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XAI counters the "black box" tendency of machine learning, where even the AI's designers cannot explain why it arrived at a specific decision. [6] [7] XAI hopes to help users of AI-powered systems perform more effectively by improving their understanding of how those systems reason. [8] XAI may be an implementation of the social right to ...
In the more general multiple regression model, there are independent variables: = + + + +, where is the -th observation on the -th independent variable.If the first independent variable takes the value 1 for all , =, then is called the regression intercept.
A regression model may be represented via matrix multiplication as y = X β + e , {\displaystyle y=X\beta +e,} where X is the design matrix, β {\displaystyle \beta } is a vector of the model's coefficients (one for each variable), e {\displaystyle e} is a vector of random errors with mean zero, and y is the vector of predicted outputs for each ...
Solve problems and you get both the answers and confirmation that your AI can think for itself, unlike models such as OpenAI’s GPT-4 that essentially regurgitate their training material.
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 ...
Xai, XAI or xAI may refer to: Explainable artificial intelligence, in artificial intelligence technology; Xai-Xai, a city in the south of Mozambique; XAI, the IATA airport code for Xinyang Minggang Airport, in Xinyang, China; xai, the ISO 639-3 language code of Kaimbé language, an extinct language in Brazil.
The need for models that can be understood by humans emerges in quantum machine learning in analogy to classical machine learning and drives the research field of explainable quantum machine learning (or XQML [98] in analogy to XAI/XML). These efforts are often also referred to as Interpretable Machine Learning (IML, and by extension IQML). [99]
The following outline is provided as an overview of and topical guide to regression analysis: Regression analysis – use of statistical techniques for learning about the relationship between one or more dependent variables ( Y ) and one or more independent variables ( X ).