Welcome to FairSeldonian Project!

With the growing usage of machine learning and artificial intelligence in real lives, the need for incorporating the mitigation of unethical and unfair elements in machine learning models is rising at an alarming rate. This requires us to develop a tool to evaluate and mitigate unfairness in these models that would help data scientists in dealing with such problems in the models they develop.

Fair-Seldonian leverages Seldonian algorithm to tackles this problem where the responsibility of regulating the undesirable behavior of machine learning algorithms and making them `fair’ is transferred from user to designer of the algorithm (i.e. at the creation step itself by ML researcher). This is implemented for fairness in machine learning for any generic constraint. In addition, some extensions for optimizing the bounds and making it more efficient are also present.

More details of Seldonian is present here.

Contents:

• Introduction
• Getting Started
  • Pre-requisites
  • Quick start
  • Configuration
  • Collaboration
• Variants
  • Basic Seldonian
  • Improvements to confidence interval
  • Improvement in bound propagation around constant values
  • Improvement in bound propagation from union bound
  • Combining all of the above optimizations
  • Optimization with Lagrangian/KKT
• Code Documentation
  • QSA (Quasi-Seldonian Algorithm)
  • ML Model
  • Equation parser
  • Inequality
  • Viewing results