-------- Forwarded Message -------- Subject: [AISWorld] HICSS 56: “Explainable Artificial Intelligence (XAI)” mini-track with publication opportunity in Information Systems Management (ISM). Date: Fri, 18 Mar 2022 15:16:52 +1100 From: Babak Abedin babak.abedin@gmail.com To: aisworld@lists.aisnet.org
*Call for Papers: “Explainable Artificial Intelligence (XAI)” Minitrack at the 56th Hawaii International Conference on System Sciences (HICSS)*
*********************************************************************
The use of Artificial Intelligence (AI) in the context of decision analytics and service science has received significant attention in academia and practice alike. Yet, much of the current efforts have focused on advancing underlying algorithms and not on decreasing the complexity of AI systems. AI systems are still “black boxes” that are difficult to comprehend—not only for developers, but particularly for users and decision-makers (Meske et al. 2022). In addition, the development and use of AI is associated with many risks and pitfalls like biases in data or predictions based on spurious correlations (“Clever Hans” phenomena) (Lapuschkin et al. 2019), which eventually may lead to malfunctioning or biased AI and hence technologically driven discrimination.
This is where research on Explainable Artificial Intelligence (XAI) comes in. Also referred to as “transparent,” “interpretable,” or “understandable AI”, XAI aims to “produce explainable models, while maintaining a high level of learning performance (prediction accuracy); and enable human users to understand, appropriately, trust, and effectively manage the emerging generation of artificially intelligent partners” (DARPA 2017). XAI hence refers to “the movement, initiatives, and efforts made in response to AI transparency and trust concerns, more than to a formal technical concept” (Adadi and Berrada 2018, p. 52140). XAI is designed user-centric in that users are empowered to scrutinize and appropriately trust AI (Förster et al. 2020), eventually impacting task performance of users (Kühl et al. 2019).
With a focus on decision support, this minitrack aims to explore and extend research on how to establish explainability of intelligent black box systems—machine learning-based or not. We especially look for contributions that investigate XAI from either a developer’s or user’s perspective. We invite submissions from all application domains, such as healthcare, finance, e-commerce, retail, public administration or others. Technically and method-oriented studies, case studies as well as design science or behavioral science approaches are welcome.
Topics of interest include, but are not limited to:
· The users’ perspective on XAI
o Theorizing XAI-human interactions
o Presentation and personalization of AI explanations for different target groups
o XAI to increase situational awareness, compliance behavior and task performance
o XAI for transparency and unbiased decision making
o Impact of explainability on AI-based decision support systems use and adoption
o Explainability of AI in crisis situations
o Potential harm of explainability in AI
o Identifying user-centric requirements for XAI systems
· The developers’ perspective on XAI
o XAI to open, control and evaluate black box algorithms
o Using XAI to identify bias in data
o Explainability and Human-in-the-Loop development of AI
o XAI to support interactive machine learning
o Prevention and detection of deceptive AI explanations
o XAI to discover deep knowledge and learn from AI
o Designing and deploying XAI systems
o Addressing user-centric requirements for XAI systems
· The governments’ perspective on XAI
o XAI and compliance
o Explainability and transparency policy guidelines
o Evidence base benefits and challenges of XAI expectations and implementations
*Submission Deadline: *
June 15th, 2022
Further information for authors: https://hicss.hawaii.edu/authors/
*Fast track:*
We provide the opportunity for the (extended) best paper of this minitrack to be fast-tracked to the journal Information Systems Management (ISM)
*Minitrack Co-Chairs: *
Christian Meske
Ruhr-Universität Bochum
Babak Abedin
Macquarie University
Mathias Klier
University of Ulm
Fethi Rabhi
University of New South Wales _______________________________________________ AISWorld mailing list AISWorld@lists.aisnet.org