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Based on a review of task forces, ordinances, and policy toolkits from New York City and Seattle, I demonstrate the ways municipalities and local publics operationalize abstract notions of accountability. Municipal interventions often prioritize revealing computational tools (transparency) and their effects on people (impact assessments). While these two forms of accountability are crucial, they may neglect to examine institutions\u2014and how they change\u2014as they incorporate automated decision systems. I thus propose a political-economic approach that recognizes algorithmic systems as part of municipal institutions and focuses on their role in intensifying data collection and commodification between public agencies and markets. I argue that algorithmic accountability, especially in public agencies, needs to focus on incompetence and asymmetries of power within a network of governments, tech companies, community groups, and technologies. With a mix of transparency, impact assessments, and political economic review, the paper proposes a more comprehensive assessment of automated decision systems through their development, procurement, use, impact, and decommissioning. <\/jats:p>","DOI":"10.1177\/20539517221115426","type":"journal-article","created":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T06:45:42Z","timestamp":1659941142000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":13,"title":["Algorithmic accountability in U.S. cities: Transparency, impact, and political economy"],"prefix":"10.1177","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1690-4237","authenticated-orcid":false,"given":"Burcu","family":"Baykurt","sequence":"first","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, Massachusetts, USA"}]}],"member":"179","published-online":{"date-parts":[[2022,8,7]]},"reference":[{"key":"bibr1-20539517221115426","unstructured":"Ada Lovelace Institute, AI Now Institute and Open Government Partnership (2021) Algorithmic accountability for the public sector. 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