"This is What Transparency Looks Like"? Opening Data, Including the Public, Aspiring for Accountability

Transparency in government is thought to be a fundamental prerequisite for accountability, or the obligation of public officials to answer to the public and be responsible for how they perform their duties in relation to those they represent. In an ‘explosion’ of transparency, public institutions are now investing heavily in transparency initiatives meant to foster public accountability through online data portals, legal requirements mandating disclosure, and innovations in community engagement and public participation, all designed to open up government to the public view. By all metrics, government transparency is increasing, but it regularly falls short in its aspirations for improved public trust, legitimacy, democratic engagement, and equity, a dilemma that transparency scholars struggle to resolve. Through a focus on local government transparency practices, this dissertation contributes to the understanding of why government transparency is not achieving accountability goals. It helps to refine transparency as a construct and uncover some of the challenges and limitations in how it is being practiced on the ground. My overarching research question is, “How is transparency enrolled in urban governance, and what are its implications for accountability?” I conducted a qualitative study of how transparency is being implemented in local government in Chicago, using primarily participant observation methods to examine several transparency projects active in Chicago between 2019 and 2022. I propose a model of transparency practices, which can be categorized into four modalities: disclosure, data, inclusion, and audit. I then look closely at two exceptionally opaque areas of local government - economic development and the criminal justice system – and show how opacity is being upheld by the tendency of transparency techniques to obscure power relations, but also how transparency can be enhanced by embracing the inherently political nature of data.