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)

Residual categories are those which cannot be formally represented within a given classification system. We examine the forms that residuality takes within our information systems today, and explore some silences which form around those inhabiting particular residual categories. We argue that there is significant ethical and political work to be done in exploring residuality.

This paper draws from critical data studies and related fields to investigate police officer-involved homicide data for Los Angeles County. We frame police officer-involved homicide data as a rhetorical tool that can reify certain assumptions about the world and extend regimes of power. We highlight the possibility that this type of sensitive civic data can be investigated and employed within local communities through creative practice. Community involvement with data can create a countervailing force to powerful dominant narratives and supplement activist (...) projects that hold local officials accountable for their actions. Our analysis examines four Los Angeles County police officer-involved homicide data sets. First, we provide accounts of the semantics, granularity, scale and transparency of this local data. Then, we describe a “counter data action,” an event that invited members of the community to identify the limits and challenges present in police officer-involved homicide data and to propose new methods for deriving meaning from these indicators and statistics. (shrink)

In light of arguments that citizen science has the potential to make environmental knowledge and policy more robust and democratic, this article inquires into the factors that shape the ability of citizen science to actually influence scientists and decision makers. Using the case of community-based air toxics monitoring with ‘‘buckets,’’ it argues that citizen science’s effectiveness is significantly influenced by standards and standardized practices. It demonstrates that, on one hand, standards serve a boundary-bridging function that affords bucket monitoring data a (...) crucial measure of legitimacy among experts. On the other hand, standards simultaneously serve a boundary-policing function, allowing experts to dismiss bucket data as irrelevant to the central project of air quality assessment. The article thus calls attention to standard setting as an important site of intervention for citizen science-based efforts to democratize science and policy. (shrink)

Part of understanding the meaning and power of algorithms means asking what new demands they might make of ethical frameworks, and how they might be held accountable to ethical standards. I develop a definition of networked information algorithms as assemblages of institutionally situated code, practices, and norms with the power to create, sustain, and signify relationships among people and data through minimally observable, semiautonomous action. Starting from Merrill’s prompt to see ethics as the study of “what we ought to do,” (...) I examine ethical dimensions of contemporary NIAs. Specifically, in an effort to sketch an empirically grounded, pragmatic ethics of algorithms, I trace an algorithmic assemblage’s power to convene constituents, suggest actions based on perceived similarity and probability, and govern the timing and timeframes of ethical action. (shrink)

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)