Dove, Edward S, David Townend, Eric M. Meslin, Martin Bobrow, Katherine Littler, Dianne Nicol, Jantina de Vries, Anne Junker, Chiara Garattini, Jasper Bovenberg, Mahsa Shabani, Emmanuelle Levesqure, Bartha M. Knoppers. 2016. Ethics Review for International Data-Intensive Research. Science 351: 6280, March 25. 1399-1400.
The authors reviewed numbers of approaches to ethics review for large data sets relevant to human subjects and their protection (excluding clinical trials research) and identified three models that could inform a framework allowing mutual recognition of international ethics review. The models are reciprocity, delegation, and federation, and a chart listing advantages and disadvantages and examples of projects for each model is provided.
Metcalf Jacob and Kate Crawford 2016. Where are human subjects in big data research? The emerging ethics divide. Big Data & Society 3(1): 1–14.
Authors illustrate how proposed changes to the regulations governing human-subjects research protections do not address certain harms caused by big data research that uses public datasets, and discuss what ethical protections "data subjects" might require.
The National Academies of Sciences, Engineering and Medicine. 2013. Proposed Revisions to the Common Rule: Perspectives of Social and Behavioral Scientists: Workshop Summary. Washington, DC: The National Academies Press.
The summary focuses on: 1. Evidence on the functioning of the Common Rule and of institutional review boards (IRBs). 2. Types and levels of risk and harms in social and behavioral sciences, and issues of severity and probability of harm. 3. Consent and special populations. 4. Protection of research participants. 5. Multidisciplinary and multisite studies. 6. The purview and roles of IRBs.
Collman, Jeff, Sorin Adam Matei (eds.) 2013. Ethical Reasoning in Big Data: an exploratory analysis. Cham: Springer.
Looking at the field of computational social science, this books looks at the privacy and ethical implications of research in human affairs using big data.
Elmer, Greg, Ganaele Langlois and Joanna Redden (eds). 2015. Compromised Data: From Social Media to Big Data. New York: Bloomsbury.
Discusses how researchers perform critical research within a compromised social data framework influenced by biases, economic interests, etc., and how this can lead to a fundamental shift between research and the public good as well as new forms of control and surveillance.
Kitchin, Rob. 2014. The Data Revolution: Big data, open data, data infrastructure and their consequences. London: SAGE Publications.
This book discusses the technical shortcomings and the social, political, and ethical consequences of this ‘data revolution’, as well as providing an analysis of the potential implications to academic, business, and government practices.
Ambrose, Meg Leta. 2014. "Lessons from the Avalanche of Numbers: Big Data in Historical Context." I/S: A Journal of Law and Policy for the Information Society 11(2): 201-277.
The big data revolution, like many changes associated with technological advancement, is often compared to the industrial revolution to create a frame of reference for its transformative power, or portrayed as altogether new. This article argues that between the industrial revolution and the digital revolution is a more valuable, yet overlooked period: the probabilistic revolution that began with the avalanche of printed numbers between 1820 and 1840. By comparing the many similarities between big data today and the avalanche of numbers in the 1800s, the article situates big data in the early stages of a prolonged transition to a potentially transformative epistemic revolution, like the probabilistic revolution. The widespread changes in and characteristics of a society flooded by data results in a transitional state that creates unique challenges for policy efforts by disrupting foundational principles relied upon for data protection. The potential of a widespread, lengthy transition also places the law in a pivotal position to shape and guide big data-based inquiry through to whatever epistemic shift may lie ahead.
Ball, Kirstie, MariaLaura Di Domenico, and Daniel Nunan. 2016. "Big Data Surveillance and the Body-subject." Body & Society 22 (2):58-81. doi: 10.1177/1357034X15624973.
This paper considers the implications of big data practices for theories about the surveilled subject who, analysed from afar, is still gazed upon, although not directly watched as with previous surveillance systems. The authors propose that this surveilled subject be viewed through a lens of proximity rather than interactivity, to highlight the normative issues arising within digitally mediated relationships. They interpret the ontological proximity between subjects, data flows and big data surveillance through Merleau-Ponty’s ideas combined with Levinas’ approach to ethical proximity and Coeckelberg’s work on proximity in the digital age. This leads us to highlight how competing normativities, and normative dilemmas in these proximal spaces, manipulate the surveilled subject’s embodied practices to lead the embodied individual towards experiencing them in a local sense.
