Local Interpretable Model-Agnostic Explanation (LIME) One commonly used post-hoc explanation algorithm is called LIME, or local interpretable model-agnostic explanation. LIME takes decisions and, by querying nearby points, builds an interpretable model that represents the decision, then uses that model...
By running simulations and comparing XAI output to the results in the training data set, the prediction accuracy can be determined. The most popular technique used for this is Local Interpretable Model-Agnostic Explanations (LIME), which explains the prediction of classifiers by the ML algorithm. ...
The best understood area of XAI is individual decision-making: why a person didn’t get approved for a loan, for instance. Techniques with names like LIME andSHAPoffer very literal mathematical answers to this question — and the results of that math can be presented to data scientists, manag...
more than could be processed by a human in a lifetime. Besides predicting the text's category very accurately, it is also highly desirable to understand how and why the categorization process takes place. In this paper, we demonstrate that such understanding can be achieved by tracing the clas...
Explainable AI (XAI) techniques are used after the fact to make the output of more complex ML models more comprehensible to human observers. Examples include local interpretable model-agnostic explanations (LIME), which approximate the model's behavior locally with simpler models to explain in...
LIME的解释示例 1.2 公平性 机器学习模型是数据驱动的,也就是我们给模型足够多的数据,模型能给我们拟合出一个非常理想的决策边界,但在这个过程中模型可能会学习到一些具有偏见的内容,从而输出违反伦理道德的决策。一个典型的案例是ProPublica组织[2]发现美国法院所使用的软件存在非常严重的歧视问题,具体来说,黑人被告远...
Explainability is about verification, or providing justifications for the model's outputs, often after it makes its predictions. Explainable AI (XAI) is used to identify the factors that led to the results. Various explainability methods can be used to present the models in ways that make their...
where there is a single model that is being explained with a single explanation. In these cases a typical approach is to identify the features in the image that are important in defining the classification, for example as a heat map over the image. Tools such as LIME (Ribeiro et al.2016...
Mergulhe na IA explicável (XAI) e saiba como criar confiança em sistemas de IA com LIME e SHAP para interpretabilidade de modelos. Entenda a importância da transparência e da justiça nas decisões baseadas em IA. Zoumana Keita 12 minVer mais ...
Why XAI Explains Individual Decisions Best The best understood area of XAI is individual decision-making: why a person didn’t get approved for a loan, for instance. Techniques with names like LIME andSHAPoffer very literal mathematical answers to this question — and the results of that math ...