Questions tagged [explainable-ai]

For questions related to explainable artificial intelligence (XAI), also known as interpretable AI, which refers to AI techniques that can be trusted and easily understood by humans, which are particularly relevant in areas like healthcare or self-driving cars. There are several concepts related to XAI, such as accountability, fairness, and transparency.

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Black Box Explanations : Using LIME and SHAP in python

Recently I came along this paper : Robust and Stable Black Box Explanations (https://proceedings.icml.cc/static/paper_files/icml/2020/5945-Paper.pdf) which discuss a nice framework for global model-...
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Who is working on explaining the knowledge encoded into machine learning models?

The thing about machine learning (ML) that worries me is that "knowledge" acquired in ML is hidden: we usually can't explain the criteria or methods used by the machine to provide an answer ...
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How can we create eXplainable Artificial Intelligence?

Currently, we can build the Artificial Intelligence (AI) approaches that respectively explain their actions within the use of goal trees 1. By moving up and down across the tree, it keeps tracking the ...
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Is it possible to create a fair machine learning system?

I started thinking about the fairness of machine learning models recently. Wiki page for Fairness_(machine_learning) defines fairness as: In machine learning, a given algorithm is said to be fair, ...