Responsible AI In Practice


Enabling responsible development of artificial intelligent technologies is one of the major challenges we face as the field moves from research to practice. Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learning in many current and future real-world applications.  Now there are calls from across the industry (academia, government, and industry leaders) for technology creators to ensure that AI is used only in ways that benefit people and “to engineer responsibility into the very fabric of the technology.”  Overcoming these challenges and enabling responsible development is essential to ensure a future where AI and machine learning can be widely used. In this talk we will discuss Responsible AI tools best practices you could apply in your machine learning lifecycle and share state-of-the-art open source tools you can incorporate to implement Responsible AI in practice.


Minsoo is a Senior Product Manager at Microsoft Azure Machine Learning designing and building out Responsible AI tools for data scientists. She’s worked with OSS tools such as InterpretML, Fairlearn, Responsible AI Toolbox and contributed to the UX of the Responsible AI dashboard now released in Azure Machine Learning. She has bachelor’s degrees in Applied Mathematics and Painting from Brown University and Rhode Island School of Design (RISD). Coming from an interdisciplinary background with experience in building machine learning models and products, analyzing data, and designing UX, she is always finding work at the intersection of AI/ML, design, and social sciences to empower data and ML practitioners to work ethically and responsibly end-to-end.

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