# Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual Examples

arXiv: https://arxiv.org/abs/2001.02271 · HTML: https://mlsanity.com/paper/2001.02271/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques](https://mlsanity.com/paper/2103.01035/) (2021)
- [GAM Coach: Towards Interactive and User-centered Algorithmic Recourse](https://mlsanity.com/paper/2302.14165/) (2023)
- [On Explaining Proxy Discrimination and Unfairness in Individual Decisions Made by AI Systems](https://mlsanity.com/paper/2509.25662/) (2025)
- [Am I Being Treated Fairly? A Conceptual Framework for Individuals to Ascertain Fairness](https://mlsanity.com/paper/2504.02461/) (2025)

## Abstract (arXiv)

AI algorithms are not immune to biases. Traditionally, non-experts have little control in uncovering potential social bias (e.g., gender bias) in the algorithms that may impact their lives. We present a preliminary design for an interactive visualization tool CEB to reveal biases in a commonly used AI method, Neural Networks (NN). CEB combines counterfactual examples and abstraction of an NN decision process to empower non-experts to detect bias. This paper presents the design of CEB and initial findings of an expert panel (n=6) with AI, HCI, and Social science experts.