The paper establishes connections between Consistency-Based Diagnosis (CBD) and concepts of Actual Causality and Causal Responsibility from the perspective of Explainable AI (XAI). It highlights that CBD has received limited attention within the XAI community and suggests that linking these areas could positively influence both XAI and Explainable Data Management.
By Leopoldo Bertossi
The paper examines how machine learning (ML) in medicine parallels clinical translation, framing this relationship as a generative analogy. It identifies epistemic and methodological warrants from clinical translation that are often invoked in ML discussions and analyzes how these warrants apply analogically to ML. The authors propose a new form of ML reliabilism, interpreting clinical translation warrants in reliabilist terms and showing how this perspective can inform ML systems distinct from existing reliabilist accounts in philosophy of AI.
By Emanuele Ratti, Lena Zuchowski
arXiv:2606. 00044v1 Announce Type: cross Abstract: The integration of artificial intelligence into clinical medicine creates a fundamental tension between algorithmic probabilistic reasoning and the experiential intuition of expert physicians; applying Lawrence Lessig's \enquote{Code is Law} framework, I argue that the architecture of clinical AI systems already functions as de facto medical regulation, reshaping liability and the standard of care.
By Aizierjiang Aiersilan
arXiv:2608.28820v1 Announce Type: new
Abstract: Computational pathology (CompPath) is transforming medicine by leveraging artificial intelligence (AI) algorithms to support diagnosis, prognosis, and...
By Shubham Innani, Suhang You, Adam Shephard, Bhakti Baheti, Francesco Ciompi, Joe Yeong, Nasir Rajpoot, Michael Feldman, Solene Florence Kammerer-Jacquet, Dimitrios Makris, Geert Litjens, Anne L. Martel, Jana Lipkova, April Khademi, Spyridon Bakas, for the MICCAI SIG-CompPath
arXiv:2608. 02238v1 Announce Type: cross Abstract: Ensuring trust in AI systems is essential for the safe and ethical integration of machine learning systems into high-stakes domains such as digital health.
By Abdullah Mamun, Shovito Barua Soumma, Hassan Ghasemzadeh
arXiv:2608.30393v1 Announce Type: new
Abstract: Biomedical artificial intelligence (AI) systems increasingly extract, organize, and reuse scientific claims from literature, clinical trials, and regul...
By Negin Sadat Babaiha, Stefan Geissler, Marie-Christine Simon, Martin Hofmann-Apitius, Marc Jacobs