-------- Forwarded Message -------- Subject: [AISWorld] CFP: Artificial Intelligence Implications for IS Research Methods Minitrack Date: Sat, 11 Jul 2020 16:14:03 +0000 From: Rohit Nishant rohit.nishant@fsa.ulaval.ca To: aisworld@lists.aisnet.org aisworld@lists.aisnet.org

HICSS 54, Jan 5-8, 2021 Track: Organizational Systems and Technology

Minitrack: ARTIFICIAL INTELLIGENCE IMPLICATIONS FOR IS RESEARCH METHODS

This minitrack focuses on the integration of artificial intelligence (AI) tools in more traditional research methods, as well as the role of the IS field in developing new digital and automated research methods. IS research has witnessed evolutions of different methodologies and methodological paradigms at regular intervals. As upcoming progress in AI is expected to fundamentally transform the very nature of work in all fields, including academia, scholars will have to contemplate its potential integration in their research practices. Some AI-based features have already been implemented in academic research, notably for data selection, sample allocation, and text analytics. There are also machine learning based tools such as iris.ai available for literature review. In that respect, it becomes important to start to reflect on the potential and implications of AI with academic research, as well as our own readiness for the forthcoming integration of AI into our work. This minitrack covers issues related to the design, development, and application of AI-based features for academic research. AI-based features can include using Natural Language Processing (NLP) to review research papers, using automated data modeling tool for empirical analysis, and AI for identifying relationships inherent in a phenomenon. This minitrack also seeks to explore research opportunities and challenges associated with the automation of research, including epistemological, methodological, and ethical implications. Submissions may include research papers (theoretical and/or empirical), as well as design studies, literature reviews, and research commentaries. Topics of interest are related to at least five main themes associated to the integration of AI in research methods, and relevant to the IS community: Integration of AI into traditional research methods and development of new research methods, which will include · Better access to Big Data · Improvement of pattern recognition accuracy · Development of new NLP algorithms for text analysis · Improving predictive and prescriptive power of research models, therefore shifting focus from inferential statistics to prediction · New research directions and opportunities uncovered by AI capabilities including · Improved understanding of existing phenomenon · Exploration of new topics and phenomenon · Discovery of new relationships, hitherto unexplored previously · Quality and evaluation of AI-supported research, including · Integration of design principles to evaluate research tools · Development of new metrics to assess AI-supported research · Ethical considerations related to the use of AI in academic research · Role of the IS field in accompanying AI-based changes in academia Important Dates: • Paper Submission Deadline: July 15, 2020, 11:59 p.m. HST Minitrack Co-Chairs: Mathieu Templier (Primary Contact) Université Laval mathieu.templier@fsa.ulaval.camailto:mathieu.templier@fsa.ulaval.ca Rohit Nishant Université Laval rohit.nishant@fsa.ulaval.camailto:rohit.nishant@fsa.ulaval.ca

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