Practical Fairness: Achieving Fair and Secure Data Models

Fairness is becoming a paramount consideration for data scientists. Mounting evidence indicates that the widespread deployment of machine learning and AI in business and government is reproducing the same biases we're trying to fight in the real world. But what does fairness mean when it comes to code? This practical book covers basic concerns related to data security and privacy to help data and AI professionals use code that's fair and free of bias.

Many realistic best practices are emerging at all steps along the data pipeline today, from data selection and preprocessing to closed model audits. Author Aileen Nielsen guides you through technical, legal, and ethical aspects of making code fair and secure, while highlighting up-to-date academic research and ongoing legal developments related to fairness and algorithms.

Identify potential bias and discrimination in data science models

Use preventive measures to minimize bias when developing data modeling pipelines

Understand what data pipeline components implicate security and privacy concerns

Write data processing and modeling code that implements best practices for fairness

Recognize the complex interrelationships between fairness, privacy, and data security created by the use of machine learning models

Apply normative and legal concepts relevant to evaluating the fairness of machine learning models