• [Behavioural impact, interaction with AI, user perception of AI] Robots vs. Machines: Identifying User Perceptions and Classifications, Kristin E. Schaefer D. 2012 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support, New Orleans, LA, 2012.


  • A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems. SINA MOHSENI and NILOOFAR ZAREI,Texas A&M UniversityERIC D. RAGAN,University of Florida. ACM Trans. Interact. Intell. Syst.1, 1, Article 1 (January 2020),46 pages. https://doi.org/10.1145/3387166

Explainable AI systems are intended to self-explain the reasoning behind system decisions and predictions.  This paper presents a survey and framework intended to share knowledge and experiences of Explainable AI design and evaluation methods across multiple disciplines. Aiming to support diverse design goals and evaluation methods in XAI research, after a thorough review of Explainable AI related papers in the fields of machine learning, visualization, and human-computer interaction, it presents a categorization of Explainable AI design goals and evaluation methods.