Brain Data in Context: Are New Rights the Way to Mental and Brain Privacy?

The potential to collect brain data more directly, with higher resolution, and in greater amounts has heightened worries about mental and brain privacy. In order to manage the risks to individuals posed by these privacy challenges, some have suggested codifying new privacy rights, including a right to "mental privacy." In this paper, we consider these arguments and conclude that while neurotechnologies do raise significant privacy concerns, such concerns are-at least for now-no different from those raised by other well-understood data collection technologies, such as gene sequencing tools and online surveillance. To better understand the privacy stakes of brain data, we suggest the use of a conceptual framework from information ethics, Helen Nissenbaum's "contextual integrity" theory. To illustrate the importance of context, we examine neurotechnologies and the information flows they produce in three familiar contexts-healthcare and medical research, criminal justice, and consumer marketing. We argue that by emphasizing what is distinct about brain privacy issues, rather than what they share with other data privacy concerns, risks weakening broader efforts to enact more robust privacy law and policy.

Our business, not the robot's: family conversations about privacy with social robots in the home

The targeted use of social robots for the family demands a better understanding of multiple stakeholders' privacy concerns, including those of parents and children. Through a co-learning workshop which introduced families to the functions and hypothetical use of social robots in the home, we present preliminary evidence from 6 families that exhibits how parents and children have different comfort levels with robots collecting and sharing information across different use contexts. Conversations and booklet answers reveal that parents adopted their child's decision in scenarios where they expect children to have more agency, such as in cases of homework completion or cleaning up toys, and when children proposed what their parents found to be acceptable reasoning for their decisions. Families expressed relief when they shared the same reasoning when coming to conclusive decisions, signifying an agreement of boundary management between the robot and the family. In cases where parents and children did not agree, they rejected a binary, either-or decision and opted for a third type of response, reflecting skepticism, uncertainty and/or compromise. Our work highlights the benefits of involving parents and children in child- and family-centered research, including parental abilities to provide cognitive scaffolding and personalize hypothetical scenarios for their children.

A preregistered vignette experiment on determinants of health data sharing behavior Willingness to donate sensor data, medical records, and biomarkers

The COVID-19 pandemic has spotlighted the importance of high-quality data for empirical health research and evidence-based political decision-making. To leverage the full potential of these data, a better understanding of the determinants and conditions under which people are willing to share their health data is critical. Building on the privacy theory of contextual integrity, the privacy calculus, and previous findings regarding different data types and recipients, we argue that established social norms shape the acceptance of novel practices of data collection and use. To investigate the willingness to share health data, we conducted a preregistered vignette experiment. The scenarios experimentally varied the vignette dimensions by data type, recipient, and research purpose. While some findings contradict our hypotheses, the results indicate that all three dimensions affected respondents' data sharing decisions. Additional analyses suggest that institutional and social trust, privacy concerns, technical affinity, altruism, age, and device ownership influence the willingness to share health data.

Collecting migrants' Facebook posts: Accounting for ethical measures in a text-as-data approach

Based on the heuristics proposed by Helen Nissenbaum to assess ethical issues surrounding research using new technologies, this paper discusses the ethics of the collection and analysis of migrants' digital traces for academic research purposes. Concretely, this paper is grounded on an empirical research that applies a topic modeling approach to a large dataset of migrants' posts written on Facebook groups. After discussing the nine aspects proposed by Nissenbaum, the paper contends that while researchers strive to comply with ethical measures by, for instance, asking adequate questions and protecting the collected data, the lack of transparency of social networking sites is harmful to critical social sciences and can hamper findings that contribute to understanding migratory patterns and decisions.

More Than Just Privacy: Using Contextual Integrity to Evaluate the Long-Term Risks from COVID-19 Surveillance Technologies

The global coronavirus pandemic has raised important questions regarding how to balance public health concerns with privacy protections for individual citizens. In this essay, we evaluate contact tracing apps, which have been offered as a technological solution to minimize the spread of COVID-19. We argue that apps such as those built on Google and Apple's "exposure notification system" should be evaluated in terms of the contextual integrity of information flows; in other words, the appropriateness of sharing health and location data will be contextually dependent on factors such as who will have access to data, as well as the transmission principles underlying data transfer. We also consider the role of prevailing social and political values in this assessment, including the large-scale social benefits that can be obtained through such information sharing. However, caution should be taken in violating contextual integrity, even in the case of a pandemic, because it risks a long-term loss of autonomy and growing function creep for surveillance and monitoring technologies.

Automating Dynamic Consent Decisions for the Processing of Social Media Data in Health Research

Social media have become a rich source of data, particularly in health research. Yet, the use of such data raises significant ethical questions about the need for the informed consent of those being studied. Consent mechanisms, if even obtained, are typically broad and inflexible, or place a significant burden on the participant. Machine learning algorithms show much promise for facilitating a "middle-ground" approach: using trained models to predict and automate granular consent decisions. Such techniques, however, raise a myriad of follow-on ethical and technical considerations. In this article, we present an exploratory user study (n = 67) in which we find that we can predict the appropriate flow of health-related social media data with reasonable accuracy, while minimizing undesired data leaks. We then attempt to deconstruct the findings of this study, identifying and discussing a number of real-world implications if such a technique were put into practice.

The Issue of "Context": Data, Culture, and Commercial Context in Social Media Ethics

One of the central concerns in research ethics in recent years has been the vast amount of data available from social media platforms and the related concerns around what establishes an ethical use of data. Toward addressing these challenges, researchers have therefore called for the consideration of "context" in Internet research. However, context remains a fuzzy concept and little guidance exists on its different dimensions. In response to this issue, this article uses worked examples from three data sets to discuss three different dimensions of "context": data context, cultural context, and commercial context. The article problematizes these dimensions and offers suggestions toward creating ethical sensibility to these by drawing on two data sets from 2017: (a) climate change imagery scraped from five social platforms and (b) digital-ethnographic work at the climate summit COP23.