CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models

Case-Based Reasoning offers a more transparent alternative to black-box AI: Rather than relying on post-hoc explanations that attempt to justify opaque decisions after the fact, Case-Based Reasoning models are "self-explainable," meaning they make decisions by comparing new situations to similar past cases—a process humans can more easily understand and verify.

The explainable AI field lacks standardization and reproducibility: The paper identifies that existing self-explainable AI research suffers from "lack of reproducibility, unfeasible comparison, diverging standards," making it difficult for organizations to reliably compare different explainable AI approaches or reproduce published results.

CaBRNet provides a common framework to advance responsible AI development: By offering an open-source, modular library with standardized tools, CaBRNet addresses fragmentation in the field and enables researchers and practitioners to develop, test, and fairly compare Case-Based Reasoning models using consistent benchmarks and methods.

Open-source infrastructure is essential for trustworthy AI: The creation of shared, accessible tools like CaBRNet supports the broader responsible AI goal of transparency and accountability by allowing independent verification, reducing barriers to adoption, and enabling broader participation in developing explainable AI systems.