# FIND: Human-in-the-Loop Debugging Deep Text Classifiers

arXiv: https://arxiv.org/abs/2010.04987 · HTML: https://mlsanity.com/paper/2010.04987/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2020.

## Built on by (most cited first, 30 of 40)

- [Post-hoc Concept Bottleneck Models](https://mlsanity.com/paper/2205.15480/) (2022)
- [Explanation-Based Human Debugging of NLP Models: A Survey](https://mlsanity.com/paper/2104.15135/) (2021)
- [Leveraging Explanations in Interactive Machine Learning: An Overview](https://mlsanity.com/paper/2207.14526/) (2022)
- [Concept-level Debugging of Part-Prototype Networks](https://mlsanity.com/paper/2205.15769/) (2022)
- [Interactive Label Cleaning with Example-based Explanations](https://mlsanity.com/paper/2106.03922/) (2021)
- [Refining Language Models with Compositional Explanations](https://mlsanity.com/paper/2103.10415/) (2021)
- [A Rationale-Centric Framework for Human-in-the-loop Machine Learning](https://mlsanity.com/paper/2203.12918/) (2022)
- [Interactive Explanations by Conflict Resolution via Argumentative Exchanges](https://mlsanity.com/paper/2303.15022/) (2023)
- [Discovering Salient Neurons in Deep NLP Models](https://mlsanity.com/paper/2206.13288/) (2022)
- [Jointly Attacking Graph Neural Network and its Explanations](https://mlsanity.com/paper/2108.03388/) (2021)
- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [If Concept Bottlenecks are the Question, are Foundation Models the Answer?](https://mlsanity.com/paper/2504.19774/) (2025)
- [Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features](https://mlsanity.com/paper/2111.03740/) (2021)
- [Self-explaining deep models with logic rule reasoning](https://mlsanity.com/paper/2210.07024/) (2022)
- [Contestable AI needs Computational Argumentation](https://mlsanity.com/paper/2405.10729/) (2024)
- [Shortcuts and Identifiability in Concept-based Models from a Neuro-Symbolic Lens](https://mlsanity.com/paper/2502.11245/) (2025)
- [CREW: Facilitating Human-AI Teaming Research](https://mlsanity.com/paper/2408.00170/) (2024)
- [Towards Benchmarking the Utility of Explanations for Model Debugging](https://mlsanity.com/paper/2105.04505/) (2021)
- [XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models](https://mlsanity.com/paper/2210.16978/) (2022)
- [A Survey on Human-AI Collaboration with Large Foundation Models](https://mlsanity.com/paper/2403.04931/) (2024)
- [GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns](https://mlsanity.com/paper/2104.03958/) (2021)
- [Explainable Patterns for Distinction and Prediction of Moral Judgement on Reddit](https://mlsanity.com/paper/2201.11155/) (2022)
- [Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices)](https://mlsanity.com/paper/2205.09787/) (2022)
- [ER-Test: Evaluating Explanation Regularization Methods for Language Models](https://mlsanity.com/paper/2205.12542/) (2022)
- [May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainability](https://mlsanity.com/paper/2309.13965/) (2023)
- [Toward a Unified Framework for Debugging Concept-based Models](https://mlsanity.com/paper/2109.11160/) (2021)
- [Accelerating the Global Aggregation of Local Explanations](https://mlsanity.com/paper/2312.07991/) (2023)
- [Explaining the Road Not Taken](https://mlsanity.com/paper/2103.14973/) (2021)
- [Test-Time Fairness and Robustness in Large Language Models](https://mlsanity.com/paper/2406.07685/) (2024)
- [HumanAL: Calibrating Human Matching Beyond a Single Task](https://mlsanity.com/paper/2205.03209/) (2022)

## Abstract (arXiv)

Since obtaining a perfect training dataset (i.e., a dataset which is considerably large, unbiased, and well-representative of unseen cases) is hardly possible, many real-world text classifiers are trained on the available, yet imperfect, datasets. These classifiers are thus likely to have undesirable properties. For instance, they may have biases against some sub-populations or may not work effectively in the wild due to overfitting. In this paper, we propose FIND -- a framework which enables humans to debug deep learning text classifiers by disabling irrelevant hidden features. Experiments show that by using FIND, humans can improve CNN text classifiers which were trained under different types of imperfect datasets (including datasets with biases and datasets with dissimilar train-test distributions).