Designing Up with Value-Sensitive Design: Building a Field Guide for Ethical ML Development

Author:

Boyd Karen1

Affiliation:

1. San Diego Workforce Partnership, USA

Funder

National Science Foundation

Publisher

ACM

Reference70 articles.

1. Philip Adler , Casey Falk , Sorelle  A. Friedler , Tionney Nix , Gabriel Rybeck , Carlos Scheidegger , Brandon Smith , and Suresh Venkatasubramanian . 2018. Auditing black-box models for indirect influence. 54, 1 ( 2018 ), 95–122. https://doi.org/10.1007/s10115-017-1116-3 10.1007/s10115-017-1116-3 Philip Adler, Casey Falk, Sorelle A. Friedler, Tionney Nix, Gabriel Rybeck, Carlos Scheidegger, Brandon Smith, and Suresh Venkatasubramanian. 2018. Auditing black-box models for indirect influence. 54, 1 (2018), 95–122. https://doi.org/10.1007/s10115-017-1116-3

2. Julia Angwin and Jeff Larson. 2016. Machine Bias. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing Julia Angwin and Jeff Larson. 2016. Machine Bias. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

3. Studying up

4. When not to design, build, or deploy

5. Solon Barocas and Andrew D. Selbst. 2016. Big data’s disparate impact. (2016). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2477899 Solon Barocas and Andrew D. Selbst. 2016. Big data’s disparate impact. (2016). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2477899

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