MediaBerkman

Aaron Shaw: How I Learned to Stop Worrying and Love Amazon’s Mechanical Turk

July 7th, 2009

The online labor market Amazon Mechnical Turk (or AMT) offers a controversial example of Crowdsourcing by allowing employers to offer micro-payments to a global pool of “Turkers” in exchange for work on small “Human Intelligence Tasks” (called HITs). Aaron Shaw, Research Fellow at the Berkman Center and a Ph.D student at UC Berkeley discusses who’s using AMT, its implications for social scientists, the future of labor markets, and life on the Internet as we know it.

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Please Note: This talk incorporates research-in-progress from the Berkman Center’s Online Cooperation Research in collaboration with Daniel Chen and John Horton. After the event was over, Aaron realized that he neglected to explicitly acknowledge Chen and Horton’s invaluable role in the project during the presentation. Aaron feels terrible about this and sincerely apologizes. He also hopes that you’ll visit their websites (links above) and read at least one of their papers. Daniel and John’s contributions to the field of experimental research on online labor markets include (a) recognizing that AMT could serve as a venue for experimental studies; (b) conducting the earliest labor market experiments on AMT; (c) solving a bunch of difficult problems so that they could make valid causal inference based on the results of these experiments.

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