# Explainable AI for System Failures: Generating Explanations that Improve Human Assistance in Fault Recovery

arXiv: https://arxiv.org/abs/2011.09407 · HTML: https://mlsanity.com/paper/2011.09407/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Understanding and Avoiding AI Failures: A Practical Guide](https://mlsanity.com/paper/2104.12582/) (2021)

## Abstract (arXiv)

With the growing capabilities of intelligent systems, the integration of artificial intelligence (AI) and robots in everyday life is increasing. However, when interacting in such complex human environments, the failure of intelligent systems, such as robots, can be inevitable, requiring recovery assistance from users. In this work, we develop automated, natural language explanations for failures encountered during an AI agents' plan execution. These explanations are developed with a focus of helping non-expert users understand different point of failures to better provide recovery assistance. Specifically, we introduce a context-based information type for explanations that can both help non-expert users understand the underlying cause of a system failure, and select proper failure recoveries. Additionally, we extend an existing sequence-to-sequence methodology to automatically generate our context-based explanations. By doing so, we are able develop a model that can generalize context-based explanations over both different failure types and failure scenarios.