Bonilla, Diego Navarro. 2013. "Information Management professionals working for intelligence organizations: ethics and deontology implications." Security & Human Rights 24 (3/4):264-279. doi: 10.1163/18750230-02404005.
Archive and information management experts trained in library science programs are ideal candidates for jobs in intelligence organizations. Their skills, abilities and knowledge are frequently required in at least two well-defined areas: open source information gathering and records management/archival organisation. Under the general overview of the debate between "big data vs. big narrative" this article focuses on the ethical challenges that affect this community of information professionals. As a key component of the so-called "intelligence culture", it will be also underlined the need for intensifying from our university classrooms the ethical dimension of information exploitation for security and defence purposes. The role played by these information profiles involved in multiple phases of the intelligence production process must be based not only on efficiency and efficacy criteria but also on deontology principles whose benefits are the fortification of democratic practice by intelligence services working in strong legal frameworks designed to guarantee fundamental rights.
boyd, danah, and Kate Crawford. 2012. "Critical Questions for Big Data." Information, Communication & Society 15 (5):662-679. doi: 10.1080/1369118X.2012.678878.
The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people. Significant questions emerge. Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data analytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means? Given the rise of Big Data as a socio-technical phenomenon, we argue that it is necessary to critically interrogate its assumptions and biases. In this article, we offer six provocations to spark conversations about the issues of Big Data: a cultural, techno- logical, and scholarly phenomenon that rests on the interplay of technology, analysis, and mythology that provokes extensive utopian and dystopian rhetoric.
Chan, Anita. 2015. "Big data interfaces and the problem of inclusion." Media, Culture & Society 37 (7):1078-1083. doi: 10.1177/0163443715594106.
A commentary on ‘Critical Questions for Big Data’ and the projection in the article of how ‘limited access to big data creates new digital divides’. Pressing questions are indeed proliferating around not only what the actual relationship is between data and real world user behavior but also around defining what the very practices, knowledge sets, legal and technological infrastructures, and social norms are that guide the work of big data as a field itself. But how much of a difference does it make for academics and academic institutions to gain access to big data when the logics of commerce and commercial enclosure around data management, collection, and use are what increasingly get privileged?
Cooper, Anwen, and Chris Green. 2016. "Embracing the Complexities of 'Big Data' in Archaeology: the Case of the English Landscape and Identities Project." Journal of Archaeological Method & Theory 23 (1):271-304. doi: 10.1007/s10816-015-9240-4.
This paper considers recent attempts within archaeology to create, integrate and interpret digital data on an unprecedented scale-a movement that resonates with the much wider so-called big data phenomenon. Using the example of the authors’ work with a particularly large and complex dataset collated for the purpose of the English Landscape and Identities project (EngLaID), Oxford, UK, and drawing on insights from social scientists' studies of information infrastructures much more broadly, they make the following key points. Firstly, alongside scrutinising and homogenising digital records for research purposes, it is vital that we continue to appreciate the broader interpretative value of 'characterful' archaeological data (those that have histories and flaws of various kinds). Secondly, given the intricate and pliable nature of archaeological data and the substantial challenges faced by researchers seeking to create a cyber-infrastructure for archaeology, it is essential that we develop interim measures that allow us to explore the parameters and potentials of working with archaeological evidence on an unprecedented scale.
Crawford. 2014a. “The Test We Can—and Should—Run on Facebook.” The Atlantic. July 2.
Discusses the Facebook emotional contagion experiment and what it means ethically for social researcher interested in doing large-scale user experimentation using social media and big data.
Crawford, Kate. 2014. "When Big Data Marketing Becomes Stalking." Scientific American 310 (4):14-14.
Can data brokers and marketers be trusted to regulate themselves?
Crawford, Kate, Mary L. Gray, and Kate Miltner. "Big Data Critiquing Big Data: Politics, Ethics, Epistemology| Special Section Introduction." International Journal of Communication 8 (2014): 1-11.
Why now? This is the first question we might ask of the big data phenomenon. Why has it gained such remarkable purchase in a range of industries and across academia, at this point in the 21st century? Big data as a term has spread like kudzu in a few short years, ranging across a vast terrain that spans health care, astronomy, policing, city planning, and advertising. From the RNA bacteriophages in our bodies to the Kepler Space Telescope, searching for terrorists or predicting cereal preferences, big data is deployed as the term of art to encompass all the techniques used to analyze data at scale. But why has the concept gained such traction now?
Crawford, Kate and Jacob Metcalf. 2016. “Where are Human Subjects in Big Data Research? The Emerging Ethics Divide.” Big Data and Society.
