Rutgers University 3 credits Computer Science
This course is an introduction to issues related to fairness (i.e., biased model/data) and explainability (i.e., black-box nature of model) with Artificial Intelligence-based automated decisions. The course will explore and analyze different local, global, model-specific, and model-agnostic explainability methods of different notions (e.g., pre-training, post-training) of explainability, and also examine different definitions and performance metrics of fairness in automated decisions, and interpret and apply different bias detection and mitigation techniques and tools. Different open-source tools for explainability and fairness will be explored to solve real-world problems.
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