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GR5243 Spring 2024 Applied Data Science

Project 4 Machine Learning Fairness Algorithms Evaluation

In this project, working in teams, you will implement, evaluate and compare algorithms for Machine Learning Fairness.

Challenge

Machine Learning Fairness refers to correcting the unfairness for certain groups or individuals in machine learning algorithms' predictions.

For this project, each team is assigned specific algorithms from the Machine Learning Fairness literature. You will study the algorithms carefully and implement them, from scratch. Algorithm assignments will be posted to a piazza post.

For submission, you will submit the GitHub repo of your codes, a testing report (must be a reproducible R/Python notebook) on the algorithms in terms of a side-by-side comparison of their performance and computational efficiency.

You can use Google Colab notebook if you're working in Python. It has the following advantages:

  • Zero configuration required
  • Free access to GPUs
  • Easy sharing

It's not mandatoy for you to use Google Colab notebook if you're writing a Python notebook.

For presentation, each team should briefly explain

  • what each algorithm does;
  • how the evaluation was carried out;
  • and what are the main results.

All developments need to be carried out in group-shared private repo on [https://www.github.com/TZstatsADS/] with clear project management log, taking advantage of GitHub issues.

Each week, we will give a tutorial in class and having live discussion and brainstorm sessions. The instruction team will join team discussions during class and online.

  • week 1 : Introduction and project description.
  • week 2 : Introduction to Machine Learning Fairness; Q&A.
  • week 3 : Discussion of assigned algorithms.

Evaluation criteria

  • (7pts) Readabiity and reproducibility of codes
  • (7pts) Validity of evaluation (well-defined measures of performance; experiment set up)
  • (6pts) Presentation (report, github and in-class presentation)

(More details will be posted as grading rubrics in courseoworks/canvas)

Suggested team workflow

  1. [wk1] Week 1 is the reading and coding week. Read the papers, understand the algorithms assigned to you and start coding up the algorithms; Also load the data and understand its structure.
  2. [wk1] As a team, brainstorm about your evaluation plan.
  3. [wk2] Based on the outcomes from week 1's reading and brainstorm sessions, continue coding and start evaluation.
  4. [wk2] Week 2 is the evaluation week.
  5. [wk2] It is ok to separate into two sub-teams, each one working on one algorithm, as long as the two teams have the same criteria for evaluating the algorithms. The two sub-teams can also serve as each other's validators.
  6. [wk3] By using R/Python Notebook to carry out coding and evaluation, your final report can just be adding explanation and comments to your Notebook.
  7. [wk3] Week 3 is the report writing week. You want to create clear explanation and illustration of the algorithms by using diagrams (can be borrowed from the papers), case examples, summarizing statistcs, and visualizations of performance statistics.

Working together

  • Setup a GitHub project folder from joining the GitHub classroom link with everyone listed as contributors. Everyone clones the project locally via your GitHub desktop and create a local branch.
  • The team can work in subgroups of 2-3, which might meet more frequently than the entire team. However, everyone should check in regularly on group discussion online and changes in the GitHub folder.
  • Learn to work together is an important learning goal of this course.

Resources

Papers

  1. Information Theoretic Measures for Fairness-aware Feature selection

  2. Learning Fair Representations

  3. Fairness Constraints: Mechanisms for Fair Classification

  4. Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

  5. Fairness-aware Classifier with Prejudice Remover Regularizer

  6. Handling Conditional Discrimination

Existing R/Python functions that can be part of your implementation.

Unless otherwise instructed, you can use existing R/Python functions as part of your implementation. But, you'll be expected to write the main algorithm by yourself.