There are growing discontinuities between the research practices of data science and established tools of research ethics regulation. Some of the core commitments of existing research ethics regulations, such as the distinction between research and practice, cannot be cleanly exported from biomedical research to data science research. Such discontinuities have led some data science practitioners and researchers to move toward rejecting ethics regulations outright. These shifts occur at the same time as a proposal for major revisions to the Common Rule—the primary regulation governing human-subjects research in the USA—is under consideration for the first time in decades. The authors contextualize these revisions in long-running complaints about regulation of social science research and argue data science should be understood as continuous with social sciences in this regard. The proposed regulations are more flexible and scalable to the methods of non-biomedical research, yet problematically largely exclude data science methods from human-subjects regulation, particularly uses of public datasets.
Eastin, Matthew S., Nancy H. Brinson, Alexandra Doorey, and Gary Wilcox. 2016. "Living in a big data world: Predicting mobile commerce activity through privacy concerns." Computers in Human Behavior 58:214-220. doi: 10.1016/j.chb.2015.12.050.
As advertisers increasingly rely on mobile-based data, consumer perceptions regarding the collection and use of such data becomes of great interest to scholars and practitioners. Recent industry data suggests advertisers seeking to leverage personal data offered via mobile devices would be wise to acknowledge and address the privacy concerns held by mobile users. Utilizing the theoretical foundation of communication privacy management (CPM), the current study investigates commonly understood privacy concerns such as collection, control, awareness, unauthorized secondary use, improper access and a newly adapted dimension of location tracking, trust in mobile advertisers, and attitudes toward mobile commerce, to predict mobile commerce engagement. Data from this study indicate that control, unauthorized access, trust in mobile advertisers, and attitude toward mobile commerce significantly predicted 43% of the variance in mobile commerce activity.
Easton-Calabria, Evan, and William L. Allen. 2015. "Developing ethical approaches to data and civil society: from availability to accessibility." Innovation: The European Journal of Social Sciences 28 (1):52-62. doi: 10.1080/13511610.2014.985193.
This research note reflects on the gaps and limitations confronting the development of ethical principles regarding the accessibility of large-scale data for civil society organizations (CSOs). Drawing upon a systematic scoping study on the use of data in the United Kingdom (UK) civil society, it finds that there are twin needs to conceptualize accessibility as more than mere availability of data, as well as examine the use of data among CSOs more generally. In order to deal with the apparent “digital divide” in UK civil society the authors present a working model in which ethical concerns accompanying data utilization by civil society may be better accounted. This suggests there is a need for further research into the nexus of civil society and data upon which interdisciplinary discussion about the ethical dimensions of engagement with data, particularly informed by insight from the social sciences, can be predicated.
Ekbia, Hamid, Michael Mattioli, Inna Kouper, G. Arave, Ali Ghazinejad, Timothy Bowman, Venkata Ratandeep Suri, Andrew Tsou, Scott Weingart, and Cassidy R. Sugimoto. 2015. "Big data, bigger dilemmas: A critical review." Journal of the Association for Information Science & Technology 66 (8):1523-1545. doi: 10.1002/asi.23294.
The recent interest in Big Data has generated a broad range of new academic, corporate, and policy practices along with an evolving debate among its proponents, detractors, and skeptics. While the practices draw on a common set of tools, techniques, and technologies, most contributions to the debate come either from a particular disciplinary perspective or with a focus on a domain-specific issue. A close examination of these contributions reveals a set of common problematics that arise in various guises and in different places. It also demonstrates the need for a critical synthesis of the conceptual and practical dilemmas surrounding Big Data. The purpose of this article is to provide such a synthesis by drawing on relevant writings in the sciences, humanities, policy, and trade literature. In bringing these diverse literatures together, we aim to shed light on the common underlying issues that concern and affect all of these areas. By contextualizing the phenomenon of Big Data within larger socioeconomic developments, we also seek to provide a broader understanding of its drivers, barriers, and challenges. This approach allows us to identify attributes of Big Data that require more attention-autonomy, opacity, generativity, disparity, and futurity-leading to questions and ideas for moving beyond dilemmas.
Flick, Uwe. 2015. "Qualitative Inquiry—2.0 at 20? Developments, Trends, and Challenges for the Politics of Research." Qualitative Inquiry 21 (7):599-608. doi: 10.1177/1077800415583296.
