# Explicit Bias Discovery in Visual Question Answering Models

arXiv: https://arxiv.org/abs/1811.07789 · HTML: https://mlsanity.com/paper/1811.07789/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2018.

## Built on by (most cited first, 30 of 42)

- [Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search](https://mlsanity.com/paper/2109.05433/) (2021)
- [Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Models](https://mlsanity.com/paper/2103.06922/) (2021)
- [Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering](https://mlsanity.com/paper/2104.03149/) (2021)
- [A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram](https://mlsanity.com/paper/2302.11097/) (2023)
- [Discover and Mitigate Unknown Biases with Debiasing Alternate Networks](https://mlsanity.com/paper/2207.10077/) (2022)
- [Discover the Unknown Biased Attribute of an Image Classifier](https://mlsanity.com/paper/2104.14556/) (2021)
- [InjecGuard: Benchmarking and Mitigating Over-defense in Prompt Injection Guardrail Models](https://mlsanity.com/paper/2410.22770/) (2024)
- [Gender and Racial Bias in Visual Question Answering Datasets](https://mlsanity.com/paper/2205.08148/) (2022)
- [SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering](https://mlsanity.com/paper/2204.02285/) (2022)
- [Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA](https://mlsanity.com/paper/2210.04692/) (2022)
- [VisQA: X-raying Vision and Language Reasoning in Transformers](https://mlsanity.com/paper/2104.00926/) (2021)
- [MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding](https://mlsanity.com/paper/2112.10728/) (2021)
- [Ethical Considerations for Responsible Data Curation](https://mlsanity.com/paper/2302.03629/) (2023)
- [Hierarchical Memory Learning for Fine-Grained Scene Graph Generation](https://mlsanity.com/paper/2203.06907/) (2022)
- [Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images](https://mlsanity.com/paper/2110.00519/) (2021)
- [Fuse, Reason and Verify: Geometry Problem Solving with Parsed Clauses from Diagram](https://mlsanity.com/paper/2407.07327/) (2024)
- [Fairness and Bias Mitigation in Computer Vision: A Survey](https://mlsanity.com/paper/2408.02464/) (2024)
- [REX: Reasoning-aware and Grounded Explanation](https://mlsanity.com/paper/2203.06107/) (2022)
- [CARETS: A Consistency And Robustness Evaluative Test Suite for VQA](https://mlsanity.com/paper/2203.07613/) (2022)
- [Navigating the Shortcut Maze: A Comprehensive Analysis of Shortcut Learning in Text Classification by Language Models](https://mlsanity.com/paper/2409.17455/) (2024)
- [Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents](https://mlsanity.com/paper/2203.02898/) (2022)
- [Chairs Can be Stood on: Overcoming Object Bias in Human-Object Interaction Detection](https://mlsanity.com/paper/2207.02400/) (2022)
- [Combo of Thinking and Observing for Outside-Knowledge VQA](https://mlsanity.com/paper/2305.06407/) (2023)
- [A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future](https://mlsanity.com/paper/2412.14056/) (2024)
- [Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion](https://mlsanity.com/paper/2306.08889/) (2023)
- [Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond](https://mlsanity.com/paper/2310.14670/) (2023)
- [Raising context awareness in motion forecasting](https://mlsanity.com/paper/2109.08048/) (2021)
- [Understanding ME? Multimodal Evaluation for Fine-grained Visual Commonsense](https://mlsanity.com/paper/2211.05895/) (2022)
- [On the Role of Visual Grounding in VQA](https://mlsanity.com/paper/2406.18253/) (2024)
- [iReason: Multimodal Commonsense Reasoning using Videos and Natural Language with Interpretability](https://mlsanity.com/paper/2107.10300/) (2021)

## Abstract (arXiv)

Researchers have observed that Visual Question Answering (VQA) models tend to answer questions by learning statistical biases in the data. For example, their answer to the question "What is the color of the grass?" is usually "Green", whereas a question like "What is the title of the book?" cannot be answered by inferring statistical biases. It is of interest to the community to explicitly discover such biases, both for understanding the behavior of such models, and towards debugging them. Our work address this problem. In a database, we store the words of the question, answer and visual words corresponding to regions of interest in attention maps. By running simple rule mining algorithms on this database, we discover human-interpretable rules which give us unique insight into the behavior of such models. Our results also show examples of unusual behaviors learned by models in attempting VQA tasks.