A Framework for Fairness: A Systematic Review of Existing Fair AI Solutions

The fairness-practice gap is a critical problem: While researchers have developed numerous tools to detect and combat algorithmic bias, these solutions are rarely being used in real-world applications, indicating a significant disconnect between what fairness experts have created and what practitioners actually implement.

Algorithmic bias is now well-documented across industries: The paper identifies and categorizes specific bias issues that have emerged from machine learning applications, demonstrating that unfairness in AI systems is a widespread, documented problem rather than an isolated concern.

One-size-fits-all solutions won't work: The review reveals important caveats and limitations to existing fairness solutions, suggesting that practitioners need tailored approaches rather than generic tools to address bias in their specific contexts.

Multiple stakeholders must collaborate for progress: Bridging the gap between fairness researchers, ML practitioners, and organizations requires addressing the distinct needs of each group—no single party can solve the fairness implementation problem alone.

Practitioners need better guidance and resources: The paper identifies a taxonomy of needs from ML practitioners themselves, suggesting that fairness experts should focus on creating more practical, accessible solutions rather than purely theoretical frameworks.