We have seen through many examples that data science and artificial intelligence can reinforce structural inequalities like sexism and racism. Data is power, and that power is distributed unequally. This book offers a vision for a feminist data science that can challenge power and work towards justice. This book takes a stand against a world that benefits some (including the authors, two white women) at the expense of others. It seeks to provide concrete steps for data scientists seeking to learn (...) how feminism can help them work towards justice, and for feminists seeking to learn how their own work can carry over to the growing field of data science. It is addressed to professionals in all fields where data-driven decisions are being made, as well as to communities that want to better understand the data that surrounds them. It is written for everyone who seeks to better understand the charts and statistics that they encounter in their day-to-day lives, and for everyone who seeks to better communicate the significance of such charts and statistics to others. This is an example-driven book written with a broad audience of scholars, students, and practitioners in mind. It offers a way of thinking about data, both their uses and their limits, that is informed by direct experience, by a commitment to action, and by the ideas associated with intersectional feminist thought. (shrink)

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. (shrink)

Predictive uses of data are becoming widespread in institutional settings as actors seek to anticipate people and their activities. Predictive modeling is increasingly the subject of scholarly and public criticism. Less common, however, is scrutiny directed at the data that inform predictive models beyond concerns about homogenous training data or general epistemological critiques of data. In this paper, I draw from a qualitative case study set in higher education in the United States to investigate the making of data. Data analytics (...) projects at universities have become more pervasive and intensive to better understand and anticipate undergraduate student bodies. Drawing from 12 months of ethnographic research at a large public university, I analyze the ways data personnel at the institution—data scientists, administrators, and programmers—sort student data into “attributes” and “behaviors,” where “attributes” are demographic data that students “can’t change.” “Behaviors,” in contrast, are data defined as reflective of what students can choose: attending and paying attention in class, studying on campus, among other data which personnel categorize as what students have control over. This discursive split enables the institution nudge students to make responsible choices according to behavior data that correlate with success in the predictive model. In discussing how personnel type, sort, stabilize, and nudge on behavior data, this paper examines the contingencies of data making processes and implications for the application of student data. (shrink)

The increasing availability of digital data reflecting economic and human development, and in particular the availability of data emitted as a by-product of people’s use of technological devices and services, has both political and practical implications for the way people are seen and treated by the state and by the private sector. Yet the data revolution is so far primarily a technical one: the power of data to sort, categorise and intervene has not yet been explicitly connected to a social (...) justice agenda by the agencies and authorities involved. Meanwhile, although data-driven discrimination is advancing at a similar pace to data processing technologies, awareness and mechanisms for combating it are not. This paper posits that just as an idea of justice is needed in order to establish the rule of law, an idea of data justice – fairness in the way people are made visible, represented and treated as a result of their production of digital data – is necessary to determine ethical paths through a datafying world. Bringing together the emerging scholarly perspectives on this topic, I propose three pillars as the basis of a notion of international data justice: visibility, engagement with technology and antidiscrimination. These pillars integrate positive with negative rights and freedoms, and by doing so challenge both the basis of current data protection regulations and the growing assumption that being visible through the data we emit is part of the contemporary social contract. (shrink)

As information systems transform our world, computer scientists design affordances that influence the uses and impacts of these technological objects. This article describes how the practices of design affect the social values materialized in emerging technologies, and explores how design practices can encourage ethical reflection and action. The article presents an ethnography of a laboratory that engineered software for mobile phones to track users’ locations, habits, and behaviors. This technical work raised a number of ethical challenges, particularly around questions of (...) data use and surveillance. The ethnography suggests that particular activities within laboratories can help engineers agree on social values as important to design. It characterizes these activities as values levers: practices that open new conversations about social values and encourage consensus around those values as design criteria. Laboratory leaders and advocates can enable and strengthen these levers to encourage ethical reflection and action as an explicit part of design practice. (shrink)

