Explainable artificial intelligence (XAI) is a field of computer science that focuses on making artificial intelligence (AI) models and their decisions more understandable to humans.
There are technical, legal, pragmatic, and commercial reasons that require AI's internal processes to be more accessible and transparent so that people can understand how they work and why they make the decisions they do. The goal is to explain the model's logic to anyone who has been affected by AI decisions.
As research into explainable AI continues to advance, we can expect to see further development of tools and techniques that make AI more transparent, accountable, and trustworthy.
In this video, Andrés Páez (Associate Professor, Department of Philosophy and Center for Research and Training in Artificial Intelligence (CinfonIA) of the University of the Andes) takes a moment to describe the problems and perspectives.
Quo Vadis IA is a research line of the Faculty of Engineering of the Universidad AustralThe goal of this interdisciplinary team is to contribute to the understanding of the state of the art of Artificial Intelligence by providing information on future scenarios so that public and private research organizations, universities, and companies can define long-term policies and strategies.
