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1. Public Concern About Monitoring Twitter Users and Their Conversations to Recruit for Clinical Trials: Survey Study

   https://doi.org/10.2196/15455  

   Reuter, Katja; Zhu, Yifan; Angyan, Praveen; Le, NamQuyen; Merchant, Akil A; Zimmer, Michael (January 2019, Journal of Medical Internet Research)

   Background Social networks such as Twitter offer the clinical research community a novel opportunity for engaging potential study participants based on user activity data. However, the availability of public social media data has led to new ethical challenges about respecting user privacy and the appropriateness of monitoring social media for clinical trial recruitment. Researchers have voiced the need for involving users’ perspectives in the development of ethical norms and regulations. Objective This study examined the attitudes and level of concern among Twitter users and nonusers about using Twitter for monitoring social media users and their conversations to recruit potential clinical trial participants. Methods We used two online methods for recruiting study participants: the open survey was (1) advertised on Twitter between May 23 and June 8, 2017, and (2) deployed on TurkPrime, a crowdsourcing data acquisition platform, between May 23 and June 8, 2017. Eligible participants were adults, 18 years of age or older, who lived in the United States. People with and without Twitter accounts were included in the study. Results While nearly half the respondents—on Twitter (94/603, 15.6%) and on TurkPrime (509/603, 84.4%)—indicated agreement that social media monitoring constitutes a form of eavesdropping that invades their privacy, over one-third disagreed and nearly 1 in 5 had no opinion. A chi-square test revealed a positive relationship between respondents’ general privacy concern and their average concern about Internet research (P<.005). We found associations between respondents’ Twitter literacy and their concerns about the ability for researchers to monitor their Twitter activity for clinical trial recruitment (P=.001) and whether they consider Twitter monitoring for clinical trial recruitment as eavesdropping (P<.001) and an invasion of privacy (P=.003). As Twitter literacy increased, so did people’s concerns about researchers monitoring Twitter activity. Our data support the previously suggested use of the nonexceptionalist methodology for assessing social media in research, insofar as social media-based recruitment does not need to be considered exceptional and, for most, it is considered preferable to traditional in-person interventions at physical clinics. The expressed attitudes were highly contextual, depending on factors such as the type of disease or health topic (eg, HIV/AIDS vs obesity vs smoking), the entity or person monitoring users on Twitter, and the monitored information. Conclusions The data and findings from this study contribute to the critical dialogue with the public about the use of social media in clinical research. The findings suggest that most users do not think that monitoring Twitter for clinical trial recruitment constitutes inappropriate surveillance or a violation of privacy. However, researchers should remain mindful that some participants might find social media monitoring problematic when connected with certain conditions or health topics. Further research should isolate factors that influence the level of concern among social media users across platforms and populations and inform the development of more clear and consistent guidelines. 

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2. When does data collection and use become a matter of concern? A cross cultural comparison of American and Dutch people’s privacy attitudes.

   Liao, Y. (January 2020, Annals of the International Communication Association)

   Around the world, people increasingly generate data through their everyday activities. Much of this also happens unwittingly, thanks to sensors, cameras, and other surveillance tools on the roads, in cities, and in businesses. However, the ways citizens and governments think about privacy vary significantly around the world. In this paper, we explore differences between citizens’ attitudes toward privacy and data collection practices in the U.S. and the Netherlands, an EU member nation. Using a factorial vignette survey methodology, we identify specific contextual factors associated with people’s level of concern about how their data is being used. We consider the role that five factors play in this assessment: actors (those using data), data type and amount, reported purpose for data use, and inferences drawn from the data. These indicate nationally bound differences but likewise point to potentially more globally shared concerns. 

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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. When does data collection and use become a matter of concern? A cross-cultural comparison of American and Dutch people’s privacy attitudes

   Vitak, J.; Liao, Y.; Mols, A. D.; Zimmer, M.; Kumar, P. C.; Pridmore, J. (January 2023, International Journal of Communication)

   Around the world, people increasingly generate data through their everyday activities. Much of this happens unwittingly through sensors, cameras, and other surveillance tools on roads, in cities, and at the workplace. However, how individuals and governments think about privacy varies significantly around the world. In this article, we explore differences between people’s attitudes toward privacy and data collection practices in the United States and the Netherlands, two countries with very different regulatory approaches to governing consumer privacy. Through a factorial vignette survey deployed in the two countries, we identify specific contextual factors associated with concerns regarding how personal data are being used. Using Nissenbaum’s framework of privacy as contextual integrity to guide our analysis, we consider the role that five factors play in this assessment: actors (those using data), data type, amount of data collected, reported purpose of data use, and inferences drawn from the data. Findings indicate nationally bound differences as well as shared concerns and indicate future directions for cross-cultural privacy research. 

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5. An Introduction to the Federated Learning Standard

   https://doi.org/10.1145/3511285.3511291  

   Zhang, Ticao; Mao, Shiwen (January 2022, GetMobile: Mobile Computing and Communications)

   With the growing concern on data privacy and security, it is undesirable to collect data from all users to perform machine learning tasks. Federated learning, a decentralized learning framework, was proposed to construct a shared prediction model while keeping owners' data on their own devices. This paper presents an introduction to the emerging federated learning standard and discusses its various aspects, including i) an overview of federated learning, ii) types of federated learning, iii) major concerns and the performance evaluation criteria of federated learning, and iv) associated regulatory requirements. The purpose of this paper is to provide an understanding of the standard and facilitate its usage in model building across organizations while meeting privacy and security concerns. 

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