Building Trustworthy AI Beyond Prediction

Over the past months, I've been developing CREDERE, an AI engineering project exploring a question that I believe is becoming increasingly important:
What happens after a machine learning model makes a prediction?
Most discussions around AI still focus on model accuracy.
In regulated domains like credit scoring, however, accuracy is only one part of the problem.

A production system also needs to answer questions such as:
Is the decision legally compliant?
Can it be explained without hallucinations?
Can every explanation be verified?
Can a human intervene when necessary?
Can every decision be audited afterwards?

Those questions cannot be solved by a better classifier alone.
They require software architecture.

Instead of building another predictive model, I designed a neuro-symbolic architecture where statistical learning is complemented by deterministic reasoning.

The public repository currently focuses on two production-oriented modules:
Compliance Engine — deterministic regulatory rules capable of overriding model predictions when legal constraints are violated.

Explanation Engine — customer-facing explanations generated without factual hallucinations through structured templates and independent fact verification.
Today I'm also publishing the CASE_STUDY.md, which documents the engineering journey behind the project.

Rather than describing only what was built, it explains:

the original problem;
the architectural decisions;
alternative approaches that were considered;
engineering trade-offs;
validation strategy;
lessons learned;
limitations;
future directions.

I believe engineering documentation is often overlooked, yet it is one of the clearest indicators of how a system was actually designed and why certain decisions were made.

If you're interested in:

Trustworthy AI

Explainable AI (XAI)

Neuro-Symbolic AI

AI Governance

Software Architecture

AI Engineering

Credit Scoring

RegTech

The project can be viewed on my GitHub: https://github.com/rafaelcontente/CREDERE

I'd genuinely appreciate your feedback.

I hope the case study proves useful not only for understanding CREDERE but also as a practical example of documenting AI systems beyond predictive performance.

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