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  1. When groups of people are tasked with making a judgment, the issue of uncertainty often arises. Existing methods to reduce uncertainty typically focus on iteratively improving specificity in the overall task instruction. However, uncertainty can arise from multiple sources, such as ambiguity of the item being judged due to limited context, or disagreements among the participants due to different perspectives and an under-specified task. A one-size-fits-all intervention may be ineffective if it is not targeted to the right source of uncertainty. In this paper we introduce a new workflow, Judgment Sieve, to reduce uncertainty in tasks involving group judgment in a targeted manner. By utilizing measurements that separate different sources of uncertainty during an initial round of judgment elicitation, we can then select a targeted intervention adding context or deliberation to most effectively reduce uncertainty on each item being judged. We test our approach on two tasks: rating word pair similarity and toxicity of online comments, showing that targeted interventions reduced uncertainty for the most uncertain cases. In the top 10% of cases, we saw an ambiguity reduction of 21.4% and 25.7%, and a disagreement reduction of 22.2% and 11.2% for the two tasks respectively. We also found through a simulation that our targeted approach reduced the average uncertainty scores for both sources of uncertainty as opposed to uniform approaches where reductions in average uncertainty from one source came with an increase for the other. 
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    Free, publicly-accessible full text available September 28, 2024
  2. Free, publicly-accessible full text available August 14, 2024
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    Browser extensions enhance the web experience and have seen great adoption from users in the past decade. At the same time, past research has shown that online trackers can use various techniques to infer the presence of installed extensions and abuse them to track users as well as uncover sensitive information about them. In this work we present a novel extension-fingerprinting vector showing how style modifications from browser extensions can be abused to identify installed extensions. We propose a pipeline that analyzes extensions both statically and dynamically and pinpoints their injected style sheets. Based on these, we craft a set of triggers that uniquely identify browser extensions from the context of the visited page. We analyzed 116K extensions from Chrome's Web Store and report that 6,645 of them inject style sheets on any website that users visit. Our pipeline has created triggers that uniquely identify 4,446 of these extensions, 1,074 (24%) of which could not be fingerprinted with previous techniques. Given the power of this new extension-fingerprinting vector, we propose specific countermeasures against style fingerprinting that have minimal impact on the overall user experience. 
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