Regulating Explainability in Machine Learning Applications -- Observations from a Policy Design Experiment

Author:

Nahar Nadia1ORCID,Rowlett Jenny2ORCID,Bray Matthew3ORCID,Omar Zahra Abba3ORCID,Papademetris Xenophon3ORCID,Menon Alka3ORCID,Kästner Christian4ORCID

Affiliation:

1. Software and Societal Systems Department, School of Computer Science, Carnegie Mellon University, United States

2. Oberlin College, USA

3. Yale University, USA

4. Carnegie Mellon University, United States

Funder

National Science Foundation

Publisher

ACM

Reference102 articles.

1. [1] Alkhatib, A. and Bernstein, M. 2019. Street-Level Algorithms: A Theory at the Gaps Between Policy and Decisions. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (2019), 1–13.

2. [2] Anderson, R. 2001. Why information security is hard - an economic perspective. Proceedings of the 17th Annual Computer Security Applications Conference (2001), 358–365.

3. FactSheets: Increasing trust in AI services through supplier's declarations of conformity

4. [4] Ayres, I. and Braithwaite, J. 1995. Responsive Regulation: Transcending the Deregulation Debate. Oxford University Press.

5. [5] Baik, J.S. 2020. Data Privacy Against Innovation or Against Discrimination?: The Case of the California Consumer Privacy Act (CCPA). Telematics and Informatics. 52, (2020).

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