Algorithmic Transparency via Quantitative Input Influence - Theory and Experiments with Learning Systems

This course aims to teach learners about Algorithmic Transparency via Quantitative Input Influence. The learning outcomes include understanding the importance of transparency in decision-making systems that employ machine learning, learning about Quantitative Input Influence (QII) measures to measure the influence of inputs on system outputs, and exploring the transparency-privacy tradeoff. The course covers topics such as learning systems, input correlation, marginal influence, and game theory. The teaching method involves theoretical explanations, experiments, and empirical validations with machine learning algorithms. This course is intended for individuals interested in understanding and improving the transparency of algorithmic decision-making systems.