FLAI doesn't play games with fairness. While others slap band-aids on symptoms, we surgically remove bias at its root using breakthrough causal reasoning. Zero bias. Zero compromise on performance.
Spoiler alert: Deleting sensitive attributes and hoping for the best doesn't work. Here's why everyone else is doing it wrong.
Remove gender from the data, ignore that salary correlates with it. Boom! Still biased. Surprised? We weren't.
Equal outcomes or equal opportunities? Pick one metric, miss half the picture. It's like measuring success with a coin flip.
Sacrifice accuracy for "fairness" that doesn't actually work. It's lose-lose, and everyone pretends it's fine.
See exactly how discrimination flows through your system. No more guessing. We literally map the bias pathways.
Finally, a metric that gets it. Equality vs. equity. EQA vs. EQI. Know exactly what type of bias you're dealing with.
Remove bias without killing performance. 100% fairness, 99.2% of original accuracy. That's not a typo.
Three game-changing innovations that make fairness actually work.
The first fairness metric that actually makes sense
We follow the bias breadcrumbs
Surgical bias removal that doesn't break your model
"Our loan algorithm was secretly sexist"
Gender bias eliminated overnight. 99.2% accuracy preserved. 85% fewer discrimination complaints. Legal team finally stopped panicking.
"COMPAS was racially biased. We fixed it."
The infamous court algorithm that everyone knew was broken? We didn't just complain about it—we actually fixed it. Equal justice, finally.
"Our hiring AI had trust issues"
Systematic bias against women and minorities in resume screening. FLAI generated clean training data. Fair hiring without lowering standards.
Three peer-reviewed papers that made the AI establishment very uncomfortable. Turns out we were right.
"Quantifying algorithmic discrimination: A two-dimensional approach to fairness in artificial intelligence"
Introduces the breakthrough equity vs equality distinction that outperforms traditional fairness measures on benchmark datasets.
Read Paper"Mitigating bias in artificial intelligence: Fair data generation via causal models"
Demonstrates how causal Bayesian networks can generate synthetic datasets that preserve utility while eliminating bias.
Read Paper"A Review of Bias and Fairness in Artificial Intelligence"
Complete taxonomy of AI bias types and mitigation strategies across the entire ML pipeline — from data collection to deployment.
Read Paper3.0.4 (Latest)
Python 3.9+
Apache-2.0