Elizabeth Anne Watkins

Data & Society Research Institute

Research Analyst, Intelligence & Autonomy Initiative

36 West 20th Street

11th Floor

New York, , NY 10011

United States

Princeton University Center for Information Technology Policy

C231A E-Quad

Olden Street

Princeton, NJ 08540

United States

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

SCHOLARLY PAPERS

5

DOWNLOADS
Rank 42,481

SSRN RANKINGS

Top 42,481

in Total Papers Downloads

1,593

SSRN CITATIONS

4

CROSSREF CITATIONS

0

Scholarly Papers (5)

1.

Algorithmic Impact Assessments and Accountability: The Co-construction of Impacts

Jacob Metcalf, Emanuel Moss, Elizabeth Anne Watkins, Ranjit Singh, and MadeleineClare Elish. 2021. Algorithmic Impact Assessments and Accountability: TheCo-construction of Impacts. InACM Conference on Fairness, Accountability,and Transparency (FAccT ’21), March 3–10, 2021, Virtual Event, Canada.ACM,
Number of pages: 19 Posted: 12 Feb 2021
Jacob Metcalf, Emanuel Moss, Emanuel Moss, Elizabeth Anne Watkins, Elizabeth Anne Watkins, Ranjit Singh and Madeleine Clare Elish
Data & Society Research Institute, Data & Society Research InstituteCUNY Graduate Center, Princeton University Center for Information Technology PolicyData & Society Research Institute, Data & Society and Google Inc.
Downloads 598 (64,236)
Citation 3

Abstract:

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algorithmic impact assessment, impact, harm, accountability, governance

2.

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.
Number of pages: 13 Posted: 28 May 2020 Last Revised: 14 May 2021
Emanuel Moss, Emanuel Moss, Elizabeth Anne Watkins, Elizabeth Anne Watkins, Jacob Metcalf and Madeleine Clare Elish
Data & Society Research InstituteCUNY Graduate Center, Princeton University Center for Information Technology PolicyData & Society Research Institute, Data & Society Research Institute and Google Inc.
Downloads 515 (77,469)
Citation 2

Abstract:

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algorithmic impact assessment, algorithmic accountability, impact assessment, data ethics, algorithmic accountability, corporate governance

3.

Assembling Accountability: Algorithmic Impact Assessment for the Public Interest

Number of pages: 64 Posted: 08 Jul 2021
Emanuel Moss, Emanuel Moss, Elizabeth Anne Watkins, Elizabeth Anne Watkins, Ranjit Singh, Madeleine Clare Elish and Jacob Metcalf
Data & Society Research InstituteCUNY Graduate Center, Princeton University Center for Information Technology PolicyData & Society Research Institute, Data & Society, Google Inc. and Data & Society Research Institute
Downloads 270 (159,077)
Citation 3

Abstract:

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

4.

Governing Algorithmic Systems with Impact Assessments: Six Observations

AAAI / ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2021
Number of pages: 12 Posted: 18 May 2021
Elizabeth Anne Watkins, Elizabeth Anne Watkins, Emanuel Moss, Emanuel Moss, Jacob Metcalf, Ranjit Singh and Madeleine Clare Elish
Princeton University Center for Information Technology PolicyData & Society Research Institute, Data & Society Research InstituteCUNY Graduate Center, Data & Society Research Institute, Data & Society and Google Inc.
Downloads 140 (285,643)

Abstract:

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algorithmic impact assessment, impact, harm, accountability, governance

5.

The Four-Fifths Rule is Not Disparate Impact: A Woeful Tale of Epistemic Trespassing in Algorithmic Fairness

Parity Technologies, Inc., Technical Report P22-1, v0.3.0
Number of pages: 12 Posted: 24 Feb 2022 Last Revised: 04 Mar 2022
Elizabeth Anne Watkins, Elizabeth Anne Watkins, Michael McKenna and Jiahao Chen
Princeton University Center for Information Technology PolicyData & Society Research Institute, Senior Data Scientist and Parity
Downloads 70 (447,905)

Abstract:

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disparate impact, AI ethics, discrimination law, metrics, fairness, bias, optimization, employment, civil rights