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

About the Author

Aileen Nielsen is a software engineer who has analyzed data in a variety of settings from a physics laboratory to a political campaign to a healthcare startup. She also has a law degree and splits her time between a deep learning startup and research as a Fellow in Law and Technology at ETH Zurich. She has given talks around the world on fairness issues in data and modeling.

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