Doing Academic Research with Amazon Mechanical Turk

Co-sponsored by Social Science Matrix and D-Lab

Mechanical Turk

Amazon Mechanical Turk (MTurk) has become increasingly popular as an online tool for conducting social science research. At the same time that MTurk has dominated research practices, its parent company has come under fire for its labor practices and monopolistic behaviors. What are the specific advantages and downsides of using online crowdsourcing tools like MTurk for conducting research? What practical and/or moral dilemmas might emerge in the course of the research process, and what concrete strategies have scientists developed to address them? Presented as part of the Social Sciences and Data Science event series, this panel will bring together researchers who will share their experience with the platform, as well as others who have written about the social and ethical aspects of MTurk more generally.

Co-sponsored by Social Science Matrix and the UC Berkeley D-Lab, this event will be presented in-person at Social Science Matrix on the UC Berkeley campus (820 Social Sciences Building), and will also be streamed via Zoom. A link to the online stream will be sent to registrants prior to the event.

Register Here

Panelists

Ali Alkhatib is the interim director of the Center for Applied Data Ethics at the University of San Francisco. He applies social science to study human-computer interaction, particularly how people relate to individual algorithmic systems and with algorithmically mediated social ecologies. His past work has covered such topics as how gig work disempowers workers; why AI makes so many frustrating errors at the margins; or how the power AIs wield allows them to get increasingly unhinged from the reality we live in. He studied Computer Science at Stanford while pursuing a PhD, and earned his B.A. in Anthropology & B.S. in Informatics, specializing in human-computer interaction, from UC Irvine.

Stefano DellaVigna is the Daniel Koshland, Sr., Distinguished Professor of Economics and Professor of Business Administration at the University of California, Berkeley. He specializes in Behavioral Economics (Psychology and Economics) and is a co-director of the Berkeley Initiative for Behavioral Economics and Finance. He has published in international journals such as the American Economic Review, the Quarterly Journal of Economics, the Journal of Finance, and the Journal of Labor Economics. He has been a Principal Investigator for an NSF Grant, an Alfred P. Sloan Fellow, and is a Distinguished Teaching Award winner. He was also a co-editor of the Journal of the European Economic Association (JEEA). His recent work has focused on the economics of the media, and in particular the impact on voting (through persuasion) and the study of conflicts of interest; the design of model-based field experiments, including the role of social pressure in charitable giving and voting, and the analysis of scientific journals and in particular editorial choices; the study of reference-dependence for unemployed workers.

Gabriel Lenz is a Professor of Political Science at UC Berkeley. He studies democratic politics, focusing on what leads citizens to make good political decisions, what leads them to make poor decisions, and how to improve their choices. His work draws on insights from social psychology and economics, and his research and teaching interests are in the areas of elections, public opinion, political psychology, and political economy. Although specializing in American democracy, he also conducts research on Canada, UK, Mexico, Netherlands, and Brazil. He has ongoing projects about improving voters’ assessments of the performance of politicians, reducing the role of candidate appearance in elections, and measuring political corruption. His past publications include Follow the Leader?: How Voters Respond to Politicians’ Policies and Performance.

Niloufar Salehi is an Assistant Professor at the School of Information at UC, Berkeley, with an affiliated appointment in EECS. Her research interests are in social computing, participatory and critical design, human-centered AI, and more broadly, human-computer-interaction (HCI). Her work has been published and received awards in premier venues in HCI including ACM CHI and CSCW. Through building computational social systems in collaboration with existing communities, controlled experiments, and ethnographic fieldwork, her research contributes the design of alternative social configurations online.

Serena Chen (moderator) is Professor of Psychology and the Marian E. and Daniel E. Koshland, Jr. Distinguished Chair for Innovative Teaching and Research at the University of California, Berkeley. She is a Fellow of the Society of Personality and Social Psychology, American Psychological Association, and the Association of Psychological Science. Professor Chen was also the recipient of the Early Career Award from the International Society for Self and Identity, and the Distinguished Teaching Award from the Social Sciences Division of the University of California, Berkeley. She was also identified as a Rising Star by the American Psychological Society. Professor Chen is currently the Faculty Director of the Berkeley Collegium and the Vice Chair of Undergraduate Affairs of the Psychology Department.

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