Grant TypeDissertation Fieldwork Grant
Institutional AffiliationToronto, U. of
Grant numberGr. 10130
Approve DateApril 8, 2021
Project TitleLiu, Yang (Toronto, U. of) "Virtuous Knowing and Data Texture: An Ethnography of Data-Driven Predictions in Canadian Nonprofits"
My research examines how data experts practice “Data Science for Social Good” that deeply invests in the credibility and power of data-driven prediction, while claiming to serve purposes other than profit-making and oppressive political agendas. I take seriously the possibility envisioned and questioned by my potential interlocutors: “Can algorithms function as a mechanism for social good?” My research focuses on how experts use data to profile psychological and behavioral patterns of aid beneficiaries to derive optimal interventions under the rubric of alleviating suffering and improving welfare. I will work with nonprofit organizations and at a “Data Science for Social Good” boot camp, where data scientists translate social issues and humanitarian aspirations into languages decipherable for data-driven approaches. I ask: how do humanitarian reasons as the governance of precarious life (Fassin 2012), audit as numeral governance of accountability (Strathern 2000, Merry 2016), and algorithmic decision-making all come together to reconfigure political engagement by data-driven nonprofit workers? In doing so, I aim to introduce an approach to the ethical implications of data-driven prediction beyond the focus of reactive ethical regulations and bring reflections on how data-driven prediction can challenge our understanding of what could and should count as known, probable, and ethical.