After 20 years of Qualitative Inquiry, some current trends and challenges are outlined, which might affect the current state and further development of qualitative research in the near future. A central focus is their impact on the politics of qualitative research. Politics of inquiry addressing problems of societal relevance are challenged by the globalization and internationalization of qualitative enquiry or trends to big data in funding. Other relevant trends are expectations about archiving and reanalysis of qualitative data, the new interest in qualitative inquiry in the context of evidence, limitations coming from ethical reviews, and the limitation to mixed methods research. These trends are discussed here by using examples from current research projects. Locating qualitative inquiry in the future is discussed between being pushed aside by citizen research and taking over some (sub)disciplines.
Fuller, Michael. 2015. "Big Data: new science, new challenges, new digital opportunities." Zygon: Journal of Religion & Science 50 (3):569-582. doi: 10.1111/zygo.12187.
The advent of extremely large data sets, known as 'big data,' has been heralded as the instantiation of a new science, requiring a new kind of practitioner: the 'data scientist.' This article explores the concept of big data, drawing attention to a number of new issues-not least ethical concerns, and questions surrounding interpretation-which big data sets present. It is observed that the skills required for data scientists are in some respects closer to those traditionally associated with the arts and humanities than to those associated with the natural sciences; and it is urged that big data presents new opportunities for dialogue, especially concerning hermeneutical issues, for theologians and data scientists.
Herther, Nancy K. 2014. "Global Efforts to Redefine Privacy in the Age of Big Data." Information Today 31 (6):1-36.
The article reports on efforts in specifying privacy in the big data era worldwide. It mentions retail firm Target Corp. and its creation of algorithms for determining pregnant teenagers. An overview of the Electronic Frontier Foundation's (EFF) rating system for the user privacy protection capability of social media sites and Internet search engines is also presented.
Holtzhausen, Derina. 2016. "Datafication: threat or opportunity for communication in the public sphere?" Journal of Communication Management 20 (1):21-36. doi: 10.1108/JCOM-12-2014-0082.
The paper also exposes the potential for harm in the use of Big Data, as well as its potential for improving society and bringing about social justice. Originality/value – The value of this paper is that it introduces the concept of datafication to communication studies and proposes theoretical foundations for the study of Big Data in the context of strategic communications. It provides a theoretical and social foundation for the inclusion of the public sphere in a definition of strategic communication and emphasizes strategic communicators’ commitment to the public sphere as more important than ever before. It highlights how communication practice and society can impact ach other positively and negatively and that Big Data should not be the future of strategic communication but only a part of it.
Honda, Laurie. 2017. “Case Study: “It Was A Matter of Life and Death”: A YouTube Engineer’s Decision to Alter Data in the ‘It Gets Better Project’.” Council for Big Data, Ethics, and Society.
In this case study, a YouTube engineer contemplates whether to subvert engineering best practices to bypass storage capacity limits on videos created for the It Gets Better Project, which aims to prevent self-harm by LGBTQ youth.
Horvitz, Eric and Deirdre Mulligan. 2015. “Data, privacy, and the greater good.” Science, Policy Forum, 17 July 2015. 349 (6245): 253-255.
Large-scale aggregate analyses of anonymized data can yield valuable results and insights that address public health challenges and provide new avenues for scientific discovery. These methods can extend our knowledge and provide new tools for enhancing health and wellbeing. However, they raise questions about how to best address potential threats to privacy while reaping benefits for individuals and to society as a whole. The use of machine learning to make leaps across informational and social contexts to infer health conditions and risks from nonmedical data provides representative scenarios for reflections on directions with balancing innovation and regulation.
Jaeger, Jaclyn. 2016. "Think the FTC isn't monitoring big data? Think again." Compliance Week 13 (146):26-27.
The Federal Trade Commission of the US released a report in January 2016 warning companies about the sort of ethical, legal and compliance risks they could face when using data analytics practices that are counter to consumer protection and equal opportunity law. The report also poses a series of questions to companies to consider when using big data to mitigate these risks.
Johnson, Jeffrey A. 2014. “From open data to information justice. Ethics and Information Technology. 16 (4):263-274.
This paper argues for subsuming the question of open data within a larger question of information justice, with the immediate aim being to establish the need for rather than the principles of such a theory. The author shows that there are several problems of justice that emerge as a consequence of opening data to full public accessibility, and are generally a consequence of the failure of the open data movement to understand the constructed nature of data. The author examines the problems of the embedding of social privilege in datasets as the data is constructed, the differential capabilities of data users (especially differences between citizens and ‘‘enterprise’’ users), and the norms that data systems impose through their function as disciplinary systems. In each cases he shows that open data has the quite real potential to exacerbate rather than alleviate injustices.
