default author photo

Elizabeth Anne Watkins

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/

Data & Society Research Institute

Research Analyst, Intelligence & Autonomy Initiative

36 West 20th Street

11th Floor

New York, , NY 10011

United States

SCHOLARLY PAPERS

5

DOWNLOADS
Rank 37,294

SSRN RANKINGS

Top 37,294

in Total Papers Downloads

3,414

TOTAL CITATIONS
Rank 45,624

SSRN RANKINGS

Top 45,624

in Total Papers Citations

39

Scholarly Papers (5)

1.

Assembling Accountability: Algorithmic Impact Assessment for the Public Interest

Number of pages: 64 Posted: 08 Jul 2021
Emanuel Moss, Elizabeth Anne Watkins, Elizabeth Anne Watkins, Ranjit Singh, Madeleine Clare Elish and Jacob Metcalf
University of Virginia, Princeton University Center for Information Technology PolicyData & Society Research Institute, Data & Society Research Institute, Google Inc. and Data & Society Research Institute
Downloads 1,164 (46,752)
Citation 27

Abstract:

Loading...

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

2.

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, Elizabeth Anne Watkins, Elizabeth Anne Watkins, Ranjit Singh and Madeleine Clare Elish
Data & Society Research Institute, University of Virginia, Princeton University Center for Information Technology PolicyData & Society Research Institute, Data & Society Research Institute and Google Inc.
Downloads 999 (57,604)
Citation 9

Abstract:

Loading...

algorithmic impact assessment, impact, harm, accountability, governance

3.

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, Elizabeth Anne Watkins, Elizabeth Anne Watkins, Jacob Metcalf and Madeleine Clare Elish
University of Virginia, Princeton University Center for Information Technology PolicyData & Society Research Institute, Data & Society Research Institute and Google Inc.
Downloads 921 (64,011)
Citation 2

Abstract:

Loading...

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

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, Jacob Metcalf, Ranjit Singh and Madeleine Clare Elish
Princeton University Center for Information Technology PolicyData & Society Research Institute, University of Virginia, Data & Society Research Institute, Data & Society Research Institute and Google Inc.
Downloads 207 (369,861)
Citation 1

Abstract:

Loading...

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 123 (584,619)

Abstract:

Loading...

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