Machine Learning for High-Risk Applications

Preface

Today, machine learning (ML) is the most commercially viable subdiscipline of artificial intelligence (AI). ML systems are used to make high-risk decisions in employment, bail, parole, lending, security, and in many other high-impact applications throughout the world’s economies and governments. In a corporate setting, ML systems are used in all parts of an organization—from consumer-facing products, to employee assessments, to back-office automation, and more. Indeed, the past decade has brought with it even wider adoption of ML technologies. But it has also proven that ML presents risks to its operators, consumers, and even the general public.

Like all technologies, ML can fail—whether by unintentional misuse or intentional abuse. As of 2023, there have been thousands of public reports of algorithmic discrimination, data privacy violations, training data security breaches, and other harmful incidents. Such risks must be mitigated before organizations, and the public, can realize the true benefits of this exciting technology. Addressing ML’s risks requires action from practitioners. While nascent standards, to which this book aims to adhere, have begun to take shape, the practice of ML still lacks broadly accepted professional licensing or best practices. That means it’s largely up to individual practitioners to hold themselves accountable for the good and bad outcomes of their technology when it’s deployed into the world. Machine Learning for High-Risk Applications