Lazer, David. The rise of the social algorithm. Science 348 (6239):1090-1091.doi: 10.1126/science.aab1422
Humanity is in the early stages of the rise of social algorithms: programs that size us up, evaluate what we want, and provide a customized experience. This quiet but epic paradigm shift is fraught with social and policy implications. The evolution of Google exemplifies this shift. It began as a simple deterministic ranking system based on the linkage structure among Web sites—the model of algorithmic Fordism, where any color was fine as long as it was black (1). The current Google is a very different product, personalizing results (2) on the basis of information about past searches and other contextual information, like location. On page 1130 of this issue, Bakshy et al. (3) explore whether such personalized curation on Facebook prevents users from accessing posts presenting conflicting political views.
Kernaghan, Kenneth. 2014. "Digital dilemmas: Values, ethics and information technology." Canadian Public Administration 57 (2):295-317. doi: 10.1111/capa.12069.
In writings on public administration, the subject areas of values and ethics and of information technology ( IT) have received substantial, but largely separate, attention. The public administration community can benefit by drawing on scholarship in the field of information and computer ethics and developing its own body of research with a view to sensitizing public servants to the effects of changes in IT on values and ethics. This article focuses on developments in the use of IT (for example, self-service technologies, Big Data, the Internet of Things) as a basis for assessing their implications for public sector values and ethics.
King, Gary. 2011. "Ensuring the Data-Rich Future of the Social Sciences." Science 331 (6018):719-721. doi: 10.1126/science.1197872.
Massive increases in the availability of informative social science data are making dramatic progress possible in analyzing, understanding, and addressing many major societal problems. Yet the same forces pose severe challenges to the scientific infrastructure supporting data sharing, data management, informatics, statistical methodology, and research ethics and policy, and these are collectively holding back progress. The author addresses these changes and challenges and suggest what can be done.
Kosinski, Michal, Sandra C. Matz, Samuel D. Gosling, Vesselin Popov, and David Stillwell. 2015. "Facebook as a research tool for the social sciences: Opportunities, challenges, ethical considerations, and practical guidelines." American Psychologist 70 (6):543-556. doi: 10.1037/a0039210.
Facebook is rapidly gaining recognition as a powerful research tool for the social sciences. It constitutes a large and diverse pool of participants, who can be selectively recruited for both online and offline studies. Additionally, it facilitates data collection by storing detailed records of its users’ demographic profiles, social interactions, and behaviors. With participants’ consent, these data can be recorded retrospectively in a convenient, accurate, and inexpensive way. Based on our experience in designing, implementing, and maintaining multiple Facebook-based psychological studies that attracted over 10 million participants, we demonstrate how to recruit participants using Facebook, incentivize them effectively, and maximize their engagement. We also outline the most important opportunities and challenges associated with using Facebook for research, provide several practical guidelines on how to successfully implement studies on Facebook, and finally, discuss ethical considerations.
Macer, Tim. 2016. "Analytics, Ethics and Market Research." Research World 2016 (56):41-43. doi: 10.1002/rwm3.20326.
Big data can mean big risks if you don't have a sound legal and ethical basis for integrating the data into your research. This is the view of three individuals I spoke to, whose job it is to consider the legal and ethical use of big data, and how to reconcile the rights of private individuals over the use of their data with the opportunities for economic and social benefits it can bring.
Martin, Kirsten. 2016. "Data aggregators, consumer data, and responsibility online: Who is tracking consumers online and should they stop?" Information Society 32 (1):51-63. doi: 10.1080/01972243.2015.1107166.
The goal of this article is to examine the strategic choices of firms collecting consumer data online and to identify the roles and obligations of the actors within the current network of online tracking.
Martin, Kirsten E. 2015. "Ethical Issues in the Big Data Industry." MIS Quarterly Executive 14 (2):67-85.
Big Data combines information from diverse sources to create knowledge, make better predictions and tailor services. This article analyzes Big Data as an industry, not a technology, and identifies the ethical issues it faces. These issues arise from reselling consumers’ data to the secondary market for Big Data. Remedies for the issues are proposed, with the goal of fostering a sustainable Big Data Industry.
McNeely, Connie L., and Jong-on Hahm. 2014. "The Big (Data) Bang: Policy, Prospects, and Challenges." Review of Policy Research 31 (4):304-310. doi: 10.1111/ropr.12082.
