Publish Date: 2023-02-10 15:37:38

SPP Seminar Series: In her Shoes: Gendered Labelling in Crowdsourced Safety Perceptions Data from India (Feb 15, 2023)

We are delighted to host a seminar by our very own Prof. Nandana Sengupta on the topic  "In her Shoes: Gendered Labelling in Crowdsourced Safety Perceptions Data from India" on Wednesday, the 15th of February 2023, at noon. See details about the talk, and the speaker bio below. We look forward to seeing you there! 

Speaker: Prof. Nandana Sengupta, SoPP, IIT Delhi
Date: Wednesday, the 15th of February 2023.
Time: 12-1:30 pm
Venue: LH 623


In her Shoes: Gendered Labelling in Crowdsourced Safety Perceptions Data from India


In recent years, there has been a proliferation of women's safety mobile applications in India that crowdsource street safety perceptions to generate `safety maps' which are used by policy makers for urban design and academics for studying mobility patterns. However, men and women's differential access to information and communication technologies (ICTs), and the distinctions between their social and cultural subjective experiences may mitigate the value of crowdsourced safety perceptions data and the predictive ability of machine learning (ML) models utilizing such data. We explore this by collecting and analyzing primary data on safety perceptions from New Delhi, India. Our curated dataset consists of streetviews covering a wide range of neighborhoods for which we obtain subjective safety ratings from both female and male respondents. Simulation experiments where varying proportions of ratings from each gender are assumed missing demonstrate that the predictive ability of standard ML techniques relies crucially on the distribution of data producers. We find that obtaining large amounts of crowdsourced safety labels from male respondents for predicting female safety perceptions is inefficient in a number of scenarios and even undesirable in others. Detailed comparisons between female and male respondents' data demonstrate significant gender differences in safety perceptions and their associated vocabularies. Our results have important implications on the design of platforms relying on crowdsourced data and the insights generated from them.  



Nandana Sengupta is an Assistant Professor at the School of Public Policy, IIT Delhi. She is an applied econometrician using both machine learning and qualitative techniques to inform her work. Her current research is centered on women in STEM and estimating potential biases embedded in machine learning systems in India. Her broader research agenda lies at the intersection of applied machine learning and economics, with a particular focus on Indian labour markets, survey design and algorithmic bias. Nandana received her PhD in Economics from the Tepper School of Business at Carnegie Mellon University in 2015. Prior to joining IIT Delhi she was Assistant Professor at the School of Policy and Governance in Azim Premji University, and a postdoctoral scholar at the University of Chicago. 



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