Today, machine learning (ML) is the most commercially viable subdiscipline of artificial intelligence (AI). Machine learning systems are used to make high-risk decisions in employment, collateral, parole, lending, security, and many other high-impact applications in economies and governments around the world. In an enterprise environment, machine learning systems are used in all parts of an organization, from consumer-oriented products to employee evaluations, back-office automation, and more. Indeed, the past decade has brought about an even wider adoption of machine learning technologies.

Like all other technologies, machine learning can fail due to unintentional misuse or intentional misconduct. As of 2023, there are thousands of public reports of algorithmic discrimination, data privacy violations, education-related data security breaches, and other harmful incidents. Such risks must be mitigated before institutions and the public can reap the real benefits of this exciting technology. Addressing the risks of machine learning requires the action of experts in this field. While the new standards that this book aims to adhere to have begun to take shape, the practice of machine learning still lacks generally accepted professional licensing or best practices. This means that holding themselves accountable for the good and bad consequences of their technology as it spreads around the world depends largely on the individual practitioners.

So what will you learn when you read this document?

• How can you identify and mitigate the risks associated with machine learning in high-risk applications?
• What frameworks and processes are needed for robust risk management in machine learning systems?
• How do you detect and correct biases in machine learning models to achieve fair and ethical results?
• What cybersecurity measures are essential to protect machine learning systems from attacks?
• How can you optimize machine learning development and deployment to minimize errors and ensure responsible use?