Big data is increasingly the cornerstone on which policy making is based. However, with potential benefits and applications come challenges and dilemmas. In this set of symposium articles, authors examine the promise and problems of big data, exploring associated prospects, risks, parameters, and payoffs from a variety of perspectives. The articles address myriad challenges in the handling of big data sets, such as collection, validation, integrity, and security; ontological issues attending data analytics and conceptual transformations; the foundations of big data collection for social science research; the gap between the acquisition of data and its use to advance discovery and innovation; the costs and benefits of using big data in decision making and analysis; and, finally, related problems of privacy, security, and ethics. Issues such as these will continue to arise with increased use of big data as fundamental to policy making and governance in today's growing information society.
Michael, Mike, and Deborah Lupton. 2016. "Toward a manifesto for the ‘public understanding of big data’." Public Understanding of Science 25 (1):104-116. doi: 10.1177/0963662515609005.
This article sketches h a ‘manifesto’ for the ‘public understanding of big data’. On the one hand, this entails such public understanding of science and public engagement with science and technology–tinged questions as follows: How, when and where are people exposed to, or do they engage with, big data? Who are regarded as big data’s trustworthy sources, or credible commentators and critics? What are the mechanisms by which big data systems are opened to public scrutiny? On the other hand, big data generate many challenges for public understanding of science and public engagement with science and technology: How do we address publics that are simultaneously the informant, the informed and the information of big data? What counts as understanding of, or engagement with, big data, when big data themselves are multiplying, fluid and recursive? As part of our manifesto, we propose a range of empirical, conceptual and methodological exhortations.
Nissenbaum, Helen. 2015. “Assuring a Role for ‘Respect for Context’ in Protecting Privacy.” In Privacy in the Modern Age: The Search for Solutions, edited by Marc Rotenberg, Jeramie Scott, and Julia Horwitz. The New Press.
An excellent introduction to changing concepts of privacy in the field of big data.
Ovadia, Steven. 2013. "The Role of Big Data in the Social Sciences." Behavioral & Social Sciences Librarian 32 (2):130-134. doi: 10.1080/01639269.2013.787274.
The article explores the important role played by big data in the social sciences. From an academic perspective, big data is defined as the interplay of technology, analysis and mythology. Former Amazon.com chief scientist Andreas Weigend has expanded the idea of social data beyond the analysis of services like Facebook and Twitter. It is suggested that librarians can play an important role in the facilitation and sharing of big data as they are free to pursue their own work with big data sets.
Panger, Galen. 2016. "Reassessing the Facebook experiment: critical thinking about the validity of Big Data research." Information, Communication & Society 19 (8):1108-1126. doi: 10.1080/1369118X.2015.1093525.
The Facebook experiment of 2014 manipulated the contents of nearly 700,000 users’ News Feeds to induce changes in their emotions. This experiment was widely criticized on ethical grounds regarding informed consent. This controversy, however, diverted attention from a more important concern the experiment was intended to address, which is the impact of Facebook use on well-being. In this paper, the author explores the well-being concerns raised by prior research and argue that the experiment does not alleviate them, owing to poor research design. As the question of Facebook's impact on well-being is of great importance, both to Facebook and to society overall, there is a pressing need for more experimental research that is both sensitive to informed consent and carefully designed to yield reliable results. In turn, the lessons of this case have implications for general issues of validity that emerge in Big Data research, now in vogue at major scientific venues.
Poor, Nathaniel and Roei Davidson. 2017. “Case Study: The Ethics of Using Hacked Data: Patreon’s Data Hack and Academic Data Standards.” Council for Big Data, Ethics, and Society.
Should researchers utilize hacked datasets that have been released in public forums? This case study discusses the ethical arguments for and against utilizing hacked crowdfunding data for academic research.
Popov, Vesselin, Samuel D. Gosling, Michal Kosinski, Sandra C. Matz, and David Stillwell. 2015. "Facebook as a Research Tool for the Social Sciences." American Psychologist 70 (6):543-556. doi: 10.1037/a0039210.
Facebook is rapidly gaining recognition as a powerful research tool for the social sciences. It constitutes a large and diverse pool of participants, who can be selectively recruited for both online and offline studies. Additionally, it facilitates data collection by storing detailed records of its users' demographic profiles, social interactions, and behaviors. With participants' consent, these data can be recorded retrospectively in a convenient, accurate, and inexpensive way. Based on our experience in designing, implementing, and maintaining multiple Facebook-based psychological studies that attracted over 10 million participants, we demonstrate how to recruit participants using Facebook, incentivize them effectively, and maximize their engagement. We also outline the most important opportunities and challenges associated with using Facebook for research, provide several practical guidelines on how to successfully implement studies on Facebook, and finally, discuss ethical considerations.
