# The human-AI relationship in decision-making: AI explanation to support people on justifying their decisions

arXiv: https://arxiv.org/abs/2102.05460 · HTML: https://mlsanity.com/paper/2102.05460/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 10 Feb 2021.

## Builds on (most cited first, 7 of 7)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making](https://mlsanity.com/paper/1902.02960/) (2019)
- [CheXplain: Enabling Physicians to Explore and UnderstandData-Driven, AI-Enabled Medical Imaging Analysis](https://mlsanity.com/paper/2001.05149/) (2020)
- [Evidence-based explanation to promote fairness in AI systems](https://mlsanity.com/paper/2003.01525/) (2020)
- [Mediation Challenges and Socio-Technical Gaps for Explainable Deep Learning Applications](https://mlsanity.com/paper/1907.07178/) (2019)
- [Do ML Experts Discuss Explainability for AI Systems? A discussion case in the industry for a domain-specific solution](https://mlsanity.com/paper/2002.12450/) (2020)

## Built on by (most cited first, 9 of 9)

- [AI-enhanced Collective Intelligence](https://mlsanity.com/paper/2403.10433/) (2024)
- [Designing explainable artificial intelligence with active inference: A framework for transparent introspection and decision-making](https://mlsanity.com/paper/2306.04025/) (2023)
- [Human Delegation Behavior in Human-AI Collaboration: The Effect of Contextual Information](https://mlsanity.com/paper/2401.04729/) (2024)
- [Designer-User Communication for XAI: An epistemological approach to discuss XAI design](https://mlsanity.com/paper/2105.07804/) (2021)
- [Towards Non-Adversarial Algorithmic Recourse](https://mlsanity.com/paper/2403.10330/) (2024)
- [How much informative is your XAI? A decision-making assessment task to objectively measure the goodness of explanations](https://mlsanity.com/paper/2312.04379/) (2023)
- [Why Reinforcement Learning in Energy Systems Needs Explanations](https://mlsanity.com/paper/2405.18823/) (2024)
- [Can AI Mitigate Human Perceptual Biases? A Pilot Study](https://mlsanity.com/paper/2311.00706/) (2023)
- [Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks](https://mlsanity.com/paper/2411.10176/) (2024)

## Abstract (arXiv)

The explanation dimension of Artificial Intelligence (AI) based system has been a hot topic for the past years. Different communities have raised concerns about the increasing presence of AI in people's everyday tasks and how it can affect people's lives. There is a lot of research addressing the interpretability and transparency concepts of explainable AI (XAI), which are usually related to algorithms and Machine Learning (ML) models. But in decision-making scenarios, people need more awareness of how AI works and its outcomes to build a relationship with that system. Decision-makers usually need to justify their decision to others in different domains. If that decision is somehow based on or influenced by an AI-system outcome, the explanation about how the AI reached that result is key to building trust between AI and humans in decision-making scenarios. In this position paper, we discuss the role of XAI in decision-making scenarios, our vision of Decision-Making with AI-system in the loop, and explore one case from the literature about how XAI can impact people justifying their decisions, considering the importance of building the human-AI relationship for those scenarios.