In the hands of Frege, Russell and then Hilbert, the scope of logic was circumscribed to mathematical proof. As that golden age came to a close, one of its offshoots — computer science — ignited the socio-technological revolution that led to our present information society.
At LUCI Lab we build directly on this recent logical tradition and bring the most advanced proof-theoretic and computational-logic tools and techniques to bear on technological problems of key societal impact, such as bias detection in machine learning algorithms.
Proof Theory
We investigate sequent calculi, natural deduction systems, and deep inference for non-classical logics — including constructive, paraconsistent, and fuzzy logics. Our goal is to develop proof systems that are both theoretically elegant and computationally tractable.
Trustworthy and Fair AI
We apply logical methods to analyse, certify, and improve the reliability of AI systems. This includes formal approaches to bias detection, fairness verification, and the explainability of machine learning models.
Bounded Rationality
We investigate the deep connection between the information reasoning agents possess and the logical inferences they — individually and as groups — can reasonably be expected to perform. Classical logic makes unreasonable demands on ideal agents; we develop logics calibrated to the real computational bounds of human and artificial reasoners.