Make your AI a trustworthy partner. Build machine learning systems that are explainable, robust, transparent, and optimized for fairness.
An enlightening book that presents a robust methodology to deal with your ML projects.
In Trust in Machine Learning
you will learn:
- What “trustworthiness” means for machine learning
- Evaluating data for biases, privacy, and consent
- Handling adversarial attacks and machine learning security
- Interpretability and transparency across the machine learning pipeline
- Aligning machine learning to your values
- Tackling the negative uses of artificial intelligence
- Ensuring an inclusive development process
- Building AI that works for the social good
Machine learning that works in the lab can make false, unjust, and even unsafe decisions when it’s deployed to the real world. Trust in Machine Learning
is a practical guide to creating AI that you can rely on to handle high-stakes issues. You’ll learn how to build systems that are optimized for trust by reducing bias, handling distribution shift, and making your whole pipeline transparent and interpretable.