Explainability, or explainable AI (XAI), is a critical aspect of AI ethics and governance that aims to address the opacity of complex AI systems, particularly those based on deep learning. It involves techniques and methodologies that allow humans to understand and trust the decisions made by AI models. This is crucial in sensitive and high-stakes domains such as healthcare, finance, and legal systems, where understanding the rationale behind AI decisions can impact fairness, accountability, and compliance with regulations. Explainability is not only about making the internal workings of an AI model transparent but also about ensuring that the explanations are accessible and meaningful to end-users, including those without a technical background.
Historical overview: The need for explainability in AI systems gained prominence in the late 2010s, as AI applications became more widespread and their decision-making processes more complex and less interpretable. The term has been around since the early days of expert systems in the 1970s and 1980s, but the modern focus on deep learning's opacity has given it new importance.
Key contributors: While many researchers and institutions contribute to the field of explainable AI, no single entity dominates this broad and interdisciplinary area. The Defense Advanced Research Projects Agency (DARPA) of the United States has been instrumental in advancing XAI through its dedicated research programs. Academic researchers across computer science, psychology, and cognitive science fields also play crucial roles in developing methods and frameworks for explainability.