My research answers this question by building large-scale news archival datasets that examine the relationship between workers and technologies going back to the mid-20th century in the U.S. I develop a five-part typology of claims that workers use to argue for governing technology based on Marx’s forms of alienation: (1) control over the product of one’s labor, (2) proximate knowledge of technology, or expertise, (3) personal harm from technology, (4) community harm from technology, and (5) resistance to automation. Using this typology I examine the ways that AI technologies can circumvent certain claims that workers make through the decentralized, complex, systematized nature of these technologies. I argue that using historical precedent allows us to understand what is truly integral to the relationship between work, technology, and human identity even as society undergoes rapid changes. Earlier versions of this work were published in The Oxford Handbook on AI Governance and ACM’s Fairness, Accountability, and Transparency in Computing (FAccT) conference proceedings. In addition to this work, I am currently a researcher on the Responsible and Equitable AI Initiative at the Berkeley AI Research Lab (BAIR) on a project examining how product managers understanding responsibility in relation to AI technologies.

Earlier research explored how organizations navigate tensions between shareholder and stakeholder interests under capitalism. I've published on organizational theory in economic sociology (in Sociology Compass), the historical development of non-profit organizations alongside for-profit corporations in 18th century England and Germany (in Research in the Sociology of Organizations), and how corporations manipulate place-image in response to globalization (in Regional Studies).