Our mission

Empowering discovery. Respecting human judgement.

We develop AI methods that make models transparent and trustworthy, supporting human decision-making, scientific discovery and alignment with human values. We believe AI should empower humans in the pursuit of knowledge, but never replace their judgment.

Our research:

• [August 2025 –   ] – We continue our research on the Excitation Pullbacks, working to improve the method and enhance the network training – with the aim to boost both generalization and interpretability. We’re open to collaboration and support – join us in shaping the future of human-aligned AI!
• [July 2025, paper, code, demo] – We propose Excitation Pullbacks, a novel way to explain AI models. Our method shows what the model really looks at when making a prediction. It amplifies the most important features for the chosen label, and these features turn out to align surprisingly well with human perception. This makes AI decisions easier to understand and more transparent.
• [March 2024, paper, code] – We introduce semantic features as a candidate building block for transparent neural networks and build fully explainable and adversarially robust PoC neural network for MNIST.