Overview of our research
Our goal is to explore how race and racism underlie the foundation of marketing, the market place, consumer technology, and marketing research. To support this research, we started a weekly lab meeting with research-active faculty from around the world and across disciplines. We call this the Technology Race and Prejudice Lab (T.R.A.P. LAB). Work from this lab “will create solutions that simultaneously enhance organizations’ economic, social and environmental outcomes. We meet every week online to explore recent published research and share new findings and get feedback.
T.R.A.P. Lab Affiliates
Who we are...
Kate Christensen, Assistant Professor of Marketing, Indiana University
Rowena Crabbe, Visiting Assistant Professor of Marketing, Virginia Tech
Ilya Gokhman, Lecturer, Leadership, and Business, Oglethorpe University
Aziza Jones, Assistant Professor of Marketing, University of Wisconsin
Steven Shepherd, Associate Professor of Marketing, Oklahoma State
Broderick Turner, Assistant Professor of Marketing, Virginia Tech
Esther Uduehi, Assistant Professor of Marketing,University of Washington
Kalinda Ukanwa, Assistant Professor of Marketing, USC
Yandou Lu, Virginia Tech
Matejas Mackin, Northwestern University
Gayoung Park, Virginia Tech
Mehrnoosh Reshadi, Texas Tech
Pradeep Jacob, Arizona State
Invited Presenters and Speakers
Jennifer Mueller, Professor of Sociology, Skidmore College
Michael Norton, Professor of Marketing, Harvard Business School
Sylvia Perry, Associate Professor of Psychology, Northwestern University
Chris Petsko, Post-doctoral Researcher, Management, Duke University
David Crockett, Professor of Marketing, University of South Carolina
Vinay Prabhu, Chief Scientist, UnifyID
Do you want to help?
You can help code computer vision training data...
We are conducting algorithm audit studies across multiple platforms. A key step is to classify the faces appearing in several important datasets used to train the algorithm. We need volunteers to help assess and classify pictures (e.g., whether the picture includes any recognizable faces and what is the likely racial and ethnic classification of those faces). You may categorize as many or as few images as you please. However, if you feel yourself losing interest, or making errors, please stop for the day. Accuracy is more important than quantity.
Building a pool of diverse participants
Academic research in business is very limited in the number of non-White participants it includes. Are you interested in getting paid to do short and interesting research studies?
We live in a world that uses algorithms to do everything. Decide your credit score. Pick your partner. Choose your fate. We are looking for long term volunteers to upload data profiles to various websites and report the results. Please contact firstname.lastname@example.org for more info.
How to measure and report race...
Do you measure age and gender in your experiments and surveys? You report it, right?
Do you measure race? Report it? Why or Why not? Click here to learn our thinking about this.