In recent years, artificial intelligence (AI) algorithms have vastly improved and are now used much more than just in the IT industry. Today they are to be found in areas including finance, medicine, art and renewable energy.
Specifically in computer vision (CV), major decisions are constantly delegated to these algorithms (medical diagnostics, autonomous cars, etc.), so it is crucial that these decisions are fair and explainable. In this context, we have the moral responsibility as a society to shift away from the concept of the “black box” in which we cannot explain the relationship between inputs and their outputs and move towards a point where any decision made by an algorithm can be interpreted.
In this Masterclass we will introduce the need for and the concept of explainability in AI to address the specific existing techniques for explainability in computer vision (focusing on the image classification task) and conclude by setting out use cases in which the interpretation of computer vision models is crucial.
Addressed to:
- People interested in artificial intelligence and computer vision
- Machine learning developers
