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Marvin Minsky et al. raised the issue that AI can function as a form of surveillance, with the biases inherent in surveillance, suggesting HI (Humanistic Intelligence) as a way to create a more fair and balanced "human-in-the-loop" AI. [61] Explainable AI has been recently a new topic researched amongst the context of modern deep learning.
Explainable Artificial Intelligence in the context of black box machine learning models: Saliency maps are a prominent tool in XAI, [6] providing visual explanations of the decision-making process of machine learning models, particularly deep neural networks. These maps highlight the regions in input images, text, or other types of data that ...
Jerry M. Mendel is an engineer, academic, and author.He is professor emeritus of Electrical and Computer Engineering at the University of Southern California. [1]Mendel has authored and co-authored 600 technical papers and 13 books including Uncertain Rule-based Fuzzy Logic Systems: Introduction and New Directions, Explainable Uncertain Rule-Based Fuzzy Systems, Perceptual Computing: Aiding ...
The field of Explainable AI seeks to provide better explanations from existing algorithms, and algorithms that are more easily explainable, but it is a young and active field. [ 18 ] [ 19 ] Others argue that the difficulties with explainability are due to its overly narrow focus on technical solutions rather than connecting the issue to the ...
Himabindu "Hima" Lakkaraju is an Indian-American computer scientist who works on machine learning, artificial intelligence, algorithmic bias, and AI accountability.She is currently an Assistant Professor at the Harvard Business School and is also affiliated with the Department of Computer Science at Harvard University.
In the field of mathematical modeling, a radial basis function network is an artificial neural network that uses radial basis functions as activation functions.The output of the network is a linear combination of radial basis functions of the inputs and neuron parameters.