Practical Fairness: Achieving Fair and Secure Data Models (Paperback)

Practical Fairness: Achieving Fair and Secure Data Models (Paperback)margin: 0px 0px 14px; padding: 0px; font family: "Amazon Ember", Arial, sans serif; font size: 14px;'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

margin: 0px 0px 14px; padding: 0px; font-family: "Amazon Ember", Arial, sans-serif; font-size: 14px;'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.'margin: -4px 0px 14px; padding: 0px; font-family: "Amazon Ember", Arial, sans-serif; font-size: 14px;'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.