How do we balance the potential benefits of deep learning with the need for explainability? Getty. People distrust artificial intelligence and in some ways this makes sense.

Deep learning neural networks, exemplified by models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs), have achieved ...

If, say, a machine learning (ML) algorithm also made business decisions, these decisions need to be annotated and presented effectively. Explainability helps business leaders understand why a ...

SageMaker Autopilot now generates a model explainability report via SageMaker Clarify, the Amazon tool used to detect algorithmic bias while increasing the transparency of machine learning models. The ...

Building explainability into the components of machine-learning models. ScienceDaily . Retrieved June 2, 2025 from www.sciencedaily.com / releases / 2022 / 06 / 220630134833.htm ...

Deep-learning techniques can be used to handle more complex problems thanks to their ability to ingest and understand significant amounts of data. However, that additional firepower comes at a cost.

HEX: Human-in-the-loop explainability via deep reinforcement learning. In a paper published in the journal Decision Support Systems, Michael T. Lash, an assistant professor in the Analytics ...

With deep learning, you start with sample data, deploy the model, and then expose it to the real world. But models that work well on training data often perform poorly on real data.

Please use one of the following formats to cite this article in your essay, paper or report: APA. Cuffari, Benedette. (2025, April 07). Using Deep Learning for Brain Imaging Data Analysis.