# Explainable AI improves task performance in human-AI collaboration

arXiv: https://arxiv.org/abs/2406.08271 · HTML: https://mlsanity.com/paper/2406.08271/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2024.

## Built on by (most cited first, 12 of 12)

- [Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration](https://mlsanity.com/paper/2409.12809/) (2024)
- [Counterfactual Simulatability of LLM Explanations for Generation Tasks](https://mlsanity.com/paper/2505.21740/) (2025)
- [Clinicians' Voice: Fundamental Considerations for XAI in Healthcare](https://mlsanity.com/paper/2411.04855/) (2024)
- [Comparative Explanations: Explanation Guided Decision Making for Human-in-the-Loop Preference Selection](https://mlsanity.com/paper/2504.03744/) (2025)
- [The Epistemological Consequences of Large Language Models: Rethinking collective intelligence and institutional knowledge](https://mlsanity.com/paper/2512.19570/) (2025)
- [Seeing Isn't Always Believing: Analysis of Grad-CAM Faithfulness and Localization Reliability in Lung Cancer CT Classification](https://mlsanity.com/paper/2601.12826/) (2026)
- [HiLSVA: Design and Evaluation of a Human-in-the-Loop Agentic System for Scientific Visualization](https://mlsanity.com/paper/2606.26614/) (2026)
- [Are Concept Bottleneck Models Effective as Decision-Support Systems?](https://mlsanity.com/paper/2608.25581/) (2026)
- [In defence of post-hoc explanations in medical AI](https://mlsanity.com/paper/2504.20741/) (2025)
- [The Impact of AI Usage and Informativeness on Skill Development in Logical Reasoning](https://mlsanity.com/paper/2605.21695/) (2026)
- [Human-Centered Human-AI Collaboration (HCHAC)](https://mlsanity.com/paper/2505.22477/) (2025)
- [AI Evaluation Should Work With Humans](https://mlsanity.com/paper/2608.13577/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human-AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remains opaque. This makes it difficult for humans to validate a prediction made by AI against their own domain knowledge. For this reason, we hypothesize that augmenting humans with explainable AI as a decision aid improves task performance in human-AI collaboration. To test this hypothesis, we analyze the effect of augmenting domain experts with explainable AI in the form of visual heatmaps. We then compare participants that were either supported by (a) black-box AI or (b) explainable AI, where the latter supports them to follow AI predictions when the AI is accurate or overrule the AI when the AI predictions are wrong. We conducted two preregistered experiments with representative, real-world visual inspection tasks from manufacturing and medicine. The first experiment was conducted with factory workers from an electronics factory, who performed N=9,600 assessments of whether electronic products have defects. The second experiment was conducted with radiologists, who performed N=5,650 assessments of chest X-ray images to identify lung lesions. The results of our experiments with domain experts performing real-world tasks show that task performance improves when participants are supported by explainable AI instead of black-box AI. For example, in the manufacturing setting, we find that augmenting participants with explainable AI (as opposed to black-box AI) leads to a five-fold decrease in the median error rate of human decisions, which gives a significant improvement in task performance.