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've been 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 AI and data 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 black box model audits. Author Aileen Nielsen guides you through the 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. Write data processing and modeling code that follows fair machine learning best practices Understand complex interrelationships between fairness, privacy, and data security Use preventive measures to minimize bias when developing data modeling pipelines Identify opportunities for bias and discrimination in current data scientist models Detect data pipeline aspects that implicate security and privacy concerns

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