Quantinuum scientists propose a new framework based on category theory to tackle the issue of AI interpretability, offering transparent and reliable insights into decision-making processes. Led by Ilyas Khan, the team aims to make AI systems interpretable by design, reducing risks in high-stakes sectors.
Artificial Intelligence (AI) has become widely used across various sectors, but its lack of interpretability presents significant challenges, especially in areas requiring high accountability like finance, healthcare, and legal fields. Research from Quantinuum scientists proposes a novel approach to AI interpretability by leveraging category theory.
A team led by Ilyas Khan, Quantinuum’s Founder and Chief Product Officer, has highlighted the opaque nature of AI’s decision-making processes, which pose risks in high-stakes domains. Explaining complex AI models is essential, yet existing post-hoc methods often deliver unreliable insights.
Quantinuum’s new framework, detailed in a paper on the pre-print server arXiv and a company blog post, offers a rigorous approach using category theory. This mathematical framework provides a structured method for defining and analyzing AI models. The research aims to make AI systems interpretable by design, rather than relying on costly and partial explanations after deployment.
Their approach uses compositional models, built with explicit structures to enhance transparency. This innovative method is not just applicable to classical models but extends to quantum models as well. Co-author Sean Tull notes the potential for these models to eliminate the need for explainability methods, being inherently self-explanatory.
By applying category theory, the researchers aim to address issues like AI’s “hallucinations” by enhancing the understanding and control of decision-making processes. This offers the promise of more reliable and accountable AI systems in the future.
