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We review the great variety of critical scholarship on algorithms, automation, and big data in areas of contemporary life both to document where there has been robust scholarship and to contribute to existing scholarship by identifying gaps in our research agenda. We identify five domains with opportunities for further scholarship: (a) China, (b) international interference in democratic politics, (c) civic engagement in Latin American, (d) public services, and (e) national security and foreign affairs. We argue that the time is right to match dedication to critical theory of algorithmic communication with a dedication to empirical research through audit studies, network ethnography, and investigation of the political economy of algorithmic production. 

We define big data as large amounts of information, collected about many people, over multiple devices. We define critical big data research as efforts to demonstrate how flaws — ethical or methodological — in the collection and use and of big have implications for social inequality. There are many critical and creative big data research endeavors around the world. Here we present an annotated catalog of projects that: are both critical and creative in their analysis of big data; have a distinct Principal Investigator (PI) or clear team; and, are producing an identifiable body of public essays, original research, or civic engagement projects. We have catalogued these endeavors with as much descriptive information as possible, and organized projects by the domains of big data critique and creativity in which they are having an impact. We identify some 35 distinct projects, and several dozen individual researchers, artists and civic leaders, operating in 16 domains of inquiry. We recommend expanding critical and creative work in several domains: expanding work in China; supporting policy initiatives in Latin America’s young democracies; expanding work on algorithmic manipulation originating in authoritarian countries; identifying best practices for how public agencies in the United States should develop big data initiatives. We recommend that the next stage of support for these lines of inquiry is to help publicize the output of these projects, many of which are of interest to a handful of specialists but should be made accessible to policy makers, journalists, and the interested public.