In recent years, many qualitative sociologists, anthropologists, and social theorists have critiqued the use of algorithms and other automated processes involved in data science on both epistemological and political grounds. Yet, it has proven difficult to bring these important insights into the practice of data science itself. We suggest that part of this problem has to do with under-examined or unacknowledged assumptions about the relationship between the two fields—ideas about how data science and its critics can and should relate. Inspired (...) by recent work in Science and Technology Studies on interventions, we attempted to stage an encounter in which practicing data scientists were asked to analyze a corpus of critical social science literature about their work, using tools of textual analysis such as co-word and topic modelling. The idea was to provoke discussion both about the content of these texts and the possible limits of such analyses. In this commentary, we reflect on the planning stages of the experiment and how responses to the exercise, from both data scientists and qualitative social scientists, revealed some of the tensions and interactions between the normative positions of the different fields. We argue for further studies which can help us understand what these interdisciplinary tensions turn on—which do not paper over them but also do not take them as given. (shrink)

For decades, scholars in the social sciences and humanities have questioned the appropriateness and utility of prior review of their research by human subjects' ethics committees. This essay seeks to organize thematically some of their published complaints and to serve as a brief restatement of the major critiques of ethics review. In particular, it argues that 1) ethics committees impose silly restrictions, 2) ethics review is a solution in search of a problem, 3) ethics committees lack expertise, 4) ethics committees (...) apply inappropriate principles, 5) ethics review harms the innocent, and 6) better options exist. (shrink)

What could a social-justice oriented, feminist data studies look like? The current datalogical turn foregrounds the digital datafication of everyday life, increasing algorithmic processing and data as an emergent regime of power/knowledge. Scholars celebrate the politics of big data knowledge production for its omnipotent objectivity or dismiss it outright as data fundamentalism that may lead to methodological genocide. In this feminist and postcolonial intervention into gender-, race- and geography-blind ‘big data’ ideologies, I call for ethical, anti-oppressive digital data-driven research in (...) the social sciences and humanities. I argue that a reflexive data scholarship can emerge from the reintegration of feminist and postcolonial science studies and ethics of care ideals. Although it is not a panacea for all ails of data mining, I offer a road map for an alternative data-analysis practice that is more power-sensitive and accountable. By incorporating a people-centric and context-aware perspective that acknowledges relationships of dependency, reflects on temptations, and scrutinises benefits and harm, an ‘asymmetrically reciprocal’ (Young, 1997) research encounter may be achieved. I bring this perspective to bear on experiences of a two-year research project with eighty-four young Londoners on digital identities and living in a highly diverse city. I align awareness of uneven relations of power and knowledge with the messy relation of dependency between human and non-human actors in data analysis. This framework productively recognises that digital data cannot be expected to speak for itself, that data do not emerge from a vacuum, and that isolated data patterns cannot be the end-goal of a situated and reflexive research endeavor. Data-driven research, in turn, shows the urgency for renewed feminist ethical reflection on how digital mediation impacts upon responsibility, intersectional power relations, human subjectivity and the autonomy of research participants over their own data. (shrink)

This paper explores the politics of representing events in the world in the form of data points, data sets, or data associations. Data collection involves an act of seeing and recording something that was previously hidden and possibly unnamed. The incidences included in a data set are not random or unrelated but stand for coherent, classifiable phenomena in the world. Moreover, for data to have an impact on law and policy, such information must be seen as actionable, that is, the (...) aggregated data must show people both something they can perceive and something that demands interrogation, explanation, or resolution. Actionable data problematize the taken-for-granted order of society by pointing to questions or imbalances that can be corrected or rectified, or simply better understood, through systematic compilations of occurrences, frequencies, distributions, or correlations. The paper describes and analyzes three different modes of authorized seeing that render data on global environmental phenomena such as climate change both visible and actionable. It argues that the political force of environmental data compilations derives from the divergent epistemological standpoints and expert practices associated with producing views from nowhere, everywhere, and somewhere. (shrink)

