Decision Intelligence and Visual Analytics Minitrack
03 - 06 january 2024
Hawaiian Village, Waikiki
Submission Deadline: 15 June 2023
Further information for authors: hicss.hawaii.edu/authors/
This minitrack seeks submissions that discuss how to augment human reasoning and decision making through interactive data visualization coupled with statistical and machine learning processes. This Hybrid Intelligence approach has applications in a broad range of situations where human expertise must be brought to bear on problems characterized by complex causal models, massive datasets, and data that are uncertain in fact, relevance, location in space and position in time. Current applications include environmental science and technologies, natural resources and energy, health and related life sciences, precision medicine, safety and security and business processes. Visual analytic environments have been widely used for pandemic policy making, response planning and execution. There has also been a growth in visual decision making environments to analyze supply chain risks, climate resiliency and adaptation.
Submissions are encouraged that focus on technological and sociotechnical approaches to support individual and collaborative analysis and decision making in organizations. Core issues of theory and methods for decision intelligence, collaborative work, data visualization, analytics, and knowledge integration in organizations. Case studies of applications of these methods to new analytic and decision making tasks in science and technology, public health, business intelligence, financial analysis, social sciences, and other domains are particularly welcome. Submissions may include studies of visual analytics and decision intelligence in the context of an organization (e.g., business planning, communication with analysts and policy-makers), perceptual and cognitive aspects of graphical visualization environments in the context of cognitive tasks, Interactive Machine Learning, and collaborative analysis using visual information systems. Additionally, submissions may include methods for understandable, trustable AI as well as human-guided AI to round out the problem-solving process. Emphasis will be given to submissions that use visual analytics for social change discovery, analysis, communication, and focus on mixed-initiative human/AI analysis.
Topics for this minitrack include, but are not limited to:
- Decision Intelligence approaches to computer augmented decision-making.
- Use of interactive visualization and visual analytics in in organizations.
- Applications of visual analytics.
- Visual analytics and visualization in “wicked” problem-solving in organizations.
- Analysis of datasets of varying size and complexity from archives and real-time streams
- Collaborative visual analysis and operational coordination within and across organizations.
- Interactive and visual risk-based decision making
- Interactive machine learning methods
- Managing response time of complex analytical tasks
- Effective deployment and case studies of success from deployed visualization and analytics experiences
- Visualization and analytics for data-driven policy making and decision support
- Issues and challenges in evaluation of visual decision making
- Mixed-initiative analysis methods for decision making
- Cognitive and social science aspects of visual decision-making environments
Authors are encouraged to bring the lens of their own background and expertise to discuss the complexities of advanced analytic and decision intelligence systems in organizations, coordination of multiple levels of analysis, decision-making and operations and design and evaluation of effective communication with and among diverse stakeholders. We invite computational, cognitive, and organizational perspectives on advanced data processing and interactive visualization for analysis and decision-making across a range of human endeavors. We also invite participation from researchers who are looking at scaling issues and multiscale issues, whether these scales refer to the time of decision making, the form-factor and operational constraints of mobile devices, the number of decision makers or the more traditional notion of multiscale simulation and real-world scales of data. We are particularly interested in approaches that combine computational and interactive analytics in “mixed initiative” or Interactive Machine Learning systems, decision support in the context of an organization (e.g. communication between analysts and policy-makers), perceptual and cognitive aspects of the analytic task, and collaborative analysis using visual information systems.
David Ebert (Primary Contact)
University of Oklahoma
Simon Fraser University
University of Texas at Austin
Brian D. Fisher, Ph.D.
Professor | School of Interactive Arts and Technology
Faculty of Communication, Art and Technology | Simon Fraser University
Rm. 7475 | 13450-102 Avenue, Surrey BC, V3T 0A3
SFU Research: https://tinyurl.com/2s3kmjcd
Department Page: http://www.sfu.ca/siat/people/faculty/brian-fisher.html