The growing significance of social media in research demands new ethical standards and practices. Although a substantial body of literature on social media ethics exists, studies on the ethics of conducting research using social media are scarce. The emergence of new evidence sources, like social media, requires innovative methods and renewed consideration of research ethics. Therefore, we pose the following question: What are the defining characteristics of ethics papers on social media research? Following a modified version of the Preferred Reporting (...) Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, we analyzed 34 publications based on ten variables: author gender, publication year, region, academic discipline, type, design, methodology, social media platform in focus, positionality statement, and ethical issues. Our findings suggest contemporary social media research ethics primarily reflects the ethical ideals of the Global North, with limited representation from the Global South. Women authors have published more papers than men authors. Previous studies have prioritized ethical concerns such as privacy, informed consent, and anonymity while overlooking researchers’ risks and the ethics of social media sites. We particularly emphasized the lack of researchers’ positionality statements in research. Our findings will pave the way to understanding social media ethics better, especially with the rapid growth of social media research in global scholarship. (shrink)

Aims To examine the understanding of the ethical dilemmas associated with Big Data and artificial intelligence (AI) among Jordanian medical students, physicians in training, and senior practitioners. Methods We implemented a literature-validated questionnaire to examine the knowledge, attitudes, and practices of the target population during the period between April and August 2023. Themes of ethical debate included privacy breaches, consent, ownership, augmented biases, epistemology, and accountability. Participants’ responses were showcased using descriptive statistics and compared between groups using t-test or ANOVA. (...) Results We included 466 participants. The greater majority of respondents were interns and residents (50.2%), followed by medical students (38.0%). Most participants were affiliated with university institutions (62.4%). In terms of privacy, participants acknowledged that Big Data and AI were susceptible to privacy breaches (39.3%); however, 59.0% found such breaches justifiable under certain conditions. For ethical debacles involving informed consent, 41.6% and 44.6% were aware that obtaining informed consent posed an ethical limitation in Big Data and AI applications and denounced the concept of “broad consent”, respectively. In terms of ownership, 49.6% acknowledged that data cannot be owned yet accepted that institutions could hold a quasi-control of such data (59.0%). Less than 50% of participants were aware of Big Data and AI’s abilities to augment or create new biases in healthcare. Furthermore, participants agreed that researchers, institutions, and legislative bodies were responsible for ensuring the ethical implementation of Big Data and AI. Finally, while demonstrating limited experience with using such technology, participants generally had positive views of the role of Big Data and AI in complementing healthcare. Conclusion Jordanian medical students, physicians in training and senior practitioners have limited awareness of the ethical risks associated with Big Data and AI. Institutions are responsible for raising awareness, especially with the upsurge of such technology. (shrink)

Providing healthcare services frequently involves cognitively demanding tasks, including diagnoses and analyses as well as complex decisions about treatments and therapy. From a global perspective, ethically significant inequalities exist between regions where the expert knowledge required for these tasks is scarce or abundant. One possible strategy to diminish such inequalities and increase healthcare opportunities in expert-scarce settings is to provide healthcare solutions involving digital technologies that do not necessarily require the presence of a human expert, e.g., in the form of (...) artificial intelligent decision-support systems (AI-DSS). Such algorithmic decision-making, however, is mostly developed in resource- and expert-abundant settings to support healthcare experts in their work. As a practical consequence, the normative standards and requirements for such algorithmic decision-making in healthcare require the technology to be at least as explainable as the decisions made by the experts themselves. The goal of providing healthcare in settings where resources and expertise are scarce might come with a normative pull to lower the normative standards of using digital technologies in order to provide at least some healthcare in the first place. We scrutinize this tendency to lower standards in particular settings from a normative perspective, distinguish between different types of absolute and relative, local and global standards of explainability, and conclude by defending an ambitious and practicable standard of local relative explainability. (shrink)

The public health measures implemented in response to the COVID-19 pandemic have resulted in a substantially increased shared reliance on private infrastructure and digital services in areas such as healthcare, education, retail, and the workplace. This development has (i) granted a number of private actors significant (informational) power, and (ii) given rise to a range of digital surveillance practices incidental to the pandemic itself. In this paper, we reflect on these secondary consequences of the pandemic and observe that, even though (...) collateral data disclosure and additional activity monitoring appears to have been generally socially accepted as inevitable consequences of the pandemic, part and parcel of a larger conglomeration of emergency compromises, these increased surveillance practices were not directly justified by appeals to solidarity and public health in the same way that the instigating public health measures were. Based on this observation, and given the increased reliance on private actors for maintaining the digital space, we argue that governments have a duty to (i) seek and ensure that there are justifications for collateral data disclosure and activity monitoring by private actors in the context of (future) public health emergencies like the COVID-19 pandemic, and (ii) regulate and provide accountability mechanisms for and oversight over these private surveillance practices on par with governmental essential services that engage in surveillance activities. (shrink)

