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CS 335: Fair, Accountable, and Transparent (FAT) Deep Learning

Spring 2020 Project Guidelines

Your course project is an excellent opportunity to explore techniques learned from class and apply them to practice. Here is a list of project ideas along with datasets to get you started. You are encouraged to discover FAT related questions in your own problems.

Project Policy

  • Projects can be done either individually or by two students. For group projects, we ask that you divide work equally.

  • You are expected to implement a FAT related algorithm onto one and more datasets of your choice. Innovations on algorithms are strongly encouraged.

  • No double dipping. You are expected to develop new work and can not reuse projects from another course or another research project. However, we do encourage you to take problems or datasets from your own research or a problem that you are familiar with.

  • Your project will contribute to 75% of your final grade, and will consist of deliverables from the following four stages. You will need to follow the ACM FAT template when writing your final report.

    • Project proposal, 1.5 page (10%)

    • Midway project report, 5 pages (15%)

    • Final presentation (30%)

    • Final report 10 pages, excluding references (45%)