Responsible AI Toolbox: Practical Approaches and Tools for Ethical Machine Learning

Explore the latest practical approaches to responsible AI and learn how open-source and cloud-integrated responsible ML capabilities empower data scientists and developers to better understand and improve ML models. Delve into the challenges of enabling responsible development of artificial intelligent technologies as the field transitions from research to practice. Discover how the Responsible AI Toolbox addresses ethical and legal challenges posed by machine learning in real-world applications. Gain insights from industry experts Minsoo Thigpen and Rachel Kellam as they discuss the importance of engineering responsibility into AI technology. Learn about tools such as InterpretML, Interpret-text, DiCE, Error Analysis, and EconML, and their integration into the Azure Machine Learning platform. Understand how these tools can help identify, diagnose, and mitigate model errors while promoting fair and responsible modeling practices.