Explainable AI (XAI) seeks to address one of the fundamental challenges in the deployment of AI systems: the black box problem, where the decision-making process of AI models, especially complex ones like deep neural networks, is opaque and not easily understood by humans. XAI involves techniques and methodologies that aim to make these processes transparent, allowing users to comprehend and trust the AI's decisions. This transparency is crucial not only for validation and debugging purposes but also for regulatory compliance, ethical considerations, and facilitating more effective human-AI collaboration. By providing explanations of AI decisions that are comprehensible to humans, XAI enhances accountability, fairness, and transparency in AI applications across various sectors including healthcare, finance, and legal systems.
The concept of explainability in AI is not new and has been discussed since the early days of AI research. However, the term "Explainable AI" and its focused emphasis gained prominence in the late 2010s, especially as AI systems became more complex and their applications more widespread. The growing concern over the accountability and ethics of AI decisions has propelled XAI to the forefront of AI governance and ethics discussions.
Identifying key contributors to XAI is challenging due to its interdisciplinary nature and the fact that it draws on decades of research in fields such as artificial intelligence, cognitive science, and human-computer interaction. However, organizations like DARPA (the Defense Advanced Research Projects Agency) have played significant roles in funding and promoting research in XAI, alongside numerous academic and industrial researchers worldwide who have contributed to the development of methodologies and frameworks for explainability.