August 28th, 2019 Tutorial on Explainable AI 137 Knowledge Graph (2) Freddy Lécué: On the role of knowledge graphs in explainable AI. Go to item. Build Tools 111. Finally, in light of practice, we outline … Considering the existing literature on XAI, this paper argues that XAI in education has commonalities with the broader use of AI but also has … Our findings suggest that the explanations derived from major algorithms in the literature provide spurious correlations rather than … If the feature values of an instance are changed according to the counterfactual, the prediction changes to the predefined prediction. There are no additional assumptions and no magic in the background. This also means it is not as dangerous as methods like LIME, where it is unclear how far we can extrapolate the local model for the interpretation. 9.3 Counterfactual Explanations | Interpretable Machine … A simple and naive approach to generating counterfactual explanations is searching by trial and error. This approach involves randomly changing feature values of the instance of interest and stopping when the desired output is predicted. Like the example where Anna tried to find a version of her apartment for which she could charge more rent. A Practical Tutorial on Explainable AI Techniques Chapter 1, Explaining Artificial Intelligence with Python. Abstract Research in the social sciences has shown that expectations are an important factor in explanations as used between humans: rather than explaining the … blog - A new tool for explainable AI - Patrick Altmeyer NeurIPS

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