Standards for the Control of Algorithmic Bias: The Canadian Administrative Context

This book explores the standards to be applied to machine learning to mitigate disparate impact in automated decision-making and provides recommendations for implementation in Canada's Directive on Automated Decision-Making.

Format: Hardback
Length: 96 pages
Publication date: 04 July 2023
Publisher: Taylor & Francis Ltd

This book delves into the critical examination of the standards that should be applied to machine learning to mitigate the disparate impact of automated decision-making in the realm of automated decision-making. It aims to provide a comprehensive exploration of the standards that can be implemented to proactively enable human rights protections for individuals who are subjected to automated decision-making processes.

In particular, the book offers insightful recommendations for the implementation of these standards in the context of Canada's Directive on Automated Decision-Making. By addressing the challenges and opportunities posed by machine learning, this book seeks to contribute to the development of a more equitable and responsible approach to automated decision-making.

The first chapter of the book provides an overview of the current landscape of automated decision-making, including its potential benefits and risks. It highlights the need for robust standards to ensure that automated decision-making systems are fair, transparent, and accountable. The chapter also explores the historical context of automated decision-making and the legal and ethical frameworks that have been developed to regulate its use.

The second chapter delves into the principles and guidelines that should be followed when designing and implementing automated decision-making systems. It emphasizes the importance of fairness, transparency, and accountability in the development and deployment of these systems. The chapter also discusses the role of human oversight and review in ensuring that automated decision-making systems are operating as intended and are not causing harm to individuals.

The third chapter provides specific recommendations for the implementation of these principles and guidelines in the context of Canada's Directive on Automated Decision-Making. It outlines the steps that organizations can take to assess the risks and benefits of their automated decision-making systems, develop and implement policies and procedures to mitigate the risks, and ensure that individuals have access to effective remedies if they are harmed by these systems.

The fourth chapter explores the challenges and opportunities that arise from the intersection of machine learning and human rights. It highlights the need for interdisciplinary collaboration and the development of new legal and ethical frameworks to address the complex issues that arise from the use of automated decision-making in the context of human rights.

The fifth chapter concludes the book by emphasizing the importance of ongoing monitoring and evaluation of automated decision-making systems. It highlights the need for organizations to continuously assess the impact of their systems on individuals and to make necessary adjustments to ensure that they are operating in a fair, transparent, and accountable manner.

In conclusion, this book provides a valuable contribution to the ongoing debate about the standards that should be applied to machine learning to mitigate the disparate impact of automated decision-making. By offering insightful recommendations for the implementation of these standards in the context of Canada's Directive on Automated Decision-Making, the book seeks to contribute to the development of a more equitable and responsible approach to automated decision-making.

Weight: 252g
Dimension: 143 x 224 x 13 (mm)
ISBN-13: 9781032550220