Borealis AI Research Introduces fAux: A New Approach To Test Individual Fairness via Gradient Alignment

Machine learning models are trained on massive datasets with hundreds of thousands, if not billions, of parameters. However, how these models translate the input parameters into results is unknown. Having said that, the decision-making behavior of the model is difficult to comprehend. Furthermore, models are frequently skewed towards specific parameters due to faulty assumptions made during the machine learning process, which are difficult to detect.

Researchers from [Borealis AI](https://www.borealisai.com/en/) introduced [fAux](https://www.borealisai.com/en/blog/faux-testing-individual-fairness-gradient-alignment/), a new approach to testing fairness. 

They state that …

ai ai research alignment artificial borealis fairness gradient research test