Responsible AI Toolbox: A New Open-Source Machine Learning Model Assessment…

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Responsible AI Toolbox: A New Open-Source Machine Learning Model Assessment…

November 16, 2021 @ 12:00 pm - 1:30 pm EST

Speaker: Minsoo Thigpen & Rachel Kellam, Technical Program Manager, Azure Machine Learning & Technical Program Manager, Responsible AI

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 are discussing the latest practical approaches to responsible AI and demonstrate how our latest open source and cloud integrated responsible ML capabilities empower data scientists and developers to understand and improve ML models better.

What You’ll Learn:
In this talk, we will showcase the new release of the Responsible AI Toolbox, which integrates together several Responsible AI tools like Error Analysis and the Interpretability dashboard as well as brand new model assessment and decision-making functionalities such as counterfactual analysis and causal inference. The toolbox is built with two intentions in mind:
– Accelerating the development lifecycle for Machine Learning in a way that implements and applies Responsible AI principles in practice.
– Serving as a collaboration framework for research in the Responsible AI field within Microsoft and beyond.

While different open-source tools have been proposed for assessing fairness, explainability or errors of a machine learning model, these properties are not independent and Machine Learning practitioners may need several of these functionalities to fully identify, diagnose, mitigate issues, and take actions in the real world.

These functionalities interact in non-trivial ways and the best results are obtained through interactively analyzing workflows that integrate them in combination, allowing for flexible deep dives. Therefore, we showcase the new, integrated release of the Responsible AI Toolbox.

Minsoo is a Technical Program Manager in the Responsible AI tooling team at Microsoft focusing on building out offerings for Microsoft’s open-sourced Responsible AI Toolbox and its integration into Azure Machine Learning platform. She has contributed to the UX/UI for tooling such as InterpretML, Interpret-text, DiCE (Diverse Counterfactual Examples), Error Analysis and EconML. She has B.A. in Applied Math and B.F.A. in Painting from Brown University and Rhode Island School of Design (RISD). Coming from an interdisciplinary background with experience in building models and applications, analyzing data, and designing UX, she loves to work in the intersection of AI/ML, design, and social sciences to empower ML practitioners to work ethically and responsibly end-to-end.

Rachel is a Technical Program Manager on Microsoft’s Responsible AI Tooling team in Azure Machine Learning. In that role, she is working to productionalize tools for data scientists that enable them to identify, diagnose, and mitigate model errors and take a pro-active approach to fair and responsible modeling. Prior to joining Microsoft, Rachel attended Texas A&M University and received and B.S. in Computer Science, focusing in Business and Cyber Security. Other passions of hers include using AI and technology to act as a catalyst towards solving humanitarian-centered problems for non-profits around the world.