Working women and caste in India: A study of social disadvantage using feature attribution

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dc.creatorJoshi, Kuhu
dc.creatorJoshi, Chaitanya K.
dc.date2019-05-21
dc.date2024-06-21T09:05:54Z
dc.date2024-06-21T09:05:54Z
dc.date.accessioned2026-06-27T15:09:55Z
dc.descriptionWomen belonging to the socially disadvantaged caste-groups in India have historically been engaged in labour-intensive, blue-collar work. We study whether there has been any change in the ability to predict a woman’s work-status and work-type based on her caste by interpreting machine learning models using feature attribution. We find that caste is now a less important determinant of work for the younger generation of women compared to the older generation. Moreover, younger women from disadvantaged castes are now more likely to be working in white-collar jobs.
dc.identifierhttps://hdl.handle.net/10568/146126
dc.identifier.urihttp://hdl.handle.net/123456789/96462
dc.languageen
dc.rightsOpen Access
dc.sourceJoshi, Kuhu; and Joshi, Chaitanya K. 2019. Working women and caste in India: A study of social disadvantage using feature attribution. Presented at the AI for Social Good ICLR2019 Workshop, in Ernest N. Morial Convention Center, New Orleans, United States, May 06, 2019. https://aiforsocialgood.github.io/iclr2019/accepted/track1/pdfs/18_aisg_iclr2019.pdf
dc.subjectinnovation
dc.subjectgender
dc.subjectmachine learning
dc.subjectcapacity development
dc.subjectlabour
dc.subjectworkforce
dc.subjectwomen
dc.subjectcaste systems
dc.subjectfemale labour
dc.titleWorking women and caste in India: A study of social disadvantage using feature attribution
dc.typeConference Paper

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Instituto Internacional de Investigación sobre Políticas Alimentarias (IFPRI)