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Explainable AI has been recently a new topic researched amongst the context of modern deep learning. Modern complex AI techniques, such as deep learning, are naturally opaque. [62] To address this issue, methods have been developed to make new models more explainable and interpretable.
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 ...
Explainable artificial intelligence encompasses both explainability and interpretability, with explainability relating to summarizing neural network behavior and building user confidence, while interpretability is defined as the comprehension of what a model has done or could do.
Open-source AI models allow for the free and open sharing of software to anyone for any purpose. Yann LeCun said that an open-source model allows everyone to benefit because progress is faster.
Trustworthy AI is also a work programme of the International Telecommunication Union, an agency of the United Nations, initiated under its AI for Good programme. [2] Its origin lies with the ITU-WHO Focus Group on Artificial Intelligence for Health, where strong need for privacy at the same time as the need for analytics, created a demand for a standard in these technologies.
The Government has set out its “adaptable” approach to regulating artificial intelligence, as it hopes to build public trust in the rapidly developing technology and tap its economic potential
Open-source artificial intelligence is an AI system that is freely available to use, study, modify, and share. [1] These attributes extend to each of the system's components, including datasets, code, and model parameters, promoting a collaborative and transparent approach to AI development. [1]
Approaches for integration are diverse. [10] Henry Kautz's taxonomy of neuro-symbolic architectures [11] follows, along with some examples: . Symbolic Neural symbolic is the current approach of many neural models in natural language processing, where words or subword tokens are the ultimate input and output of large language models.