mlsanityData to 7 Oct 2026

Designer-User Communication for XAI: An epistemological approach to discuss XAI design

arXiv 2105.07804 ↗First posted 17 May 2021cs.AI cs.HCcited by 2

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Abstract (arXiv)

Artificial Intelligence is becoming part of any technology we use nowadays. If the AI informs people's decisions, the explanation about AI's outcomes, results, and behavior becomes a necessary capability. However, the discussion of XAI features with various stakeholders is not a trivial task. Most of the available frameworks and methods for XAI focus on data scientists and ML developers as users. Our research is about XAI for end-users of AI systems. We argue that we need to discuss XAI early in the AI-system design process and with all stakeholders. In this work, we aimed at investigating how to operationalize the discussion about XAI scenarios and opportunities among designers and developers of AI and its end-users. We took the Signifying Message as our conceptual tool to structure and discuss XAI scenarios. We experiment with its use for the discussion of a healthcare AI-System.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 21 references that are in the map. First posted on arXiv 17 May 2021.

Builds on

9 of 21 references.

References · 9

  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. 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 251
  4. 2018Explaining Explanations in AIcited by 136
  5. 2017Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciencescited by 84
  6. 2018TED: Teaching AI to Explain its Decisionscited by 33
  7. 2021The human-AI relationship in decision-making: AI explanation to support people on justifying their decisionscited by 9
  8. 2019Mediation Challenges and Socio-Technical Gaps for Explainable Deep Learning Applicationscited by 2
  9. 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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Citing papers per year: 2022: 1; 2023: 0; 2024: 1.

2 papers.