Explainable Artificial Intelligence

From Simple Predictors to Complex Generative Models

Spring 2023, Harvard University

Overview:

As machine learning models are increasingly being employed to aid critical decision making in high-stakes domains such as healthcare, finance, and law, it becomes important to ensure that relevant stakeholders are able to understand the behavior of these models. Such an understanding helps determine if, when, and how much to rely on the outputs generated by these models. This graduate level course aims to familiarize students with the recent advances in the emerging field of eXplainable Artificial Intelligence (XAI). In this course, we will review seminal position papers in the field, understand the notion of explainability from the perspective of different end users (e.g., doctors, ML researchers/engineers), discuss in detail different classes of interpretable models and post hoc explanations (e.g., rule-based and prototype-based models, feature attributions, counterfactual explanations, mechanistic interpretability), and explore the connections between explainability and fairness, robustness, and privacy. This course will also cover latest research on understanding large language models (e.g., GPT-3) and diffusion models (e.g., DALLE 2), and highlight the unique opportunities and challenges that arise when interpreting the behavior of such large generative models.

Prerequisites:

Students are expected to be fluent in basic linear algebra, probability, algorithms, and machine learning. Students are also expected to have programming and software engineering skills to work with data sets using Python, numpy, and sklearn.

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