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)

I argue that scientific knowledge is collective knowledge, in a sense to be specified and defended. I first consider some existing proposals for construing collective knowledge and argue that they are unsatisfactory, at least for scientific knowledge as we encounter it in actual scientific practice. Then I introduce an alternative conception of collective knowledge, on which knowledge is collective if there is a strong form of mutual epistemic dependence among scientists, which makes it so that satisfaction of the justification condition (...) on knowledge ineliminably requires a collective. Next, I show how features of contemporary science support the conclusion that scientific knowledge is collective knowledge in this sense. Finally, I consider implications of my proposal and defend it against objections. (shrink)

This article examines how the availability of Big Data, coupled with new data analytics, challenges established epistemologies across the sciences, social sciences and humanities, and assesses the extent to which they are engendering paradigm shifts across multiple disciplines. In particular, it critically explores new forms of empiricism that declare ‘the end of theory’, the creation of data-driven rather than knowledge-driven science, and the development of digital humanities and computational social sciences that propose radically different ways to make sense of culture, (...) history, economy and society. It is argued that: Big Data and new data analytics are disruptive innovations which are reconfiguring in many instances how research is conducted; and there is an urgent need for wider critical reflection within the academy on the epistemological implications of the unfolding data revolution, a task that has barely begun to be tackled despite the rapid changes in research practices presently taking place. After critically reviewing emerging epistemological positions, it is contended that a potentially fruitful approach would be the development of a situated, reflexive and contextually nuanced epistemology. (shrink)

In a 1955 lecture the physicist Richard Feynman reflected on the place of doubt within scientific practice. ‘Permit us to question, to doubt, to not be sure’, proposed Feynman, ‘it is possible to live and not to know’. In our contemporary world, the science of machine learning algorithms appears to transform the relations between science, knowledge and doubt, to make even the most doubtful event amenable to action. What might it mean to ‘leave room for doubt’ or ‘to live and (...) not to know’ in our contemporary culture, where the algorithm plays a major role in the calculability of doubts? I propose a posthuman mode of doubt that decentres the liberal humanist subject. In the science of machine learning algorithms the doubts of human and technological beings nonetheless dwell together, opening onto a future that is never fully reduced to the single output signal, to the optimised target. (shrink)

Now available in English, Styles of Knowing explores the development of various scientific reasoning processes in cultural-historical context. Influenced by historian Alistair Crombie’s Styles of Scientific Thinking in the European Tradition, Chunglin Kwa organizes his book according to six distinct styles: deductive, experimental, analytical-hypothetical, taxonomic, statistical, and evolutionary. Instead of featuring individual scientific disciplines in different chapters, each chapter explains the historical applications of each style’s unique criterion for good science. Kwa shows also how styles have influenced each other and (...) transformed over time. In a chapter written especially for American audiences, Kwa examines how changes in engineering and technology during the twentieth century affected the balance among the various styles of science. Based on extensive research in Greek and Latin primary sources and numerous modern secondary sources, Kwa demonstrates the heterogeneous nature of scientific discovery. This accessible and innovative introduction to scientific change provides a foundational history for the classroom, historians, and nonspecialists. (shrink)

The prestige of science, derived from its claims to certainty, has adversely affected the humanities. There is, in fact, a “politics of certainty”. Our ability to predict events in a limited sphere has been idealized, engendering dangerous illusions about our power to control nature and eliminate time. In addition, the perception and propagation of science as a bearer of certainty has served to legitimate harmful forms of social, sexual, and political power. Yet, as Ilya Prigogine has argued, renewed attention to (...) the irreducible reality of time has brought us to “the end of certainty”. As we enter the age of uncertainty, there is disagreement about how science should be understood and communicated. Some scientists cling to the ideal of certainty, while others emphasize the creative potential of spontaneity, novelty, and surprise. (shrink)

In light of recent technological innovations and discourses around data and algorithmic analytics, scholars of many stripes are attempting to develop critical agendas and responses to these developments. In this mutual interview, three scholars discuss the stakes, ideas, responsibilities, and possibilities of critical data studies. The resulting dialog seeks to explore what kinds of critical approaches to these topics, in theory and practice, could open and make available such approaches to a broader audience.

Although there is widespread agreement that our epistemic dependence on other people's knowledge is a key ingredient of our cognitive life, the role of trust in this dependence is much more open to debate. Is trust in epistemic authority—or “epistemic trust” for short—an epistemological notion in any sense, or is it simply a bridge-concept that connects our epistemological concerns to moral issues? Should we depict it in terms of the more familiar sociological notion of trust as a basis for cooperation?