Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions

Explainability requires interdisciplinary collaboration: The paper identifies 27 open problems across nine categories, emphasizing that solving XAI challenges requires experts from diverse fields working together rather than AI researchers alone, reflecting the complexity of making AI systems truly understandable to different stakeholders.

Current XAI methods have significant limitations: While XAI has emerged as a field with "practical and ethical benefits," the paper acknowledges ongoing challenges in existing approaches, suggesting that current explanation techniques may not adequately address real-world needs across different domains and user groups.

Black box AI systems demand urgent attention: The manifesto emphasizes that as opaque AI systems "continue to flourish in diverse real-world applications," understanding these models has become "paramount," indicating that the widespread deployment of unexplainable AI poses risks that responsible AI development must address.

One-size-fits-all explanations are insufficient: By proposing research directions tailored to nine distinct problem categories, the paper implies that different applications and stakeholders require different types of explanations—a single explanation method cannot serve all purposes in responsible AI deployment.

XAI is foundational to ethical AI: The paper frames explainability as having "ethical benefits," suggesting that transparency and understandability are not optional features but essential components of developing AI systems that can be responsibly deployed and governed in society.