# LOGAN: Local Group Bias Detection by Clustering

arXiv: https://arxiv.org/abs/2010.02867 · HTML: https://mlsanity.com/paper/2010.02867/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [A Survey of Race, Racism, and Anti-Racism in NLP](https://mlsanity.com/paper/2106.11410/) (2021)
- [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022)
- [Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias](https://mlsanity.com/paper/2212.10678/) (2022)
- [Gender Bias in BERT -- Measuring and Analysing Biases through Sentiment Rating in a Realistic Downstream Classification Task](https://mlsanity.com/paper/2306.15298/) (2023)
- [Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification](https://mlsanity.com/paper/2106.10826/) (2021)
- [Unlearning with Fisher Masking](https://mlsanity.com/paper/2310.05331/) (2023)
- [Measuring Fairness of Text Classifiers via Prediction Sensitivity](https://mlsanity.com/paper/2203.08670/) (2022)
- [Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis](https://mlsanity.com/paper/2305.12709/) (2023)
- [Auditing Algorithmic Fairness in Machine Learning for Health with Severity-Based LOGAN](https://mlsanity.com/paper/2211.08742/) (2022)

## Abstract (arXiv)

Machine learning techniques have been widely used in natural language processing (NLP). However, as revealed by many recent studies, machine learning models often inherit and amplify the societal biases in data. Various metrics have been proposed to quantify biases in model predictions. In particular, several of them evaluate disparity in model performance between protected groups and advantaged groups in the test corpus. However, we argue that evaluating bias at the corpus level is not enough for understanding how biases are embedded in a model. In fact, a model with similar aggregated performance between different groups on the entire data may behave differently on instances in a local region. To analyze and detect such local bias, we propose LOGAN, a new bias detection technique based on clustering. Experiments on toxicity classification and object classification tasks show that LOGAN identifies bias in a local region and allows us to better analyze the biases in model predictions.