CS-ECE Colloquium: Human-Centric Machine Learning: Enabling Machine Learning for High-Stakes Decision-Making
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Wednesday, March 7, 2018 - 12:00pm to 1:00pm
Hima Lakkaraju, Ph.D. candidate in Computer Science at Stanford University
In this talk, I will describe novel computational frameworks which address the aforementioned challenges, thus, paving the way for large-scale deployment of machine learning models to address problems of significant societal impact. First, I will discuss how to build interpretable predictive models and explanations of complex black box models which can be readily understood and consequently trusted by human decision-makers. I will then outline efficient and provably near-optimal approximation algorithms to solve these problems. Next, I will present a novel evaluation framework which allows us to reliably compare the quality of decisions made by human decision-makers and machine learning models amidst challenges such as missing counterfactuals and presence of unmeasured confounders (unobservables). Lastly, I will provide a brief overview of my research on diagnosing and characterizing biases (systematic errors) in human decisions and predictions of machine learning models. I will conclude the talk by sketching future directions which enable effective and efficient collaboration between humans and machine learning models to address problems of societal impact.