# BiasFinder: Metamorphic Test Generation to Uncover Bias for Sentiment Analysis Systems

arXiv: https://arxiv.org/abs/2102.01859 · HTML: https://mlsanity.com/paper/2102.01859/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2021.

## Built on by (most cited first, 24 of 24)

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- [METAL: Metamorphic Testing Framework for Analyzing Large-Language Model Qualities](https://mlsanity.com/paper/2312.06056/) (2023)
- [Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking](https://mlsanity.com/paper/2302.08018/) (2023)
- [The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis](https://mlsanity.com/paper/2310.12318/) (2023)
- [Fairness Testing of Large Language Models in Role-Playing](https://mlsanity.com/paper/2411.00585/) (2024)
- ["Oops, Did I Just Say That?" Testing and Repairing Unethical Suggestions of Large Language Models with Suggest-Critique-Reflect Process](https://mlsanity.com/paper/2305.02626/) (2023)
- [Towards an Enhanced Understanding of Bias in Pre-trained Neural Language Models: A Survey with Special Emphasis on Affective Bias](https://mlsanity.com/paper/2204.10365/) (2022)
- [How Emotionally Stable is ALBERT? Testing Robustness with Stochastic Weight Averaging on a Sentiment Analysis Task](https://mlsanity.com/paper/2111.09612/) (2021)
- [A Catalog of Fairness-Aware Practices in Machine Learning Engineering](https://mlsanity.com/paper/2408.16683/) (2024)
- [Do Existing Testing Tools Really Uncover Gender Bias in Text-to-Image Models?](https://mlsanity.com/paper/2501.15775/) (2025)
- [Language-Agnostic Bias Detection in Language Models with Bias Probing](https://mlsanity.com/paper/2305.13302/) (2023)
- [Gender Bias in Transformer Models: A comprehensive survey](https://mlsanity.com/paper/2306.10530/) (2023)
- [Efficient Fairness Testing in Large Language Models: Prioritizing Metamorphic Relations for Bias Detection](https://mlsanity.com/paper/2505.07870/) (2025)
- [ASDF: A Differential Testing Framework for Automatic Speech Recognition Systems](https://mlsanity.com/paper/2302.05582/) (2023)
- [Linguistic features for sentence difficulty prediction in ABSA](https://mlsanity.com/paper/2402.03163/) (2024)
- [Efficient DNN-Powered Software with Fair Sparse Models](https://mlsanity.com/paper/2407.02805/) (2024)
- [Automated Harmfulness Testing for Code Large Language Models](https://mlsanity.com/paper/2503.16740/) (2025)
- [MT4DP: Data Poisoning Attack Detection for DL-based Code Search Models via Metamorphic Testing](https://mlsanity.com/paper/2507.11092/) (2025)
- [Applications and Challenges of Fairness APIs in Machine Learning Software](https://mlsanity.com/paper/2508.16377/) (2025)
- [BTC-SAM: Leveraging LLMs for Generation of Bias Test Cases for Sentiment Analysis Models](https://mlsanity.com/paper/2509.24101/) (2025)
- [Towards a more efficient bias detection in financial language models](https://mlsanity.com/paper/2603.08267/) (2026)
- [Ethics Testing: Proactive Identification of Generative AI System Harms](https://mlsanity.com/paper/2604.22089/) (2026)
- [Specification-Based Testing of Large Language Models](https://mlsanity.com/paper/2610.14443/) (2026)

## Abstract (arXiv)

Artificial Intelligence (AI) software systems, such as Sentiment Analysis (SA) systems, typically learn from large amounts of data that may reflect human biases. Consequently, the machine learning model in such software systems may exhibit unintended demographic bias based on specific characteristics (e.g., gender, occupation, country-of-origin, etc.). Such biases manifest in an SA system when it predicts a different sentiment for similar texts that differ only in the characteristic of individuals described. Existing studies on revealing bias in SA systems rely on the production of sentences from a small set of short, predefined templates. To address this limitation, we present BisaFinder, an approach to discover biased predictions in SA systems via metamorphic testing. A key feature of BisaFinder is the automatic curation of suitable templates based on the pieces of text from a large corpus, using various Natural Language Processing (NLP) techniques to identify words that describe demographic characteristics. Next, BisaFinder instantiates new text from these templates by filling in placeholders with words associated with a class of a characteristic (e.g., gender-specific words such as female names, "she", "her"). These texts are used to tease out bias in an SA system. BisaFinder identifies a bias-uncovering test case when it detects that the SA system exhibits demographic bias for a pair of texts, i.e., it predicts a different sentiment for texts that differ only in words associated with a different class (e.g., male vs. female) of a target characteristic (e.g., gender). Our empirical evaluation showed that BiasFinder can effectively create a larger number of fluent and diverse test cases that uncover various biases in an SA system.