How glass box models can provide X-AI without compromising results
Our Machine Learning Engineer, Bas Roelenga, published an article on Medium about black and glass box models. Explainable AI has become more and more important over the years in our data driven world. Being able to explain your predictions is crucial when working with sensitive data. In this blogpost Bas examines and analyses how we can achieve state of the art performance while also maintaining explainability. This article also demonstrates how explainable AI is being used at Slimmer AI.
"Artificial Intelligence plays a big role in our daily lives. AI is being used everywhere, from our search queries on Google to self-driving vehicles such as Tesla. With the use of deep learning, the models used in these applications have become even more complex. In fact, they are so complex that in many cases we have no idea how these AI models reach their decisions."- To read more, please go to Think outside the ‘black’ box.
"Hi, I am an astronomer turned data scientist from Groningen. Using my strong background in data analyses I solve real world problems using (explainable) AI in a responsible way. My specialities are analyzing large datasets using state of the art machine learning techniques. Currently my focuses are on applying state of the art NLP techniques and explainable AI."
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