Ethics of Artificial Intelligence

Machine learning has become an indispensable tool for creating intelligent applications, accelerating scientific discoveries, and making better data-driven decisions. Yet, the automation and scaling of such tasks can have troubling negative societal impacts. Through practical case studies, you will identify issues of fairness, justice and truth in AI applications. You will then apply recent techniques to detect and mitigate such algorithmic biases, along with methods to provide more transparency and explainability to state-of-the-art ML models. Finally, you will derive fundamental formal results on the limits of such techniques, along with tradeoffs that must be made for their practical application.

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We require in-person lecture attendance. Specifically, the three socio-technical analyses dates will include in-class projects. More broadly, class participation and in-class discussions will count toward the final grade.

Students are free to form study groups and may discuss homeworks, projects and assignments in groups. However, for homeworks, each student must write down the solutions and code from scratch independently, without referring to any written notes from the joint session. In other words, each student must understand the solution well enough in order to reconstruct it by themselves. It is an honor code violation to copy, refer to, or look at written or code solutions from a previous year or any other source, including but not limited to: official solutions from a previous year, solutions posted online, and solutions you or someone else may have written up in a previous year, or from any other source. Furthermore, it is an honor code violation to post your assignment solutions online, such as on a public git repo. You must explicitly cite any external content, research, code or other resources you use for your homeworks, analyses or projects. The Stanford Honor Code can be found here. The Stanford Honor Code pertaining to CS courses can be found here.

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