-------- Forwarded Message --------
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)
Universiteģ Laval
mathieu.templier@fsa.ulaval.ca<mailto:mathieu.templier@fsa.ulaval.ca>
Rohit Nishant
Universiteģ Laval
rohit.nishant@fsa.ulaval.ca<mailto:rohit.nishant@fsa.ulaval.ca>
_______________________________________________
AISWorld mailing list
AISWorld@lists.aisnet.org