Governing with Algorithmic Impact Assessments: Six Observations

Watkins, Elizabeth and Moss, Emanuel and Metcalf, Jacob and Singh, Ranjit and Elish, Madeleine Clare, Governing Algorithmic Systems with Impact Assessments: Six Observations (May 14, 2021). AAAI / ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), Available at SSRN: https://ssrn.

13 Pages Posted: 28 May 2020 Last revised: 14 May 2021

See all articles by Emanuel Moss

Emanuel Moss

Data & Society Research Institute; CUNY Graduate Center

Elizabeth Anne Watkins

Princeton University Center for Information Technology Policy; Data & Society Research Institute

Jacob Metcalf

Data & Society Research Institute

Madeleine Clare Elish

Google Inc.; University of Oxford - Oxford Internet Institute

Date Written: April 24, 2020

Abstract

Algorithmic impact assessments (AIA) are increasingly being proposed as a mechanism for algorithmic accountability. These assessments are seen as potentially useful for anticipating, avoiding, and mitigating the negative consequences of algorithmic decision-making systems (ADS). At the same time, what an AIA would entail remains under-specified. While promising, AIAs raise as many questions as they answer. Choices about the methods, scope, and purpose of impact assessments structure the possible governance outcomes. 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, the public availability of the assessment, and what the outputs of the assessment might be all shape the forms of accountability that AIA proponents seek to encourage. These considerations remain open, and will determine whether and how AIAs can function as a viable governance mechanism in the broader algorithmic accountability toolkit, especially with regard to furthering the public interest. 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, algorithmic accountability, impact assessment, data ethics, algorithmic accountability, corporate governance

Suggested Citation

Moss, Emanuel and Moss, Emanuel and Watkins, Elizabeth and Watkins, Elizabeth and Metcalf, Jacob and Elish, Madeleine Clare, Governing with Algorithmic Impact Assessments: Six Observations (April 24, 2020). Watkins, Elizabeth and Moss, Emanuel and Metcalf, Jacob and Singh, Ranjit and Elish, Madeleine Clare, Governing Algorithmic Systems with Impact Assessments: Six Observations (May 14, 2021). AAAI / ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), Available at SSRN: https://ssrn., Available at SSRN: https://ssrn.com/abstract=3584818 or http://dx.doi.org/10.2139/ssrn.3584818

Emanuel Moss

Data & Society Research Institute

36 West 20th Street
11th Floor
New York,, NY 10011
United States

CUNY Graduate Center

New York, NY
United States

Elizabeth Watkins

Data & Society Research Institute ( email )

36 West 20th Street
11th Floor
New York,, NY 10011
United States

Princeton University Center for Information Technology Policy ( email )

C231A E-Quad
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Princeton, NJ 08540
United States

HOME PAGE: http://https://citp.princeton.edu/citp-people/watkins/

Jacob Metcalf

Data & Society Research Institute ( email )

36 West 20th Street
11th Floor
New York,, NY 10011
United States

Madeleine Clare Elish (Contact Author)

Google Inc. ( email )

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Second Floor
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United States

University of Oxford - Oxford Internet Institute ( email )

1 St. Giles
University of Oxford
Oxford OX1 3PG Oxfordshire, Oxfordshire OX1 3JS
United Kingdom

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