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AMCIS-AI Mini Track 2023 : Call for papers- AMCIS 2023 Mini-track: Revolutionizing the Design of AI Models for Real Impact | |||||||||||||
Link: https://new.precisionconference.com | |||||||||||||
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Call For Papers | |||||||||||||
Call for papers- AMCIS 2023 Mini-track: Revolutionizing the Design of AI Models for Real Impact
Despite the rapid advancement of AI, integrating AI models with business processes remains one of the most challenging tasks in the current world of Information Technology. Various industry partners (e.g., Gartner's) reported that 80% to 90% of AI projects ended up with PowerPoint slides and never made it into production. Such failures may be attributable to the fact that AI models are not tailored to the problem or integrated with the processes and technologies of the production environment. Hence, IS researchers need to study the design and impact of AI models to create substantive value for organizations and societies. The new and exciting research topics would significantly extend our current theories, methodologies, and empirical insights that bring AI models closer to business and societal problems. We welcome submissions from a breadth of research paradigms, including behavioral, economics, design science, and data science. Our mini-track invites submissions related to the following two main themes: Designing AI Models: Topics include novel AI, machine learning, and deep learning methods for business and societal problems, designing scalable, explainable, fair, or unbiased AI models, Deployment & Impact of AI Models: Topics include challenges of measuring the value of AI models, best practices & success factors in turning AI models into AI products, federated learning, edge computing for AI models, the impacts of AI models on business decisions and processes (intended/ unintended consequences) All submissions must be made via the AMCIS 2023 PCS submission system (https://new.precisionconference.com/). Deadline: March 1st, 2023 Minitrack Chairs: Jingjing Li, jingjing.li@virginia.edu(mailto:jingjing.li@virginia.edu), University of Virginia Reza Mousavi, mousavi@virginia.edu(mailto:mousavi@virginia.edu), University of Virginia |
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