# Effects of Sensemaking Translucence on Distributed Collaborative Analysis

arXiv: https://arxiv.org/abs/1603.04931 · HTML: https://mlsanity.com/paper/1603.04931/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2016.

## Built on by (most cited first, 3 of 3)

- [Designing for Human-Agent Alignment: Understanding what humans want from their agents](https://mlsanity.com/paper/2404.04289/) (2024)
- [AI-Enhanced Sensemaking: Exploring the Design of a Generative AI-Based Assistant to Support Genetic Professionals](https://mlsanity.com/paper/2412.15444/) (2024)
- [Thinking in Graphs with CoMAP: A Shared Visual Workspace for Designing Project-Based Learning](https://mlsanity.com/paper/2604.06200/) (2026)

## Abstract (arXiv)

Collaborative sensemaking requires that analysts share their information and insights with each other, but this process of sharing runs the risks of prematurely focusing the investigation on specific suspects. To address this tension, we propose and test an interface for collaborative crime analysis that aims to make analysts more aware of their sensemaking processes. We compare our sensemaking translucence interface to a standard interface without special sensemaking features in a controlled laboratory study. We found that the sensemaking translucence interface significantly improved clue finding and crime solving performance, but that analysts rated the interface lower on subjective measures than the standard interface. We conclude that designing for distributed sensemaking requires balancing task performance vs. user experience and real-time information sharing vs. data accuracy.