Governing Algorithmic Systems with Impact Assessments: Six Observations
AAAI / ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2021
12 Pages Posted: 18 May 2021
Date Written: May 14, 2021
Algorithmic decision-making and decision-support systems (ADS) are gaining influence over how society distributes resources, administers justice, and provides access to opportunities. Yet collectively we do not adequately study how these systems affect people or document the actual or potential harms resulting from their integration with important social functions. This is a significant challenge for computational justice efforts of measuring and governing AI systems. Impact assessments are often used as instruments to create accountability relationships and grant some measure of agency and voice to communities affected by projects with environmental, financial, and human rights ramifications. Applying these tools — through Algorithmic Impact Assessments (AIA) — is a plausible way to establish accountability relationships for ADSs. At the same time, what an AIA would entail remains under-specified; they raise as many questions as they answer. Choices about the methods, scope, and purpose of AIAs structure the conditions of possibility for AI governance. In this paper, we present our research on the history of impact assessments across diverse domains, through a sociotechnical lens, to present six observations on how they co-constitute accountability. Decisions about what type of effects count as an impact; when impacts are assessed; whose interests are considered; who is invited to participate; who conducts the assessment; how assessments are made publicly available, and what the outputs of the assessment might be; all shape the forms of accountability that AIAs engender. Because AlAs are still an incipient governance strategy, approaching them as social constructions that do not require a single or universal approach offers a chance to produce interventions that emerge from careful deliberation.
Keywords: algorithmic impact assessment, impact, harm, accountability, governance
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