In this talk, we will address several areas of recent work centered around the themes of transparency and fairness in machine learning as well as highlight the challenges in this area. We will discuss recent results involving linear algebraic tools for learning, such as methods in non-negative matrix factorization that include tailored approaches for fairness. We will showcase our derived theoretical guarantees as well as practical applications of those approaches. These methods allow for natural transparency and human interpretability while still offering strong performance. Then, we will discuss new challenges and directions in fairness including an example in large-scale optimization that allows for population subgroups to have better predictors than when treated within the population as a whole. Throughout the talk, we will include example applications from collaborations with community partners, using machine learning to help organizations with fairness and justice goals.
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