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1. Multiple-choice quizzes improve memory for misinformation debunks, but do not reduce belief in misinformation

   https://doi.org/10.1186/s41235-023-00488-9  

   Collier, Jessica R; Pillai, Raunak M; Fazio, Lisa K (December 2023, Cognitive Research: Principles and Implications)

   Fact-checkers want people to both read and remember their misinformation debunks. Retrieval practice is one way to increase memory, thus multiple-choice quizzes may be a useful tool for fact-checkers. We tested whether exposure to quizzes improved people’s accuracy ratings for fact-checked claims and their memory for specific information within a fact check. Across three experiments, 1551 US-based online participants viewed fact checks (either health- or politics-related) with or without a quiz. Overall, the fact checks were effective, and participants were more accurate in rating the claims after exposure. In addition, quizzes improved participants’ memory for the details of the fact checks, even 1 week later. However, that increased memory did not lead to more accurate beliefs. Participants’ accuracy ratings were similar in the quiz and no-quiz conditions. Multiple-choice quizzes can be a useful tool for increasing memory, but there is a disconnect between memory and belief. 

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2. The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism

   https://doi.org/10.1145/3287560.3287568  

   Goldenfein, Jake (January 2019, Proceedings of the 2019 ACM FAT* Conference)

   Computer vision and other biometrics data science applications have commenced a new project of profiling people. Rather than using 'transaction generated information', these systems measure the 'real world' and produce an assessment of the 'world state' - in this case an assessment of some individual trait. Instead of using proxies or scores to evaluate people, they increasingly deploy a logic of revealing the truth about reality and the people within it. While these profiling knowledge claims are sometimes tentative, they increasingly suggest that only through computation can these excesses of reality be captured and understood. This article explores the bases of those claims in the systems of measurement, representation, and classification deployed in computer vision. It asks if there is something new in this type of knowledge claim, sketches an account of a new form of computational empiricism being operationalised, and questions what kind of human subject is being constructed by these technological systems and practices. Finally, the article explores legal mechanisms for contesting the emergence of computational empiricism as the dominant knowledge platform for understanding the world and the people within it. 

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3. Identity Theft, Trust Breaches, and the Production of Economic Insecurity

   https://doi.org/10.1177/00031224231189895  

   Brensinger, Jordan (August 2023, American Sociological Review)

   Across various domains of social life, organizational reliance on personal data and exposure to unanticipated financial hardship have transformed Americans’ life chances and access to opportunities. This article examines an area where they intersect: the hardship caused by breakdowns in information systems. I focus on the case of identity theft, showing how that event—experienced by tens of millions of Americans annually—contributes to economic insecurity. To do so, I first develop a theory of insecurity that links feelings of precariousness to breaches of trust at three levels: interpersonal, organizational, and systemic. Drawing on an original qualitative study of identity theft resolution, I find that most victims worried about their financial lives because they could no longer count on certain people, organizations, or systems. Beneath this commonality, race and class informed feelings of insecurity and associated coping strategies following identity theft. Low-income people and people of color tended to direct suspicion at personal networks and report ending relationships and informal assistance. In contrast, middle- and upper-income and White individuals disproportionately blamed organizations and demanded their protection. These findings—along with the trust-based theory that helped make them visible—have important implications for the study of insecurity, inequality, and trust in the information age. 

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4. Towards Scalable and Dynamic Social Sensing Using A Distributed Computing Framework

   https://doi.org/10.1109/ICDCS.2017.196  

   Zhang, Daniel Yue; Zheng, Chao; Wang, Dong; Thain, Doug; Mu, Xin; Madey, Greg; Huang, Chao (June 2017, IEEE International Conference on Distributed Computing Systems)

   With the rapid growth of online social media and ubiquitous Internet connectivity, social sensing has emerged as a new crowdsourcing application paradigm of collecting observations (often called claims) about the physical environment from humans or devices on their behalf. A fundamental problem in social sensing applications lies in effectively ascertaining the correctness of claims and the reliability of data sources without knowing either of them a priori, which is referred to as truth discovery. While significant progress has been made to solve the truth discovery problem, some important challenges have not been well addressed yet. First, existing truth discovery solutions did not fully solve the dynamic truth discovery problem where the ground truth of claims changes over time. Second, many current solutions are not scalable to large-scale social sensing events because of the centralized nature of their truth discovery algorithms. Third, the heterogeneity and unpredictability of the social sensing data traffic pose additional challenges to the resource allocation and system responsiveness. In this paper, we developed a Scalable Streaming Truth Discovery (SSTD) solution to address the above challenges. In particular, we first developed a dynamic truth discovery scheme based on Hidden Markov Models (HMM) to effectively infer the evolving truth of reported claims. We further developed a distributed framework to imple- ment the dynamic truth discovery scheme using Work Queue in HTCondor system. We also integrated the SSTD scheme with an optimal workload allocation mechanism to dynamically allocate the resources (e.g., cores, memories) to the truth discovery tasks based on their computation requirements. We evaluated SSTD through real world social sensing applications using Twitter data feeds. The evaluation results on three real-world data traces (i.e., Boston Bombing, Paris Shooting and College Football) show that the SSTD scheme is scalable and outperforms the state-of-the- art truth discovery methods in terms of both effectiveness and efficiency. 

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5. Strategy-dependent effects of working-memory limitations on human perceptual decision-making

   https://doi.org/10.7554/eLife.73610  

   Schapiro, Kyra; Josić, Krešimir; Kilpatrick, Zachary P; Gold, Joshua I (March 2022, eLife)

   Working memory, the brain’s ability to temporarily store and recall information, is a critical part of decision making – but it has its limits. The brain can only store so much information, for so long. Since decisions are not often acted on immediately, information held in working memory ‘degrades’ over time. However, it is unknown whether or not this degradation of information over time affects the accuracy of later decisions. The tactics that people use, knowingly or otherwise, to store information in working memory also remain unclear. Do people store pieces of information such as numbers, objects and particular details? Or do they tend to compute that information, make some preliminary judgement and recall their verdict later? Does the strategy chosen impact people’s decision-making? To investigate, Schapiro et al. devised a series of experiments to test whether the limitations of working memory, and how people store information, affect the accuracy of decisions they make. First, participants were shown an array of colored discs on a screen. Then, either immediately after seeing the disks or a few seconds later, the participants were asked to recall the position of one of the disks they had seen, or the average position of all the disks. This measured how much information degraded for a decision based on multiple items, and how much for a decision based on a single item. From this, the method of information storage used to make a decision could be inferred. Schapiro et al. found that the accuracy of people’s responses worsened over time, whether they remembered the position of each individual disk, or computed their average location before responding. The greater the delay between seeing the disks and reporting their location, the less accurate people’s responses tended to be. Similarly, the more disks a participant saw, the less accurate their response became. This suggests that however people store information, if working memory reaches capacity, decision-making suffers and that, over time, stored information decays. Schapiro et al. also noticed that participants remembered location information in different ways depending on the task and how many disks they were shown at once. This suggests people adopt different strategies to retain information momentarily. In summary, these findings help to explain how people process and store information to make decisions and how the limitations of working memory impact their decision-making ability. A better understanding of how people use working memory to make decisions may also shed light on situations or brain conditions where decision-making is impaired. 

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