A former data scientist at the Federal Reserve Bank of Chicago has launched a new project aimed at changing how credit scoring is done in a world with artificial intelligence and machine learning.

Chris Lam is the founder and CEO of Epistamai, an AI startup doing research on algorithmic bias in credit underwriting. Lam also went on social media this week to highlight another initiative he’s involved with dubbed Open Credit Scoring.

Here’s what the project is about, according to its website:

“Financial services is entering a new era of AI-driven decision making. From machine learning models to generative and agentic AI systems, lenders are rapidly adopting increasingly sophisticated technologies to automate underwriting, improve operations, and expand access to credit.

“Open Credit Scoring seeks to establish the scientific foundations for trustworthy AI-enabled credit decisions. Rather than treating fairness, validity, transparency, and governance as isolated challenges, we approach them as properties of a larger complex system that includes people, institutions, policies, and feedback loops.

“To understand and improve these systems, we use systems thinking to integrate machine learning, causal inference, and system dynamics into a unified framework for studying AI-enabled credit decisions. Together, these approaches help explain not only how models make predictions, but how AI systems shape — and are shaped by — the social, economic, and legal systems in which they operate.”

Lam also chairs the IEEE Standard for Fair Decision Making Through Causal Analysis. IEEE claims to be the world’s largest technical professional organization dedicated to advancing technology for the benefit of humanity.

And its companion group is the IEEE Standards Association (IEEE SA), which is a collaborative organization where innovators raise the world’s standards for technology. IEEE SA provides a globally open, consensus-building environment and platform that empowers people to work together in the development of leading-edge, market-relevant technology standards, and industry solutions shaping a better, safer and sustainable world.

According to the project website, the IEEE Standard for Fair Decision Making Through Causal Analysis “describes how to perform causal fairness analysis to make fairer decisions in various high-stakes applications (e. g. credit, employment, education) that are more likely to be compliant with a country’s antidiscrimination laws and regulations. It provides a standardized fairness model that encodes knowledge and assumptions about how to map the causal relationships between different variables such as a protected class (e. g. race, gender) and an outcome.

“The document provides the reader with a standardized language for directly translating concepts among the law, causal inference, and supervised machine learning. The standard provides criteria for selecting which variables to include in a machine learning model, how to train and deploy the model to make fairer predictions and decisions, as well as how to evaluate the model to determine the likelihood for illegal discrimination.”