Unveiling ML-fairness-gym, a revolutionary tool that delves into the long-term implications of Machine Learning systems. Discover how this platform aids in understanding and mitigating biases, ensuring fairness, and promoting transparency in AI-driven solutions.
Fairness is not static: Deeper Understanding of Long Term Fairness via Simulation Studies
The fairness of algorithmic decisions ideally would be analyzed with greater consideration for the environmental and temporal context than error metric-based techniques allow
- ML-fairness-gym is a set of components for building simple simulations that explore potential long-run impacts of deploying machine learning-based decision systems in social environments
Conclusion and Future Work
The ML-fairness-gym framework is flexible enough to simulate and explore problems where “fairness” is under-explored.
- Potential uses: help other researchers and machine learning developers better understand the effects that machine learning algorithms have on our society, and inform the development of more responsible and fair machine learning systems
Deficiencies in Static Dataset Analysis
If test sets are generated from existing systems, they may be incomplete or reflect the biases inherent to those systems
- In the lending example, a test set could be incomplete because it may only have information on whether an applicant who has been given a loan has defaulted or repaid
ML-fairness-gym as a Simulation Tool for Long-Term Analysis
simulates sequential decision making using Open AI’s Gym framework
- Agents interact with simulated environments in a loop
- Environment reveals observation that the agent uses to inform its subsequent actions
- Simulates outcomes so that the long-term effects of the bank’s policies on fairness to the applicant population can be assessed
- Used to help ML practitioners bring simulation-based analysis to their ML systems, an approach that has proven effective in many fields where closed form analysis is difficult