As we navigate physical (e.g., supermarket) and digital (e.g., social media) systems, we generate personal data about our behavior. Researchers and designers increasingly rely on this data and appeal to several approaches to collect it. One of these is data donation, which encourages people to voluntarily transfer their (personal) data collected by external parties to a specific cause. One of the central pillars of data donation is informed consent, meaning people should be _adequately informed_ about what and how their data (...) will be used. However, can we be _adequately informed_ when it comes to donating our data when many times we don’t even know it is being collected and, even more so, what exactly is being collected? In this paper, we investigate how to foster (personal) data literacy and increase donors’ understanding of their data. We introduce a Research through Design approach where we define a data donation journey in the context of speech records, data collected by Google Assistant. Based on the data donation experiences of 22 donors, we propose a data donation framework that understands and approaches data donation as an encompassing process with mutual benefit for donors and researchers. Our framework supports a donation process that dynamically and iteratively engages donors in exploring and understanding their data and invites them to (re)evaluate and (re)assess their participation. Through this process, donors increase their data literacy and are empowered to give meaningfully informed consent. (shrink)

This paper argues, against the prevailing view, that consent to privacy policies that regular internet users usually give is largely unproblematic from the moral point of view. To substantiate this claim, we rely on the idea of the right not to know (RNTK), as developed by bioethicists. Defenders of the RNTK in bioethical literature on informed consent claim that patients generally have the right to refuse medically relevant information. In this article we extend the application of the RNTK to online (...) privacy. We then argue that if internet users can be thought of as exercising their RNTK before consenting to privacy policies, their consent ought to be considered free of the standard charges leveled against it by critics. (shrink)

This paper argues, against the prevailing view, that consent to privacy policies that regular internet users usually give is largely unproblematic from the moral point of view. To substantiate this claim, we rely on the idea of the right not to know (RNTK), as developed by bioethicists. Defenders of the RNTK in bioethical literature on informed consent claim that patients generally have the right to refuse medically relevant information. In this article we extend the application of the RNTK to online (...) privacy. We then argue that if internet users can be thought of as exercising their RNTK before consenting to privacy policies, their consent ought to be considered free of the standard charges leveled against it by critics. (shrink)

En este capítulo se analizan las propuestas éticas para el desarrollo digital y empresarial de cuatro grandes corporativos internacionales: Microsoft, Google (Alphabet), Facebook (Meta) y Apple. Se ponderan cada uno de sus compromisos publicados en sus plataformas respectivas o las políticas compartidas por sus direcciones ejecutivas. Si bien cada una de las megaempresas, al menos en el papel, presume una serie de valores incuestionables por su integridad, también es cierto que la mayoría ha tenido que enfrentar crisis por la carencia (...) precisamente de algunos de los principios por ellos mismos proclamados. También es cierto que no puede hacerse generalizaciones precipitadas, por eso aquí se comparte primero, objetivamente, lo que proponen y, siendo autorregulatorias, se discute hasta qué punto han cumplido sus propias aspiraciones éticas. (shrink)

La repetición y la superposición innecesaria de principios éticos similares para el desarrollo de una inteligencia artificial responsable no solo entran en conflicto, sino que esta confusión y ambigüedad pueden llegar, incluso, a resultar peligrosas si los postulados son un mero “lavado de cara” y las verdaderas intenciones se esconden detrás de intereses mezquinos. Esto aplica tanto a particulares, a empresas, como a gobiernos. El proceso de establecer leyes, normas, estándares y mejores prácticas para asegurar que la IA sea benéfica (...) para toda la sociedad es un llamado urgente para un “mejor futuro de la humanidad”. De todo lo anterior, surge este intento por traducir en solo seis principios fundamentales el cúmulo de literatura que hasta ahora se tiene y, posteriormente, defender tres máximas que podrían sintetizar no solo los principios esbozados en este libro, sino los marcos normativos vigentes con aspiraciones universalistas: responsabilidad, transparencia, seguridad, no discriminación, privacidad y sostenibilidad; y como máximas clásicas de la ética: honradez, intencionalidad y conciencia moral. (shrink)