Effects of Sensemaking Translucence on Distributed Collaborative Analysis

📝 Abstract

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.

💡 Analysis

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.

📄 Content

Effects of Sensemaking Translucence on Distributed Collaborative Analysis Nitesh Goyal Dept of Information Science Cornell University ngoyal@cs.cornell.edu Susan R. Fussell Dept of Information Science Cornell University sfussell@cornell.edu ABSTRACT 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. Author Keywords Implicit sharing; collaborative analysis; sensemaking.
ACM Classification Keywords H.5.3. Information interfaces and presentation (e.g., HCI): Groups and Organization Interfaces: Computer-supported cooperative work INTRODUCTION In March 2008, Demetrius Smith was charged for the murder of Robert Long. Even though Long was working with police as an informant and potential witness against his boss named Morales, police ignored Morales as a potential suspect. Despite Morales having motive, and opportunity, they decided to pursue incriminating Smith. It was not until April 2011 that the case was reopened because evidence pointed to racial information playing an important role in fake testimonies and police investigation. After serving a five year prison sentence, Smith was exonerated and released. The biased perception held by investigators hindered the process of sensemaking in two ways. First, the investigators should not only have collected evidence that confirmed their (wrong) hypothesis that Smith committed the crime, but also collected evidence that disconfirmed their hypothesis. Second, self-awareness of personal biases is hard. It is even harder in the process of complex sensemaking like crime analysis. In retrospect, awareness of biases might have afforded investigators the cognizance that their attention was prematurely focused on a single suspect instead of appropriately distributed across other suspects, including Morales. Thus, an absence of due process and transparency into one’s own mental process enabled biased sensemaking. Unfortunately the Smith case is not the only criminal case in which biases hinder sensemaking. Police Chief periodical reports that on average, 16 murders occur every day that might never be solved and their perpetrators never arrested because of reasons like confirmation biases and groupthink [43]. These issues may be exacerbated in cases where crime investigators in multiple agencies need to work together, due to reduced information sharing and awareness across geographically distributed teams and investigating partners [13, 37]. While the timely exchange of information is essential to successfully solving crimes, at the same time, information received from one analyst can unduly influence another’s reasoning, resulting in cognitive tunneling as in the Morales case.
In the current work, we focus on the notion of sensemaking translucence, or the process of making analysts more aware of their sensemaking processes. Sensemaking involves foraging for information pieces that could connect with each other, resulting in multiple initial hypotheses. These hypothesis are then closely synthesized to find evidence that confirms or disconfirms them, until an ultimate hypothesis remains [42]. Successful crime investigators pursue multiple suspects until they have sufficient information to rule out all but one of them, the correct one [30]. While, the sensemaking process can go wrong when information is not shared in a timely fashion, it can also go wrong when an analyst prematurely decides on a suspect without ruling out the others as in the case of Demetrius Smith.
To balance the need for information exchange with the goal of reducing cognitive biases, we propose a sensemaking translucence interface that consists of two integrated parts: a hypothesis window that is intended to motivate explicit interchange of ideas about suspects’ means, motives and Paste the appropriate copyright/license statement here. ACM now supports three different publication options: • ACM copyright: ACM holds the copyright on the work. This is the historical approach. • License: The author(s) retain copyright, but ACM receives an exclusive publication license. • Open Acce

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