Scholars in science and technology studies (STS) have recently been called upon to advise governments on the design of procedures for public engagement. Any such instrumental function should be carried out consistently with STS’s interpretive and normative obligations as a social science discipline. This article illustrates how such threefold integration can be achieved by reviewing current US participatory politics against a 70-year backdrop of tacit constitutional developments in governing science and technology. Two broad cycles of constitutional adjustment are discerned: the (...) first enlarging the scope of state action as well as public participation, with liberalized rules of access and sympathetic judicial review; the second cutting back on the role of the state, fostering the rise of an academic-industrial complex for technology transfer, and privatizing value debates through increasing delegation to professional ethicists. New rules for public engagement in the United Sates should take account of these historical developments and seek to counteract some of the anti-democratic tendencies observable in recent decades. (shrink)

This article argues that the study conducted by Facebook in conjunction with Cornell University did not have sufficient ethical oversight, and neglected in particular to obtain necessary informed consent from the participants in the study. It establishes the importance of informed consent in Internet research ethics and suggests that in Facebook’s case, a reasonable shift could be made from traditional medical ethics ‘effective consent’ to a ‘waiver of normative expectations’, although this would require much-needed change to the company’s standard practice. (...) Finally, it gives some practical recommendations for how to implement such consent strategies, and how the ethical oversight gap between university-led research and industry-led research can be bridged, potentially using emerging Responsible Research and Innovation frameworks which are currently gathering momentum in Europe. (shrink)

This special issue of Science, Technology, & Human Values assembles papers that consider relations among science, ethics, and justice. The papers are drawn from a 2011 National Science Foundation-sponsored workshop that brought together interdisciplinary scholars to consider, incorporate, and attend to the meanings, uses, and social consequences of ethical questions and justice ideals in technoscientific projects. The papers included in this special issue examine key areas that emerged from this workshop, including public participation, the production of knowledge, what counts as (...) consent, ownership of biomaterials, and others. Together, the papers raise questions about new directions and articulations of power, justice, and inequalities in science and technology studies. (shrink)

The legal basis for processing personal data and some other types of Big Data is often the informed consent of the data subject involved. Many data controllers, such as social network sites, offer terms and conditions, privacy policies or similar documents to which a user can consent when registering as a user. There are many issues with such informed consent: people get too many consent requests to read everything, policy documents are often very long and difficult to understand and users (...) feel they do not have a real choice anyway. Furthermore, in the context of Big Data refusing consent may not prevent predicting missing data. Finally, consent is usually asked for when registering, but rarely is consent renewed. As a result, consenting once often implies consent forever. At the same time, given the rapid changes in Big Data and data analysis, consent may easily get outdated. This paper suggests expiry dates for consent, not to settle questions, but to put them on the table as a start for further discussion on this topic. Although such expiry dates may not solve all the issues of informed consent, they may be a useful tool in some situations. (shrink)

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. We 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. The ethical frameworks for Big Data research are highly contested and in flux, and the potential harms of data science research are unpredictable. We examine several contentious cases of research harms in data science, including the 2014 Facebook emotional contagion study and the 2016 use of geographical data techniques to identify the pseudonymous artist Banksy. To address disputes about application of human-subjects research ethics in data science, critical data studies should offer a historically nuanced theory of “data subjectivity” responsive to the epistemic methods, harms and benefits of data science and commerce. (shrink)

Social media offers an attractive site for Big Data research. Access to big social media data, however, is controlled by companies that privilege corporate, governmental, and private research firms. Additionally, Institutional Review Boards’ regulative practices and slow adaptation to emerging ethical dilemmas in online contexts creates challenges for Big Data researchers. We examine these challenges in the context of a feminist qualitative Big Data analysis of the hashtag event #WhyIStayed. We argue power, context, and subjugated knowledges must each be central (...) considerations in conducting Big Data social media research. In doing so, this paper offers a feminist practice of holistic reflexivity in order to help social media researchers navigate and negotiate this terrain. (shrink)

