Sensemaking Workshop: CHI 2018
About the Workshop
We are seeking workshop papers on topics in the area of sensemaking for a workshop to be held at CHI 2018 in Montreal, Canada.
Making sense of information is central to HCI as people look to understand complex systems, domains and problems. Broadly, we take the topic of sensemaking to mean understanding of how people collect and organize information for analysis and synthesis, and the tools and processes they follow when doing this.
Thus, sensemaking, per se, is everywhere in the systems we build and in the domains we study. Whenever people need to function well with data, making sense of the information is often a central task.
We are interested in both individual and group sensemaking practices—from how one person figures out where to go when visiting Montreal, up to large, collaborative group sensemaking where teams of people assemble and interpret large, complex, interlocking sets of data. Representative tasks include the practice of people who deal with sensemaking hand-offs (e.g., in a medical setting) or in analytical areas (e.g., making sense of financial data for forensic purposes)
In particular, what are the tools, techniques and best practices of people who need to make sense of a large amount of complex information? What issues of scale, complexity and coordination arise that are particular to making sense of a complex world?
Workshop Goals
- First, we will create working relationships between researchers whose work focuses on aspects of sensemaking. While we certainly hope to bring together those working within the HCI community, we would like to try to bring in some researchers from other disciplines as well, including Library & Information Science (LIS) and Organizational Theory and Psychology (e.g., cognitive/problem solving research).
- Our second outcome is to enrich our understanding of sensemaking activities. This includes striving for a shared understanding of the different notions of sensemaking, laying out and structuring the space of varieties of sensemaking (e.g., different levels of social aggregation, static vs. dynamic contexts), articulating their commonalities and differences.
- Our third goal is to draw from this is a greater understanding of design implications for improved sensemaking tools, systems and designs. There is a clearly emerging demand for tools for verifiability and trustability of facts shared on public media channels. For example, a new generation of tools is emerging to allow journalists to spot inaccurate or fake news by leveraging ML algorithms and visualizations. Can we take advantage of these tools in our everyday sensemaking tasks as well?