The inability for humans to see inside black boxes can result in AI adoption (and even its further development) being hindered, which is why growing levels of autonomy, complexity, and ambiguity in AI methods continues to increase the need for interpretability, transparency, understandability, and ...
Fujitsu and Tokai National Higher Education and Research System leverage explainable AI to enhance space weather prediction in collaboration with JAXA
Explainability is currently a paramount characteristic of AI decision-making in ART clinics and the introduction of data-driven insights that align with clinical reasoning promotes the possible wider adoption of clinical decision-support systems in the ART domain2,27,28. Secondly, the use of data ...
When artificial intelligence (AI) is used to make high-stakes decisions, some worry that this will create a morally troubling responsibility gap—that
As these systems get better atperforming tasks, they could begin to diverge from ourvalues. Finally, because of these technologies' potential tobecome more powerful as they increase in capability, weneed to prepare for dangerous outcomes. As AI capabilitiesadvance, they will become better at ...
We welcome submissions of novel scientific research and position papers presenting novel ideas, perspectives, or challenges in explainable AI for Time Series and Data Streams as regular papers (max. 8-16 pages) or extended abstracts (up to 2-4 pages). Each paper will be double-blind peer-revi...
Artificial intelligence (AI) encompasses the application of computer systems to undertake tasks that might challenge human capabilities, frequently in manners that are elusive to specify. Machine Learning (ML) and Deep Learning (DL), branches of AI, can be utilized for the timely diagnosis of ...
With its potential to contribute to the ethical governance of AI, eXplainable AI (XAI) research frequently asserts its relevance to ethical considerations. Yet, the substantiation of these claims with rigorous ethical analysis and reflection remains largely unexamined. This contribution endeavors to scruti...
As research on XAI matures, the community is starting to critically reflect on the path built so far and on the rationales that are given in the literature to motivate XAI. An early example of such a critical reflection, published with the title “Explainable AI: Beware of Inmates Running th...
Model risk managers at several large banks5—mirroring the AI research community at large6—are reportedly divided on this matter,7 and there appears to be no clear consensus yet. Explainable AI to the rescue The emerging field of explainable AI (or XAI) can help banks navigate issues of tra...