The Snowden leaks, first published in June 2013, provided unprecedented insights into the operations of state-corporate surveillance, highlighting the extent to which everyday communication is integrated into an extensive regime of control that relies on the ‘datafication’ of social life. Whilst such data-driven forms of governance have significant implications for citizenship and society, resistance to surveillance in the wake of the Snowden leaks has predominantly centred on techno-legal responses relating to the development and use of encryption and policy advocacy around (...) privacy and data protection. Based on in-depth interviews with a range of social justice activists, we argue that there is a significant level of ambiguity around this kind of anti-surveillance resistance in relation to broader activist practices, and critical responses to the Snowden leaks have been confined within particular expert communities. Introducing the notion of ‘data justice’, we therefore go on to make the case that resistance to surveillance needs to be conceptualized on terms that can address the implications of this data-driven form of governance in relation to broader social justice agendas. Such an approach is needed, we suggest, in light of a shift to surveillance capitalism in which the collection, use and analysis of our data increasingly comes to shape the opportunities and possibilities available to us and the kind of society we live in. (shrink)

“Informed consent” implicitly links the transmission of information to the granting of permission on the part of patients, tissue donors, and research subjects. But what of the corollary, informed refusal? Drawing together insights from three moments of refusal, this article explores the rights and obligations of biological citizenship from the vantage point of biodefectors—those who attempt to resist technoscientific conscription. Taken together, the cases expose the limits of individual autonomy as one of the bedrocks of bioethics and suggest the need (...) for a justice-oriented approach to science, medicine, and technology that reclaims the epistemological and political value of refusal. (shrink)

This text addresses the central problem in anthropological theory of the late 1990s - the paradox that humans are both products of social discipline and creators of remarkable improvisation.

Science and Democracy in Europe and the United States Sheila Jasanoff. Lippmann, Walter. The Phantom Public. New Brunswick: Transaction Publishers, 1993 [1925]. Litfin, Karen . Ozone Discourses: Science and Politics in Global ...

IRBs in action -- Everyone's an expert? Warrants for expertise -- Local precedents -- Documents and deliberations: an anticipatory perspective -- Setting IRBs in motion in Cold War America -- An ethics of place -- The many forms of consent -- Deflecting responsibility -- Conclusion: the making of ethical research.

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences (...) affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)

In recent years, educational institutions have started using the tools of commercial data analytics in higher education. By gathering information about students as they navigate campus information systems, learning analytics “uses analytic techniques to help target instructional, curricular, and support resources” to examine student learning behaviors and change students’ learning environments. As a result, the information educators and educational institutions have at their disposal is no longer demarcated by course content and assessments, and old boundaries between information used for assessment (...) and information about how students live and work are blurring. Our goal in this paper is to provide a systematic discussion of the ways in which privacy and learning analytics conflict and to provide a framework for understanding those conflicts. -/- We argue that there are five crucial issues 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. First, we argue that we must distinguish among different entities with respect to whom students have, or lack, privacy. Second, we argue that we need clear criteria for what information may justifiably be collected in the name of learning analytics. Third, we need to address whether purported consequences of learning analytics (e.g., better learning outcomes) are justified and what the distributions of those consequences are. Fourth, we argue that regardless of how robust the benefits of learning analytics turn out to be, students have important autonomy interests in how information about them is collected. Finally, we argue that it is an open question whether the goods that justify higher education are advanced by learning analytics, or whether collection of information actually runs counter to those goods. (shrink)

There are good reasons for higher education institutions to use learning analytics to risk-screen students. Institutions can use learning analytics to better predict which students are at greater risk of dropping out or failing, and use the statistics to treat ‘risky’ students differently. This paper analyses this practice using normative theories of discrimination. The analysis suggests the principal ethical concern with the differing treatment is the failure to recognize students as individuals, which may impact on students as agents. This concern (...) is cross-examined drawing on a philosophical argument that suggests there is little or no distinctive difference between assessing individuals on group risk statistics and using more ‘individualized’ evidence. This paper applies this argument to the use of learning analytics to risk-screen students in higher education. The paper offers reasons to conclude that judgment based on group risk statistics does involve a distinctive failure in terms of assessing persons as individuals. However, instructional design offers ways to mitigate this ethical concern with respect to learning analytics. These include designing features into courses that promote greater use of effort-based factors and dynamic rather than static risk factors, and greater use of sets of statistics specific to individuals. (shrink)