# Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching

arXiv: https://arxiv.org/abs/2102.03919 · HTML: https://mlsanity.com/paper/2102.03919/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 73 references that are in the map.
- First posted on arXiv 7 Feb 2021.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [Counterfactual Visual Explanations](https://mlsanity.com/paper/1904.07451/) (2019)
- [Learning to Explain: An Information-Theoretic Perspective on Model Interpretation](https://mlsanity.com/paper/1802.07814/) (2018)
- [Dataset Distillation](https://mlsanity.com/paper/1811.10959/) (2018)
- [Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning](https://mlsanity.com/paper/1803.04765/) (2018)
- [Representer Point Selection for Explaining Deep Neural Networks](https://mlsanity.com/paper/1811.09720/) (2018)
- [Building machines that adapt and compute like brains](https://mlsanity.com/paper/1711.04203/) (2017)
- [The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification](https://mlsanity.com/paper/1503.01161/) (2015)
- [Generating Counterfactual Explanations with Natural Language](https://mlsanity.com/paper/1806.09809/) (2018)
- [Teaching Categories to Human Learners with Visual Explanations](https://mlsanity.com/paper/1802.06924/) (2018)
- [Firefly Monte Carlo: Exact MCMC with Subsets of Data](https://mlsanity.com/paper/1403.5693/) (2014)
- [People infer recursive visual concepts from just a few examples](https://mlsanity.com/paper/1904.08034/) (2019)
- [How Case Based Reasoning Explained Neural Networks: An XAI Survey of Post-Hoc Explanation-by-Example in ANN-CBR Twins](https://mlsanity.com/paper/1905.07186/) (2019)
- [Learning to Explain with Complemental Examples](https://mlsanity.com/paper/1812.01280/) (2018)
- [Optimal Cooperative Inference](https://mlsanity.com/paper/1705.08971/) (2017)
- [Infant directed speech is consistent with teaching](https://mlsanity.com/paper/1606.01175/) (2016)

## Built on by (most cited first, 10 of 10)

- [Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations](https://mlsanity.com/paper/2210.11584/) (2022)
- [A Psychological Theory of Explainability](https://mlsanity.com/paper/2205.08452/) (2022)
- [How explainable AI affects human performance: A systematic review of the behavioural consequences of saliency maps](https://mlsanity.com/paper/2404.16042/) (2024)
- [Explainable AI for medical imaging: Explaining pneumothorax diagnoses with Bayesian Teaching](https://mlsanity.com/paper/2106.04684/) (2021)
- [Explainable AI for Natural Adversarial Images](https://mlsanity.com/paper/2106.09106/) (2021)
- [When Redundancy Matters: Machine Teaching of Representations](https://mlsanity.com/paper/2401.12711/) (2024)
- [I-CEE: Tailoring Explanations of Image Classification Models to User Expertise](https://mlsanity.com/paper/2312.12102/) (2023)
- [Abstraction, Validation, and Generalization for Explainable Artificial Intelligence](https://mlsanity.com/paper/2105.07508/) (2021)
- [On a Combinatorial Problem Arising in Machine Teaching](https://mlsanity.com/paper/2402.04907/) (2024)
- [Teaching and Learning under Deductive Errors](https://mlsanity.com/paper/2605.13384/) (2026)

## Abstract (arXiv)

State-of-the-art deep-learning systems use decision rules that are challenging for humans to model. Explainable AI (XAI) attempts to improve human understanding but rarely accounts for how people typically reason about unfamiliar agents. We propose explicitly modeling the human explainee via Bayesian Teaching, which evaluates explanations by how much they shift explainees' inferences toward a desired goal. We assess Bayesian Teaching in a binary image classification task across a variety of contexts. Absent intervention, participants predict that the AI's classifications will match their own, but explanations generated by Bayesian Teaching improve their ability to predict the AI's judgements by moving them away from this prior belief. Bayesian Teaching further allows each case to be broken down into sub-examples (here saliency maps). These sub-examples complement whole examples by improving error detection for familiar categories, whereas whole examples help predict correct AI judgements of unfamiliar cases.