Qiu, Jack Linchuan. 2015. "Reflections on Big Data: ‘Just because it is accessible does not make it ethical’." Media, Culture & Society 37 (7):1089-1094. doi: 10.1177/0163443715594104.
Drawing from observations in China and from world history, this is a reflection on boyd and Crawford’s provocation on social problems related to Big Data, especially ‘Just because it is accessible does not make it ethnical’.
Richards, Neil M., and Jonathan H. King. 2014. "Big Data Ethics." Wake Forest Law Review 49 (2):393-432.
In this paper, the authors argue that big data, broadly defined, is producing increased powers of institutional awareness and power that require the development of a Big Data Ethics. In Part I, they trace the origins and rapid growth of the Information Revolution. In Part II, they call for the development of a “Big Data Ethics,” a set of four related principles that should govern data flows in our information society, and inform the establishment of big data norms. First, we must recognize “privacy” as an inevitable system of information rules rather than merely secrecy. Second, we must recognize that shared private information can remain “confidential.” Third, we must recognize that big data requires transparency. Fourth, we must recognize that big data can compromise identity.
Rubel, Alan, and Kyle M. L. Jones. 2016. "Student privacy in learning analytics: An information ethics perspective." Information Society 32 (2):143-159. doi: 10.1080/01972243.2016.1130502.
Higher education institutions have started using big data analytics tools. By gathering information about students as they navigate information systems, learning analytics employs techniques to understand student behaviors and to improve instructional, curricular, and support resources and learning environments. However, learning analytics presents important moral and policy issues surrounding student privacy. The authors argue that there are five crucial questions about student privacy that we must address in order to ensure that whatever the laudable goals and gains of learning analytics, they are commensurate with respecting students' privacy and associated rights, including (but not limited to) autonomy interests. They address information access concerns, the intrusive nature of information-gathering practices, whether or not learning analytics is justified given the potential distribution of consequences and benefits, and issues related to student autonomy.
Schneider, Karl F., David S. Lyle, and Francis X. Murphy. 2015. "Framing the Big Data Ethics Debate for the Military." JFQ: Joint Force Quarterly (77):16-23.
The author reflects on the ethics of big data and its relevance to the military. Topics discussed include understanding ethics of big data and its applications for increased understanding of heterogeneity, challenges of big data including costs associated with use and analysis, use of personally identifiable information and policy formulation, and firewall framework for military big data to protect the data and its use in U.S. Army's talent management initiative Green Pages.
Schroeder, Ralph, and Josh Cowls. 2014. "Big data, ethics, and the social implications of knowledge production." GeoJournal.
This position paper addresses current debates about data in general, and big data specifically, by examining the ethical issues arising from advances in knowledge production. Typically, ethical issues such as privacy and data protection are discussed in the context of regulatory and policy debates. Here we argue that this overlooks a larger picture whereby human autonomy is undermined by the growth of scientific knowledge. To make this argument, we first offer definitions of data and big data, and then examine why the uses of data-driven analyses of human behaviour in particular have recently experienced rapid growth. Next, we distinguish between the contexts in which big data research is used, and argue that this research has quite different implications in the context of scientific as opposed to applied research. We conclude by pointing to the fact that big data analyses are both enabled and constrained by the nature of data sources available. Big data research will nevertheless inevitably become more pervasive, and this will require more awareness on the part of data scientists, policymakers and a wider public about its contexts and often unintended consequences.
Steinmann, Michael, Julia Shuster, Jeff Collmann, Sorin Adam Matei, Rochelle E. Tractenberg, Kevin FitzGerald, Gregory J. Morgan, and Douglas Richardson. 2015. "Embedding Privacy and Ethical Values in Big Data Technology." In Transparency in Social Media, pp. 277-301. Springer International Publishing.
