Kirill Petrov

Submitted: 11 January 2026 You are already at the latest version This study develops a fairness-calibrated credit-scoring method that combines counterfactual perturbation with group-wise regularization. Using 3.1 million credit files with demographic annotations, the model evaluates whether score outputs remain stable when protected attributes are perturbed in a causal graph. A fairness-adjusted gradient-boosting model is then trained with penalties on group-level prediction disparities.

Hi Product Hunt 👋 I'm Kirill, co-creator of Tila, infinite AI canvas. Problem 👀 For decades, we've been working with content in fragmented ways – Word for text, Excel for data, Photoshop for images, and so on. But in the age of AI, this approach feels increasingly outdated. I realized that content is no longer static – it's fluid and transformable. Text becomes images, images become videos, and data becomes visualizations. Solution 🤸‍♂️ That's why we built Tila.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.