Municipal Surveillance Regulation and Algorithmic Accountability
Michael Katell and
P. M. Krafft
No zx2sw, SocArXiv from Center for Open Science
A wave of recent scholarship has warned about the potential for discriminatory harms of algorithmic systems, spurring an interest in algorithmic accountability and regulation. Meanwhile, parallel concerns about surveillance practices have already led to multiple successful regulatory efforts of surveillance technologies—many of which have algorithmic components. Here, we examine municipal surveillance regulation as offering lessons for algorithmic oversight. Taking the 2017 Seattle Surveillance Ordinance as our primary case study and surveying efforts across five other cities, we describe the features of existing surveillance regulation; including procedures for describing surveillance technologies in detail, processes for public engagement, and processes for establishing acceptable uses. Although these surveillance-focused laws were not intended to address algorithmic accountability, we find these considerations to be relevant to the law’s aim of surfacing disparate impacts of systems in use. We also find that in notable cases, government employees did not identify regulated algorithmic surveillance technologies as reliant on algorithmic or machine learning systems, highlighting a definitional gap that could hinder future efforts toward algorithmic regulation. We argue that (i) finer-grained distinctions between types of analytic and information systems in the language of law and policy, and (ii) risk assessment tools integrated into their implementation would both strengthen future regulatory efforts by rendering underlying algorithmic components more legible and accountable to political and community stakeholders.
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