In the last years, the development of Artificial Intelligence (AI) has been remarkable. Businesses have progressively start working with it in numerous tasks, typically with the usage of AI models, a lot of them developed with Machine Learning.
Machine learning is being used by thousands of companies to improve their decision-making
procedures and to expand their businesses. These enterprises are currently developing and using AI models thanks to complex algorithms.
Nowadays, the importance of Artificial intelligence (AI) models in data-driven companies is increasing exponentially. These models are constantly learning and evolving automatically thanks to machine learning. Even though they are very accurate, in most of the cases we will not be able to understand the reasoning behind the model’s predictions. In consequence, one of the biggest challenges that these companies are facing is understanding how their AI models make predictions.
Artificial Intelligence (AI) is increasing its potential to help companies in their daily processes. However, algorithms powered by AI are usually difficult to understand as they lack transparency and explicability. Hoping for a more transparent and trustworthy AI, a new field has appeared: Explainable AI (XAI).
