mlsanityData to 7 Oct 2026

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

arXiv 2102.05460 ↗First posted 10 Feb 2021cs.HC cs.AIcited by 9

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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.

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

Builds on

7 of 33 references.

References · 7

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  2. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  3. 2019Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Makingcited by 109
  4. 2020CheXplain: Enabling Physicians to Explore and UnderstandData-Driven, AI-Enabled Medical Imaging Analysiscited by 40
  5. 2020Evidence-based explanation to promote fairness in AI systemscited by 3
  6. 2019Mediation Challenges and Socio-Technical Gaps for Explainable Deep Learning Applicationscited by 2
  7. 2020Do ML Experts Discuss Explainability for AI Systems? A discussion case in the industry for a domain-specific solutioncited by 2

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Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 3; 2024: 5.

9 papers.