In 2008, a group of researchers publicly released profile data collected from the Facebook accounts of an entire cohort of college students from a US university. While good-faith attempts were made to hide the identity of the institution and protect the privacy of the data subjects, the source of the data was quickly identified, placing the privacy of the students at risk. Using this incident as a case study, this paper articulates a set of ethical concerns that must be addressed (...) before embarking on future research in social networking sites, including the nature of consent, properly identifying and respecting expectations of privacy on social network sites, strategies for data anonymization prior to public release, and the relative expertise of institutional review boards when confronted with research projects based on data gleaned from social media. (shrink)

Various Value-Conscious Design frameworks have recently emerged to introduce moral and ethical intelligence into business and technical design contexts, with the goal of proactively influencing the design of technologies to account for moral and ethical values during the conception and design process. Two attempts to insert ethical intelligence into technical design communities to influence the design of technologies in ethical- and value-conscious ways are described, revealing discouraging results. Learning from these failed attempts, the article identifies three key challenges of pragmatic (...) engagement with technical design communities: confronting competing values; identifying the role of the values advocate; and the justification of a value framework. Addressing these challenges must become a priority if one is to be successful in pragmatically engaging with real-world business and design contexts to bring moral and ethical intelligence to bear in the design of emerging information and communication technologies. (shrink)

Frequent public uproar over forms of data science that rely on information about people demonstrates the challenges of defining and demonstrating trustworthy digital data research practices. This paper reviews problems of trustworthiness in what we term pervasive data research: scholarship that relies on the rich information generated about people through digital interaction. We highlight the entwined problems of participant unawareness of such research and the relationship of pervasive data research to corporate datafication and surveillance. We suggest a way forward by (...) drawing from the history of a different methodological approach in which researchers have struggled with trustworthy practice: ethnography. To grapple with the colonial legacy of their methods, ethnographers have developed analytic lenses and researcher practices that foreground relations of awareness and power. These lenses are inspiring but also challenging for pervasive data research, given the flattening of contexts inherent in digital data collection. We propose ways that pervasive data researchers can incorporate reflection on awareness and power within their research to support the development of trustworthy data science. (shrink)

This article is based on a roundtable held at the Association of Internet Researchers annual conference in 2017, in Tartu, Estonia. The roundtable was organized by the Oxford Internet Institute’s Digital Ethics Lab. It was entitled “Artificial Intelligence and the Good Society”. It brought together four scholars—Michael Zimmer, Stine Lomborg, Ben Zevenbergen, and Corinne Cath—to discuss the promises and perils of artificial intelligence, in particular what ethical frameworks are needed to guide AI’s rapid development and increased use in societies. The (...) paper covers three case studies. They give a distinct overview of the ethical issues raised by the use of AI at different levels of analysis: top-down application of AI, bottom-up use of AI, and how academics and governments have reacted to these new challenges. From the case studies, four areas emerged. They represent some of the most topical ethical questions related to AI: its uses, its users, its designers, and the data that fuel it. Each of them provided a specific subset of ethical concerns that need further investigation. In conclusion, three recommendations are formulated for researchers and regulators to ensure the AI has a net-positive impact on society. (shrink)

As libraries begin to embrace Web 2.0 technologies to serve patrons—ushering in the era of Library 2.0—unique dilemmas arise in the realm of information ethics, especially regarding patron privacy. The norms of Web 2.0 promote the open sharing of information—often personal information—and the design of many Library 2.0 services capitalize on access to patron information and might require additional tracking, collection, and aggregation of patron activities. Thus, embracing Library 2.0 potentially threatens the traditional ethics of librarianship, where protecting patron privacy (...) and intellectual freedom has been held paramount. The question is not whether libraries will move toward Library 2.0, but how they will do it, and whether they can preserve the contextual integrity of patron privacy and maintain their professional librarian ethic, while also providing enhanced services to their patrons. This article will provide an ethical examination of the emergence of new Library 2.0 tools and technologies in relation to existing ethical norms of information flow within the library context. By doing so, librarians and information professionals will be better situated to avoid—or at least renegotiate—the impending Faustian bargain regarding patron privacy in the "2.0" era. (shrink)

Renewed attention to integrating information ethics within graduate library and information science programs has forced LIS educators to ensure that future information professionals - and the users they interact with - participate appropriately and ethically in our contemporary information society. Along with focusing on graduate LIS curricula, information ethics must become infused in multiple and varied educational contexts, ranging from elementary and secondary education, technical degrees and undergraduate programs, public libraries, through popular media, and within the home.Teaching information ethics in (...) these diverse settings and contexts brings numerous challenges and requires new understandings and innovative approaches. In keeping with the 2011 Association for Library and Information Science Education conference theme of "Competitiveness and Innovation," a diverse panel of educators and researchers were convened to foster a discussion in how to best incorporate information ethics education across diverse contexts, and how to develop innovative educational methods to overcome the challenges these contexts inevitably present. This article reports on that panel discussion and offers recommendations towards achieving success in information ethics education. (shrink)

Purpose Existing algorithms for predicting suicide risk rely solely on data from electronic health records, but such models could be improved through the incorporation of publicly available socioeconomic data – such as financial, legal, life event and sociodemographic data. The purpose of this study is to understand the complex ethical and privacy implications of incorporating sociodemographic data within the health context. This paper presents results from a survey exploring what the general public’s knowledge and concerns are about such publicly available (...) data and the appropriateness of using it in suicide risk prediction algorithms. Design/methodology/approach A survey was developed to measure public opinion about privacy concerns with using socioeconomic data across different contexts. This paper presented respondents with multiple vignettes that described scenarios situated in medical, private business and social media contexts, and asked participants to rate their level of concern over the context and what factor contributed most to their level of concern. Specific to suicide prediction, this paper presented respondents with various data attributes that could potentially be used in the context of a suicide risk algorithm and asked participants to rate how concerned they would be if each attribute was used for this purpose. Findings The authors found considerable concern across the various contexts represented in their vignettes, with greatest concern in vignettes that focused on the use of personal information within the medical context. Specific to the question of incorporating socioeconomic data within suicide risk prediction models, the results of this study show a clear concern from all participants in data attributes related to income, crime and court records, and assets. Data about one’s household were also particularly concerns for the respondents, suggesting that even if one might be comfortable with their own being used for risk modeling, data about other household members is more problematic. Originality/value Previous studies on the privacy concerns that arise when integrating data pertaining to various contexts of people’s lives into algorithmic and related computational models have approached these questions from individual contexts. This study differs in that it captured the variation in privacy concerns across multiple contexts. Also, this study specifically assessed the ethical concerns related to a suicide prediction model and determining people’s awareness of the publicness of select data attributes, as well as which of these data attributes generated the most concern in such a context. To the best of the authors’ knowledge, this is the first study to pursue this question. (shrink)