Building the NIST AI Risk Management Framework: Workshop #3

IEEE P7000 suite of standards including
IEEE 7010-2020 IEEE Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well-Being and
IEEE 7000-2021 IEEE Standard Model Process for Addressing Ethical Concerns during System Design

Ethics Certification Program for Autonomous and Intelligent Systems (ECPAIS)

Ethically Aligned Design especially the chapter on Well-being

IEEE 1012-2016 , IEEE Standard for System, Software, and Hardware Verification and Validation

IEEE 829-2008, IEEE Standard for Software and System Test Documentation.

IEEE P3119, Standard for the Procurement of Artificial Intelligence and Automated Decision Systems

Privacy, Equity and Justice in Artificial Intelligence , Policy Statement, November 2021.

Democratic Use of Artificial Intelligence , Policy Statement, November 2021.

Artificial Intelligence: Job, Education, Workforce and Diversity , Policy Statement, November 2021.

Effective Governance of Artificial Intelligence , Policy Statement, June 2021.

Jeanna Matthews, Bruce Hedin, Marc Canellas
Trustworthy Evidence for Trustworthy Technology: An Overview of Evidence for Assessing the Trustworthiness of Autonomous and Intelligent Systems
IEEE-USA,
September 29 2022.

K. Karachalios, N. Stern and J, Havens
Measuring What Matters in the Era of Global Warming and Algorithmic Promises, 2019.

Gabriela Bar, Gabriela Wiktorzak, Jeanna Matthews
Four Conditions for Building Trusted AI Systems: Effectiveness, Competence, Accountability, and Transparency
IEEE Beyond Standards, July 13 2021.

C. Ignacio Gutierrez, G. Marchant, K. Michael
Effective and Trustworthy Implementation of AI Soft Law Governance
IEEE Transaction on Technology and Society 2021, February 18 2021.

C. Ignacio Gutierrez, G. Marchant
A Global Perspective of Soft Law Programs for the Governance of Artificial Intelligence

ACM US Public Policy Council (US-ACM)
Statement on Algorithmic Transparency and Accountability ,
January 12 2017.

S. Garfinkel, J. Matthews, S. Shapiro, J. Smith
Toward Algorithmic Transparency and Accountability ,
Communications of the ACM , Vol. 60, No. 9, Page 5, Sept. 2017, 10.1145/3125780.
Panel session at National Press Club (9/14/2017), Summary .

R. Baeza-Yates, T. Chen, N. Diakopoulos, A. Matthews, J. Matthews, E. Moss, A. Rosenthal
ACM US-TPC Comments on NIST Artificial Intelligence Risk Management Framework, January 27 2022.
Comments relative to: NIST Concept Paper , Previous Comments, and Workshop

ACM Code of Ethics and Professional Conduct

J. Matthews
Patterns and Anti-Patterns, Principles and Pitfalls: Accountability and Transparency in AI
Association for the Advancement of Artificial Intelligence (AAAI) AI Magazine , April 2020.
PDF (unformatted preprint, for beautiful PDF use link above)

Catherine Muñoz, Jeanna Matthews, Jorge Pérez
Sistemas de toma de decisiones automatizadas: ¿De qué hablamos cuando hablamos de transparencia y del derecho a una explicación?
Bits de Ciencia, Issue 21, pp. 27-36, 2021.
Full Issue PDF

R. Caplan, J. Donovan, L. Hanson, and J. Matthews
Algorithmic Accountability: A Primer ,
Data and Society Report, April 18 2018.

D. Roselli, J. Matthews, N. Talagala
Managing Bias in AI
Proceedings of the 1st Workshop on Fairness, Accountability, Transparency, Ethics, and Society on the Web , In Conjunction with the Web Conference 2019 San Francisco, CA, May 13-14 2019.
PDF

Isabella Grasso, David Russell, Abigail Matthews, Jeanna Matthews, and Nicholas Record
Applying Algorithmic Accountability Frameworks with Domain-specific Codes of Ethics: A Case Study in Ecosystem Forecasting for Shellfish Toxicity in the Gulf of Maine
Proceedings of the ACM-IMS Foundations of Data Science Conference(FODS-2020), October 18-20 2020.
PDF
Slides