# Explain yourself! Effects of Explanations in Human-Robot Interaction

arXiv: https://arxiv.org/abs/2204.04501 · HTML: https://mlsanity.com/paper/2204.04501/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 71 references that are in the map.
- First posted on arXiv 9 Apr 2022.

## Builds on (most cited first, 13 of 13)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [Towards Explainable Artificial Intelligence](https://mlsanity.com/paper/1909.12072/) (2019)
- [Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences](https://mlsanity.com/paper/1712.00547/) (2017)
- [Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions](https://mlsanity.com/paper/1901.03729/) (2019)
- [Stakeholders in Explainable AI](https://mlsanity.com/paper/1810.00184/) (2018)
- [Explainable Autonomous Robots: A Survey and Perspective](https://mlsanity.com/paper/2105.02658/) (2021)
- [Modeling Trust in Human-Robot Interaction: A Survey](https://mlsanity.com/paper/2011.04796/) (2020)
- [Explainable Artificial Intelligence (XAI): An Engineering Perspective](https://mlsanity.com/paper/2101.03613/) (2021)
- [Trust Considerations for Explainable Robots: A Human Factors Perspective](https://mlsanity.com/paper/2005.05940/) (2020)
- [Warmth and Competence to Predict Human Preference of Robot Behavior in Physical Human-Robot Interaction](https://mlsanity.com/paper/2008.05799/) (2020)
- [At Most 43 Moves, At Least 29: Optimal Strategies and Bounds for Ultimate Tic-Tac-Toe](https://mlsanity.com/paper/2006.02353/) (2020)

## Built on by (most cited first, 2 of 2)

- [Towards Probabilistic Planning of Explanations for Robot Navigation](https://mlsanity.com/paper/2411.05022/) (2024)
- [Enhancing Robot Assistive Behaviour with Reinforcement Learning and Theory of Mind](https://mlsanity.com/paper/2411.07003/) (2024)

## Abstract (arXiv)

Recent developments in explainable artificial intelligence promise the potential to transform human-robot interaction: Explanations of robot decisions could affect user perceptions, justify their reliability, and increase trust. However, the effects on human perceptions of robots that explain their decisions have not been studied thoroughly. To analyze the effect of explainable robots, we conduct a study in which two simulated robots play a competitive board game. While one robot explains its moves, the other robot only announces them. Providing explanations for its actions was not sufficient to change the perceived competence, intelligence, likeability or safety ratings of the robot. However, the results show that the robot that explains its moves is perceived as more lively and human-like. This study demonstrates the need for and potential of explainable human-robot interaction and the wider assessment of its effects as a novel research direction.