Standards for the Control of Algorithmic Bias The Canadian Administrative Context

Governments around the world use machine learning in automated decision-making systems for a broad range of functions. This book seeks to answer the question: what standards should be applied to machine learning to mitigate disparate impact in automated decision-making?

Info autore

Natalie Heisler has advised public- and private-sector organizations around the world in the strategy and deployment of data, analytics, and artificial intelligence for more than twenty years. Natalie brings a unique, multidisciplinary perspective to her work, spanning social, regulatory, policy, and technical dimensions. Natalie has a BA in Psychology, an MSc in Mathematics, and an MA in Political Science and lives in Toronto, Canada.
Maura R. Grossman, JD, PhD, is a research professor in the David R. Cheriton School of Computer Science at the University of Waterloo and an affiliate faculty member at the Vector Institute of Artificial Intelligence, both in Ontario, Canada. She also is principal at Maura Grossman Law, in Buffalo, New York, USA. Professor Grossman's multidisciplinary work falls at the intersection of law, health, technology, ethics, and policy.

Dettagli sul prodotto

• Autori: Natalie Heisler, Maura R. Grossman, Natalie Grossman Heisler
• Editore: Taylor & Francis Ltd.
• Contenuto: Libro
• Forma del prodotto: Tascabile
• Data pubblicazione: 27.05.2025
• Categoria: Scienze sociali, diritto, economia > Diritto > Tematiche generali, enciclopedie; Guide e manuali
• EAN: 9781032550244
• ISBN: 978-1-0-3255024-4
• Numero di pagine: 96
• Categorie: Data Mining, Artificial Intelligence, LAW / General, COMPUTERS / Computer Science, POLITICAL SCIENCE / Public Policy / Social Policy, LAW / Administrative Law & Regulatory Practice, COMPUTERS / Programming / Algorithms, Systems of law, algorithms and data structures, Human Rights Compliance, Privacy & data protection, Privacy and data protection, Algorithms & data structures, Legal aspects of IT, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Data Science / Machine Learning, Digital and information technologies: Legal aspects, public sector algorithms, legal standards automation, fairness assessment methods, transparency in AI systems, administrative law review, government machine learning oversight