The phenomenon now commonly referred to as “Big Data” holds great promise and opportunity as a potential source of solutions to many societal ills ranging from cancer to terrorism; but it might also end up as “. . .a troubling manifestation of Big Brother, enabling invasions of privacy, decreased civil freedoms (and) increased state and corporate control” (Boyd & Crawford, 2012, p. 664). Discussions about the use of Big Data are widespread as “(d)iverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people” (Boyd & Crawford, 2012, p. 662). This chapter attempts to establish guidelines for the discussion and analysis of ethical issues related to Big Data in research, particularly with respect to privacy. In doing so, it adds new dimensions to the agenda setting goal of this volume. It is intended to help researchers in all fields, as well as policy-makers, to articulate their concerns in an organized way, and to specify relevant issues for discussion, policy-making and action with respect to the ethics of Big Data. On the basis of our review of scholarly literature and our own investigations with big and small data, we have come to recognize that privacy and the great potential for privacy violations constitute major concerns in the debate about Big Data. Furthermore, our approach and our recommendations are generalizable to other ethical considerations inherent in Big Data as we illustrate in the final section of the chapter.
Swan, Melanie. 2013. "The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery." Big Data 1 (2):85-99. doi: 10.1089/big.2012.0002.
A key contemporary trend emerging in big data science is the quantified self (QS)–individuals engaged in the self-tracking of any kind of biological, physical, behavioral, or environmental information as n=1 individuals or in groups. There are opportunities for big data scientists to develop new models to support QS data collection, integration, and analysis, and also to lead in defining open-access database resources and privacy standards for how personal data is used. In the long-term future, the quantified self may become additionally transformed into the extended exoself as data quantification and self-tracking enable the development of new sense capabilities that are not possible with ordinary senses. The individual body becomes a more knowable, calculable, and administrable object through QS activity, and individuals have an increasingly intimate relationship with data as it mediates the experience of reality.
Vaidhyanathan, Siva, and Chris Bulock. 2014. "Knowledge and Dignity in the Era of “Big Data”." Serials Librarian 66 (1-4):49-64. doi: 10.1080/0361526X.2014.879805.
Private companies and government agencies are now creating and tapping into vastly more data than ever before. These data flows include enormous amounts of personal information and raise questions about privacy and intellectual policy that could have profound impacts on our lives. While data collection and creation are nearly ubiquitous, the instruments of collection and analysis are often hidden in order to track more natural behavior. Recent revelations of massive governmental data collection offer the country, and librarians in particular, an opportunity to discuss and question the societal implications of “Big Data,” and the policies that govern them.
Vayena, Effy et al., 2016. “Elements of a New Ethical Framework for Big Data Research” Washington and Lee Law Review 72(3): 420-441.
Emerging large-scale data sources hold tremendous potential for new scientific research into human biology, behaviors, and relationships. At the same time, big data research presents privacy and ethical challenges that the current regulatory framework is ill-suited to address. In light of the immense value of large-scale research data, the central question moving forward is not whether such data should be made available for research, but rather how the benefits can be captured in a way that respects fundamental principles of ethics and privacy.
Voosen, Paul. 2015. "After Facebook Fiasco, Big-Data Researchers Rethink Ethics." Chronicle of Higher Education 61 (17):A14-A14.
The article discusses the impact of controversy over an academic study conducted in collaboration with online social networking company Facebook in which Facebook users' were studied without their knowledge, highlighting concerns about the ethics of big-data research. Topics addressed include the views of professor Jeffrey T. Hancock, who worked with Facebook on the study, as well as criticism of the lack of informed consent in the study.
Wang, Yinying. 2016. "Big Opportunities and Big Concerns of Big Data in Education." TechTrends: Linking Research & Practice to Improve Learning 60 (4):381-384. doi: 10.1007/s11528-016-0072-1.
Against the backdrop of the ever-increasing influx of big data, this article examines the opportunities and concerns over big data in education. Specifically, this article first introduces big data, followed by delineating the potential opportunities of using big data in education in two areas: learning analytics and educational policy. Then, the concerns over data security, privacy protection, and ethical boundaries of accessing personal digital data are discussed. The article concludes with an invitation to education practitioners, policymakers, and researchers to advance our understanding of big data and better serve students in the digital era.
Zwitter, Andrej. 2014. "Big Data ethics." Big Data and Society 1 (2). doi: 10.1177/2053951714559253.
The speed of development in Big Data and associated phenomena, such as social media, has surpassed the capacity of the average consumer to understand his or her actions and their knock-on effects. We are moving towards changes in how ethics has to be perceived: away from individual decisions with specific and knowable outcomes, towards actions by many unaware that they may have taken actions with unintended consequences for anyone. Responses will require a rethinking of ethical choices, the lack thereof and how this will guide scientists, governments, and corporate agencies in handling Big Data. This essay elaborates on the ways Big Data impacts on ethical conceptions.