Guest speakers bring innovative research investigating migration, infrastructure, livelihoods, gender, and politics. In the 2017–2018 academic year, we hosted development studies scholars working at the intersection of gender and foreign aid, with emphasis on aid-giving institutions in the Global North — CIDA in Canada, USAID in the US.

The 2018–2019 series takes off from where we left in March: the politics of numbers behind development. Numbers — or to use a more current term, data (usually connoting numerical data) — are ubiquitous. Business organisations have talked for years about “data as the new oil”; governments, transnational aid agencies, Bretton Woods organisations, and others have turned to carefully exploring “data” as the new twenty-first century asset. The focus on numerical data — Big Data, machine learning, artificial intelligence — as the bedrock of informed policy and business decision-making is instrumental to understanding emerging changes in development practice.

There is a long history of computational techniques in development aid work. But with new media technologies, machine-readable data sets, and large-scale digital infrastructures, much of what USAID, CIDA, UKAID, Gates, MSDF, Omidyar, and others do is changing with particular data analytics and computing practices. This new “data” moment joins relatively older debates in ICTD, in mass media for development, and in internet for development. Microfinance now depends on cell phone data to predict financial behaviour; development projects are increasingly monitored on impact investment metrics; development impact bonds proliferate; public health work involves technology partners with the “best” data collection applications for malaria and TB interventions; insurance companies scramble for the “best” data on prospective clients.

What do we make of this “Smartness Mandate” (Orit Halpern, forthcoming) within development? Does attention to audit practices — say, of the Bill Gates Foundation — help us understand how calculative, computer-aided practices shape what development does, is, and becomes? The “Will to Improve” that Tania Li (2007) theorised a decade ago is now increasingly a human–computer assemblage of datasets being mined with (un)sophisticated machine learning. STS, information science, geography, anthropology, and environmental history have taken “infrastructure” as a matter of concern; STS and anthropology’s focus on multiple ontologies opens new avenues for older concerns in development studies.

With these reflections in mind, the theme for 2018–2019 is Development Infrastructures. We invite scholars from information science, history of science, anthropology, geography, and STS to reflect on smartness, data-driven development, design in development, data and global health, data and state welfare.

For more, please see the seminar website.