Finalists for a tenure-track faculty position in Operations Research will present a research talk.<\/p>\n
Nathan Justin \u201919 will discuss \u201cResponsible Machine Learning via Mixed-Integer Optimization.\u201d<\/p>\n
Abstract: In the last few decades, machine learning (ML) has achieved significant success across domains ranging from healthcare and sustainability to the social sciences, criminal justice and finance. But its deployment in increasingly sophisticated and sensitive areas affecting individuals, the groups they belong to and society as a whole raises critical concerns around fairness, transparency and robustness, among others. As the complexity and scale of ML systems and of the settings in which they are deployed grow, so does the need for responsible ML methods that address these challenges while providing reliable performance in practice. Mixed-integer optimization (MIO) offers a powerful framework for embedding responsible ML considerations directly into the learning process while maintaining performance. This talk outlines some of the core principles of responsible ML, their importance in applications and the practical utility of MIO for building ML models that align with these principles. To illustrate these ideas, I will present recent research on how mixed-integer optimization can be used to construct decision trees designed for responsible deployment in high-stakes settings, motivating a variety of future research directions.<\/p>\n","protected":false},"excerpt":{"rendered":"
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