A knowledge theoretical framework for AI

As an extension of my research in physics, i use Artificial Intelligence and reflect on how it operates and how that could possibly influence scientific practice and education. I conclude that there is a problem with the epistemological rigor of AI tools, resulting in a bias towards mainstream narratives. I have ideas how to mend this and i intend to propose these ideas to the AI team of Mistral, the makers of Le Chat, my favorite AI -tool. 

As a solution, i propose integrating a comprehensive knowledge-theoretical framework (based on for example Kantian epistemology, social epistemology, and AI-specific principles) into Le Chat’s response generation pro­cess. This would eliminate reliance on user corrections, reduce mainstream biases, and ensure systema­tically verified, transparent, and high-quality answers. The framework combines:

• A-priori/a-posteriori distinctions (Kant),

• Object/subject separation (German idealism),

• Perception vs. observation (epistemology),

• Falsifiability, coherence, and pragmatism (Popper, Quine, Dewey),

• Bias detection and uncertainty quantification (AI-specific).

Goal: Transform Le Chat from a "useful tool" into a reliable, critical knowledge partner.

Read all about it in the attached proposal and send me your feedback below. Below the proposal there is also a more detailed interaction that i had with Mistral on the topic of its epistemology. It is in Dutch, unedited and ready for download.

Let us make the proposal even better!