper/1402.3722/) (2014)
- [A Survey on the Fairness of Recommender Systems](https://mlsanity.com/paper/2206.03761/) (2022)
- [Fairness GAN](https://mlsanity.com/paper/1805.09910/) (2018)
- [Themis-ml: A Fairness-aware Machine Learning Interface for End-to-end Discrimination Discovery and Mitigation](https://mlsanity.com/paper/1710.06921/) (2017)
- [Dictionary-based Debiasing of Pre-trained Word Embeddings](https://mlsanity.com/paper/2101.09525/) (2021)

## Built on by (most cited first, 1 of 1)

- [Textual Data Bias Detection and Mitigation -- An Extensible Pipeline with Experimental Evaluation](https://mlsanity.com/paper/2512.10734/) (2025)

## Abstract (arXiv)

The concept of fairness is gaining popularity in academia and industry. Social media is especially vulnerable to media biases and toxic language and comments. We propose a fair ML pipeline that takes a text as input and determines whether it contains biases and toxic content. Then, based on pre-trained word embeddings, it suggests a set of new words by substituting the bi-ased words, the idea is to lessen the effects of those biases by replacing them with alternative words. We compare our approach to existing fairness models to determine its effectiveness. The results show that our proposed pipeline can de-tect, identify, and mitigate biases in social media data