To be successful, policy must anticipate a broad range of constituents. Yet, all too often, technology policy is written with primarily mainstream populations in mind. In this article, drawing on Value Sensitive Design and discount evaluation methods, we introduce a new method—Diverse Voices—for strengthening pre-publication technology policy documents from the perspective of underrepresented groups. Cost effective and high impact, the Diverse Voices method intervenes by soliciting input from “experiential” expert panels. We first describe the method. Then we report on two (...) case studies demonstrating its use: one with a white paper on augmented reality technology with expert panels on people with disabilities, people who were formerly or currently incarcerated, and women; and the other with a strategy document on automated driving vehicle technologies with expert panels on youth, non-car drivers, and extremely low-income people. In both case studies, panels identified significant shortcomings in the pre-publication documents which, if addressed, would mitigate some of the disparate impact of the proposed policy recommendations on these particular stakeholder groups. Our discussion includes reflection on the method, evidence for its success, its limitations, and future directions. (shrink)

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, requirements for public engagement, and processes for establishing acceptable uses. Although the Seattle Surveillance Ordinance was 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 definitional gaps that could hinder future efforts toward algorithmic regulation. We argue that finer-grained distinctions between types of information systems in the language of law and policy, and risk assessment tools integrated into their implementation would strengthen future regulatory efforts by rendering underlying algorithmic components more legible and accountable to political and community stakeholders. (shrink)