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1. Crowdsourcing Perceptions of Gerrymandering

   https://doi.org/10.1609/hcomp.v10i1.21993  

   Kelly, Benjamin; Kang, Inwon; Xia, Lirong (October 2022, Proceedings of the AAAI Conference on Human Computation and Crowdsourcing)

   Gerrymandering is the manipulation of redistricting to influence the results of a set of elections for local representatives. Gerrymandering has the potential to drastically swing power in legislative bodies even with no change in a population’s political views. Identifying gerrymandering and measuring fairness using metrics of proposed district plans is a topic of current research, but there is less work on how such plans will be perceived by voters. Gathering data on such perceptions presents several challenges such as the ambiguous definitions of ‘fair’ and the complexity of real world geography and district plans. We present a dataset collected from an online crowdsourcing platform on a survey asking respondents to mark which of two maps of equal population distribution but different districts appear more ‘fair’ and the reasoning for their decision. We performed preliminary analysis on this data and identified which of several commonly suggested metrics are most predictive of the responses. We found that the maximum perimeter of any district was the most predictive metric, especially with participants who reported that they made their decision based on the shape of the districts. 

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2. "I'm not as afraid as a woman might be about sharing my exact location:" On the Intersection of Identity and Privacy Concerns in Fitness Tracking

   https://doi.org/10.1145/3706598.3713941  

   Jo, Yeeun; Jameel, Mahnoor; Cobb, Camille; Bates, Adam (April 2025, ACM)

   Users’ perceptions of fitness tracking privacy is a subject of active study, but how do various aspects of social identity inform these perceptions? We conducted an online survey (N=322) that explores the influence of identity on fitness tracking privacy perceptions and practices, considering participants’ gender, race, age, and whether or not they identify as LGTBQ*. Participants reported how comfortable they felt sharing fitness data, commented on whether they believed their identity impacted this comfort, and brainstormed several data sharing risks and a possible mitigation for each risk. For each surveyed dimension of social identity, we find one or more reliable effects on participants’ level of comfort sharing fitness data, specifically when considering institutional groups like employers, insurers, and advertisers. Further, 64% of participants indicate at least one of their identity characteristics informs their comfort. We also find evidence that the perceived risks of sharing fitness data vary by identity, but do not find evidence of difference in the strategies used to manage these risks. This work highlights a path towards reasoning about the privacy challenges of fitness tracking with respect for the lived experiences of all users. 

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3. Do Crowdsourced Fairness Preferences Correlate with Risk Perceptions?

   https://doi.org/10.1145/3640543.3645209  

   Haider, Chowdhury_Mohammad Rakin; Clifton, Christopher; Yin, Ming (March 2024, ACM)

   With the increasing prevalence of automatic decision-making systems, concerns regarding the fairness of these systems also arise. Without a universally agreed-upon definition of fairness, given an automated decision-making scenario, researchers often adopt a crowdsourced approach to solicit people’s preferences across multiple fairness definitions. However, it is often found that crowdsourced fairness preferences are highly context-dependent, making it intriguing to explore the driving factors behind these preferences. One plausible hypothesis is that people’s fairness preferences reflect their perceived risk levels for different decision-making mistakes, such that the fairness definition that equalizes across groups the type of mistakes that are perceived as most serious will be preferred. To test this conjecture, we conduct a human-subject study (𝑁 =213) to study people’s fairness perceptions in three societal contexts. In particular, these three societal contexts differ on the expected level of risk associated with different types of decision mistakes, and we elicit both people’s fairness preferences and risk perceptions for each context. Our results show that people can often distinguish between different levels of decision risks across different societal contexts. However, we find that people’s fairness preferences do not vary significantly across the three selected societal contexts, except for within a certain subgroup of people (e.g., people with a certain racial background). As such, we observe minimal evidence suggesting that people’s risk perceptions of decision mistakes correlate with their fairness preference. These results highlight that fairness preferences are highly subjective and nuanced, and they might be primarily affected by factors other than the perceived risks of decision mistakes. 

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4. Predicting Human Perceptions of Robot Performance during Navigation Tasks

   https://doi.org/10.1145/3719020  

   Zhang, Qiping; Tsoi, Nathan; Nagib, Mofeed; Choi, Booyeon; Tan, Jie; Chiang, Hao-Tien Lewis; Vázquez, Marynel (April 2025, ACM Transactions on Human-Robot Interaction)

   Understanding human perceptions of robot performance is crucial for designing socially intelligent robots that can adapt to human expectations. Current approaches often rely on surveys, which can disrupt ongoing human–robot interactions. As an alternative, we explore predicting people’s perceptions of robot performance using non-verbal behavioral cues and machine learning techniques. We contribute the SEAN TOGETHER Dataset consisting of observations of an interaction between a person and a mobile robot in Virtual Reality, together with perceptions of robot performance provided by users on a 5-point scale. We then analyze how well humans and supervised learning techniques can predict perceived robot performance based on different observation types (like facial expression and spatial behavior features). Our results suggest that facial expressions alone provide useful information, but in the navigation scenarios that we considered, reasoning about spatial features in context is critical for the prediction task. Also, supervised learning techniques outperformed humans’ predictions in most cases. Further, when predicting robot performance as a binary classification task on unseen users’ data, the F1-Score of machine learning models more than doubled that of predictions on a 5-point scale. This suggested good generalization capabilities, particularly in identifying performance directionality over exact ratings. Based on these findings, we conducted a real-world demonstration where a mobile robot uses a machine learning model to predict how a human who follows it perceives it. Finally, we discuss the implications of our results for implementing these supervised learning models in real-world navigation. Our work paves the path to automatically enhancing robot behavior based on observations of users and inferences about their perceptions of a robot. 

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5. Investigating the role of group-based morality in extreme behavioral expressions of prejudice

   https://doi.org/10.1038/s41467-021-24786-2  

   Hoover, Joe; Atari, Mohammad; Mostafazadeh Davani, Aida; Kennedy, Brendan; Portillo-Wightman, Gwenyth; Yeh, Leigh; Dehghani, Morteza (July 2021, Nature Communications)

   Abstract Understanding motivations underlying acts of hatred are essential for developing strategies to prevent such extreme behavioral expressions of prejudice (EBEPs) against marginalized groups. In this work, we investigate the motivations underlying EBEPs as a function of moral values. Specifically, we propose EBEPs may often be best understood as morally motivated behaviors grounded in people’s moral values and perceptions of moral violations. As evidence, we report five studies that integrate spatial modeling and experimental methods to investigate the relationship between moral values and EBEPs. Our results, from these U.S. based studies, suggest that moral values oriented around group preservation are predictive of the county-level prevalence of hate groups and associated with the belief that extreme behavioral expressions of prejudice against marginalized groups are justified. Additional analyses suggest that the association between group-based moral values and EBEPs against outgroups can be partly explained by the belief that these groups have done something morally wrong. 

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