Probability for Computer Scientists

Learning goal: Understanding formal definitions of algorithmic fairness; exploring the limits of formal fairness and contrasting it with other normative concepts such as representation and justice.

Description: This assignment builds on introductory knowledge of machine learning techniques, namely the naïve Bayes algorithm and logistic regression, to introduce concepts and definitions of algorithmic fairness. Students analyze sources of bias in algorithmic systems, then learn formal definitions of algorithmic fairness such as independence, separation, and fairness through awareness or unawareness. They are also introduced to notions of fairness that complicate the formal paradigms, including intersectionality and subgroup analysis, representation, and justice beyond distribution.

Contains: Assignment Handout, Ethics slides, Explainer

Learning goals: Applying probabilistic reasoning to scenarios involving climate change; learning frameworks for decision-making under uncertainty; understanding and comparing principles for allocation of responsibility for past harms; applying frameworks and principles in the context of climate risks and the apportionment of responsibility for climate change.

Description: This assignment uses the tools of probability theory to introduce students to risk weighted expected utility models of decision making. The risk weighted expected utility framework is then used to understand decision-making under uncertainty in the context of climate change. Which of the IPCC’s forecasts should we use? Do we owe it to future people to adopt a conservative risk profile when making decisions on their behalf? The assignment also introduces normative principles for allocating responsibility for addressing climate change. Students apply these formal tools and frameworks to understanding the ethical dimensions of climate change.

Contains: Assignment Handout, Ethics slides, Explainer

Learning goal: Understanding how probability can be used to assess the fairness of AI algorithms.

Description: In this module, students are introduced to philosophical and statistical conceptions of fairness including parity and calibration, and apply these ideas to algorithms that have disparate impacts on different protected groups, including AI algorithms that predict criminal recidivism. Along the way, students consider the ethical upshot of AI and probability in other domains, including climate change and election forecasting.

Contains: Lecture Slides, Section Handout