Author Archives: Shreeharsh Kelkar

I am interested in understanding the role of computing, data, software and algorithms in institutions and workplaces using historical and ethnographic methods. More broadly, I am interested in the relationship between institutions, technology and knowledge production.

Three Perspectives on “Fake News”

Editor's Note: Today, Shreeharsh Kelkar brings us the inaugural post in a new series on Fake News and the Politics of Knowledge. The goal is to tackle the knowledge politics of both so-called "fake news" itself and the discourse that has cropped up around it, from a wide range of theoretical perspectives on media, science, technology, and communication. If you are interested in contributing, please write to editor@castac.org with a brief proposal.  Donald Trump’s shocking upset of Hillary Clinton in the 2016 US Presidential Election brought into wide prominence issues that heretofore had been debated mostly in intellectual and business circles: the question of "filter bubbles," of people who refuse to accept facts (scientific or otherwise), and what these mean for liberal democracies and the public sphere.  All these concerns have now have coalesced around an odd little signifier, "fake news" [1].     (more…)

How (Not) to Talk about AI

Most CASTAC readers familiar with science and technology studies (STS) have probably had conversations with friends—especially friends who are scientists or engineers—that go something like this:  Your friend says that artificial intelligence (AI) is on its way, whether we want it or not.  Programs (or robots, take your pick) will be able to do a lot of tasks that, until now, have always needed humans.  You argue that it's not so simple; that what we're seeing is as much a triumph of re-arranging the world as it is of technological innovation. From your point of view, a world of ubiquitous software is being created; which draws on contingent, flexible, just-in-time, human labor; with pervasive interfaces between humans and programs that make one available to the other immediately. Your comments almost always get misinterpreted as a statement that the programs themselves are not really intelligent.  Is that what you believe, your (more...)

Teaching (Non)Technological Determinism: A Theory of Key Points

How can we account for the radical uncertainty of change when we think about the future, but its seeming inevitability when it comes to the past?  This is, arguably, the hardest part in doing the history and anthropology of technology.  It is also, not surprisingly, the hardest to teach our students.  In what follows, I suggest that the experience of watching (and playing) sports might be of help here. (more…)

Silicon Valley as Ally or Foe? Reflections on the Politics of Income Inequality

The meteoric rise of Bernie Sanders in the Democratic primaries—and the Occupy movement before that—have officially put income inequality on the political radar in the U.S., after years of slow wage growth and a near-catastrophic financial crash. In keeping with the times, Silicon Valley too has begun thinking about inequality. Resident philosopher Paul Graham, venture capitalist and founder of the famous YCombinator startup incubator, wrote an essay on inequality that caused a bit of a ruckus (in Silicon Valley and without). The short version: Graham is not happy with the current rhetorical war on inequality that politicians are waging. He thinks inequality is a natural product of a culture that values startups and innovation, and that a full-scale political fight against inequality is inadvisable. YCombinator recently put out a “Request for Research” to sponsor social science research on Basic Income guarantee schemes. Such a scheme—Silicon Valley’s go-to solution for the (more...)

Trusting Experts: Can we reconcile STS and Social Psychology?

Numerous battles are being fought today within and across America’s political landscape, from global warming to the regulation of new technologies (e.g., GMOs, fracking). Science plays a big role in these debates, and as a result, social psychologists, political scientists, economists, and other social scientists have become interested in the question of why people (or rather, certain people) don’t accept scientific findings. These social scientists have converged on a concept called motivated reasoning: that because our reasoning powers are directed towards particular ends, we tend to pick facts that best fit our needs and motivations. Motivated reasoning, in this explanation, is a universal concept, perhaps a product of evolution; all human beings do it, including experts. It also raises the profoundly disturbing possibility of a scientific end to our Enlightenment hopes that experts—let alone publics—can be rational, that they can neatly separate facts from values and facilitate a harmonious society. Influential science journalists (more...)

How influential was Alan Turing? The tangled invention of computing (and its historiography)

Alan Turing was involved in some of the most important developments of the twentieth century: he invented the abstraction now called the Universal Turing Machine that every undergraduate computer science major learns in college; he was involved in the great British Enigma code-breaking effort that deserves at least some credit for the Allied victory in World War II, and last, but not the least, while working on building early digital computers post-Enigma, he described -- in a fascinating philosophical paper that continues to puzzle and excite to this day -- the thing we now call the Turing Test for artificial intelligence. His career was ultimately cut short, however, after he was convicted in Britain of "gross indecency" (in effect for being gay), and two years later was found dead in an apparent suicide. The celebrations of Turing's birth centenary began three years ago in 2012. As a result, far, far more people (more...)

Crowdsourcing the Expert

"Crowd" and "cloud" computing are exciting new technologies on the horizon, both for computer science types and also for us STS-types (science and technology studies, that is) who are interested in how different actors put them to (different) uses. Out of these, crowd computing is particularly interesting -- as a technique that both improves artificial intelligence (AI) and operates to re-organize work and the workplace. In addition, as Lilly Irani shows, it also performs cultural work, producing the figure of the heroic problem-solving innovator. To this, I want to add a another point: might "human computation and crowdsourcing" (as its practitioners call it) be changing our widely-held ideas about experts and expertise? Here's why. I'm puzzled by how crowdsourcing research both valorizes expertise while at the same time sets about replacing the expert with a combination of programs and (non-expert) humans. I'm even more puzzled by how crowd computing experts rarely specify the nature of (more...)

Speed-Bump, Meet Knee Defender

Bruno Latour's Science in Action remains an unparalleled introduction to science studies because of its conversational style and clever use of the conventions of the "how-to" genre. And Latour has other shorter, more pedagogical, articles that show wonderfully how non-living objects are deeply embedded in complex social relations. But I sometimes wonder if his examples--the door-closer, the speed-bump, or sometimes, even the gun -- are too simple. I worry about teaching these examples to savvy undergraduates in an introductory STS class. Will they just laugh it off dismissing it as obvious? Will they look at it as philosophy, as a conceptual case, rather than as anthropology? Could there be a more immediate example where the politics is not abstract, but more concrete? Where the students can use the immediacy of their own experience, but also where the stakes are higher? (more…)

On the Porous Boundaries of Computer Science

The term "big data" [1] brings up the specter of a new positivism,  as another one in the series of many ideological tropes that have sought to supplant the qualitative and descriptive sciences with numbers and statistics.[2] But what do scientists think of big data? Last year, in a widely circulated blog post titled "The Big Data Brain Drain: Why Science is in Trouble," physicist Jake VanderPlas made the argument that the real reason big data is dangerous is because it moves scientists from the academy to corporations. (more…)

What’s the Matter with Artificial Intelligence?

In the media these days, Artificial Intelligence (henceforth AI) is making a comeback. Kevin Drum wrote a long piece for Mother Jones about what the rising power of intelligent programs might mean for the political economy of the United States: for jobs, capital-labor relations, and the welfare state. He worries that as computer programs become more intelligent day by day, they will be put to more and more uses by capital, thereby displacing white-collar labor. And while this may benefit both capital and labor in the long run, the transition could be long and difficult, especially for labor, and exacerbate the already-increasing inequality in the United States. (For more in the same vein, here’s Paul Krugman, Moshe Vardi, Noah Smith, and a non-bylined Economist piece.) (more…)