ML-fairness-gym provides a framework for simulating the long-term impacts of machine learning-based decision systems in social environments. It targets researchers and practitioners in fair machine learning, enabling the exploration of dynamic fairness properties that may counteract static fairness definitions over time.

How It Works

The gym implements a generalized framework for studying long-term fairness effects by creating simulation scenarios where a learning agent interacts with an environment over time. It leverages the environment API from OpenAI Gym, allowing for the reproduction and generalization of environments discussed in existing research papers. This approach facilitates the investigation of how ML systems designed for static fairness might behave differently in dynamic, real-world settings.

Quick Start & Requirements

• Installation: pip install ml-fairness-gym
• Prerequisites: OpenAI Gym (version 0.19.0 specified in v0.1.1).
• Resources: No specific hardware or dataset requirements are detailed for basic setup.
• Documentation: https://github.com/google/ml-fairness-gym

Highlighted Details

• Focuses on long-term, dynamic fairness impacts, a critical but less-explored area in ML fairness.
• Generalizes and reproduces environments from existing fair ML research papers.
• Adheres to the OpenAI Gym environment API for compatibility and ease of use.

Maintenance & Community

• Initial release (v0.1.0) with updates to use gym 0.19.0 (v0.1.1).
• Discussion group: ml-fairness-gym-discuss@google.com.
• Not an officially supported Google product.

Licensing & Compatibility

• License details are not explicitly stated in the provided README snippet. Compatibility for commercial use or closed-source linking is therefore undetermined.

Limitations & Caveats

The project is in its early stages (v0.1.1), with the initial release focusing on reproducing existing research environments. The scope and maturity of the framework for novel simulations may be limited.