What is ExplainedAI?
ExplainedAI is a Research-to-Business project at Tampere University, funded by Business Finland, where we are preparing the commercialization of new interpretable machine learning solutions.
Last updated: May 9, 2026
What problem does ExplainedAI solve?
Many machine learning systems can make useful predictions, but they often fail to explain themselves in a way that is compact, reliable, and usable by people who need to make decisions. In high-stakes or regulated domains, a model output is rarely enough. Users need to understand what drove the prediction, what alternatives were possible, and when the explanation should not be trusted.
How does ExplainedAI approach interpretable machine learning?
The project builds on research connecting logic, machine learning, and explainability. The central idea is to make explanations more structured: not just feature scores or post-hoc visualizations, but concise descriptions that can be inspected, compared, and reasoned about.
What is ExplainedAI preparing?
- Interpretable methods for tabular data and classification tasks.
- Tools for producing short, human-readable explanations of model behavior.
- Commercial use cases where transparency, auditability, and trust are part of the product requirement.
What is my role in ExplainedAI?
I work on the business-development side of the project: turning the research into a clearer product direction, identifying useful applications, and communicating the technical value to people outside the original research context.
Frequently asked questions about ExplainedAI
What is ExplainedAI?
ExplainedAI is a Tampere University Research-to-Business project, funded by Business Finland, that prepares new interpretable machine learning solutions for commercialization.
Why does interpretability matter in machine learning?
Interpretability matters because decision makers often need to understand why a model produced an output before they can trust, audit, or act on it.
How can I use it?
ExplainedAI is looking for applications where transparent predictions, auditability, and compact explanations are important parts of the product or decision process.