Designing for Collaborative Sensemaking: Using Expert & Non-Expert Crowd
Nitesh Goyal

TL;DR
This paper explores designing collaborative systems that enable expert and non-expert crowds to work together effectively for complex sensemaking tasks like crime solving, addressing challenges of bias, diverse perspectives, and workflow structure.
Contribution
It proposes a framework for enabling remote collaboration between experts and non-experts in complex sensemaking, scaling previous lab work to larger, diverse teams.
Findings
Identified key micro-tasks for effective collaboration.
Developed a workflow model for expert and non-expert integration.
Demonstrated improved problem-solving through collaborative sensemaking.
Abstract
Crime solving is a domain where solution discovery is often serendipitous. Unstructured mechanisms, like Reddit, for crime solving through crowds have failed so far. Mechanisms, collaborations, workflows, and micro-tasks necessary for successful crime solving might also vary across different crimes. Cognitively, while experts might have deeper domain knowledge, they might also fall prey to biased analysis. Non-experts, while lacking formal training, might instead offer non-conventional perspectives requiring direction. The analytical process is itself an iterative process of foraging and sensemaking. Users would explore to broaden solution space and narrow down to a solution iteratively until identifying the global maxima instead of local maxima. In this proposal, my research aims to design systems for enabling complex sensemaking tasks that require collaboration between remotely…
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Taxonomy
TopicsInnovative Approaches in Technology and Social Development · Team Dynamics and Performance · Information Systems Theories and Implementation
