Assembling Accountability: Algorithmic Impact Assessment for the Public Interest

64 Pages Posted: 8 Jul 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

Ranjit Singh

Data & Society Research Institute

Madeleine Clare Elish

Google Inc.; University of Oxford - Oxford Internet Institute

Jacob Metcalf

Data & Society Research Institute

Date Written: June 29, 2021

Abstract

The Algorithmic Impact Assessment is a new concept for regulating algorithmic systems and protecting the public interest. Assembling Accountability: Algorithmic Impact Assessment for the Public Interest is a report that maps the challenges of constructing algorithmic impact assessments (AIAs) and provides a framework for evaluating the effectiveness of current and proposed AIA regimes. This framework is a practical tool for regulators, advocates, public-interest technologists, technology companies, and critical scholars who are identifying, assessing, and acting upon algorithmic harms.

First, report authors Emanuel Moss, Elizabeth Anne Watkins, Ranjit Singh, Madeleine Clare Elish, and Jacob Metcalf analyze the use of impact assessment in other domains, including finance, the environment, human rights, and privacy. Building on this comparative analysis, they then identify common components of existing impact assessment practices in order to provide a framework for evaluating current and proposed AIA regimes. The authors find that a singular, generalized model for AIAs would not be effective due to the variances of governing bodies, specific systems being evaluated, and the range of impacted communities.

After illustrating the novel decision points required for the development of effective AIAs, the report specifies ten necessary components that constitute robust impact assessment regimes.

Keywords: accountability, impact assessment, algorithms, artificial intelligence, governance, policy

Suggested Citation

Moss, Emanuel and Moss, Emanuel and Watkins, Elizabeth and Watkins, Elizabeth and Singh, Ranjit and Elish, Madeleine Clare and Metcalf, Jacob, Assembling Accountability: Algorithmic Impact Assessment for the Public Interest (June 29, 2021). Available at SSRN: https://ssrn.com/abstract=3877437 or http://dx.doi.org/10.2139/ssrn.3877437

Emanuel Moss (Contact Author)

CUNY Graduate Center

New York, NY
United States

Data & Society Research Institute

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

Elizabeth Watkins

Princeton University Center for Information Technology Policy ( email )

C231A E-Quad
Olden Street
Princeton, NJ 08540
United States

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

Data & Society Research Institute ( email )

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

Ranjit Singh

Data & Society Research Institute ( email )

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

Madeleine Clare Elish

Google Inc. ( email )

1600 Amphitheatre Parkway
Second Floor
Mountain View, CA 94043
United States

University of Oxford - Oxford Internet Institute ( email )

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

Jacob Metcalf

Data & Society Research Institute ( email )

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

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