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Explainable artificial intelligence and machine learning: A reality rooted perspective

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Abstract As a consequence of technological progress, nowadays, one is used to the availability of big data generated in nearly all fields of science. However, the analysis of such data possesses vast challenges. One of these challenges relates to the explainability of methods from artificial intelligence (AI) or machine learning. Currently, many of such methods are nontransparent with respect to their working mechanism and for this reason are called black box models, most notably deep learning methods. However, it has been realized that this constitutes severe problems for a number of fields including the health sciences and criminal justice and arguments have been brought forward in favor of an explainable AI (XAI). In this paper, we do not assume the usual perspective presenting XAI as it should be, but rather provide a discussion what XAI can be. The difference is that we do not present wishful thinking but reality grounded properties in relation to a scientific theory beyond physics. This article is categorized under: Fundamental Concepts of Data and Knowledge > Explainable AI Algorithmic Development > Statistics Technologies > Machine Learning
An overview describing the limitations of explainable artificial intelligence (AI) with respect to attainable goals. (Left) Different scientific fields are arranged according to their increasing complexity (Anderson, 1972) starting from the best (most comprehensive) theories of physics in the center. The further the distance from these theories the less comprehensive are the models describing subjects of complex adaptive systems (left coordinate system). (Right) Any AI system analyzes a random sample of data drawn from a population. One source of uncertainty is provided by the sample size of the data (right coordinate system) that translates directly into uncertain statements about the population
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Technologies > Machine Learning
Algorithmic Development > Statistics
Fundamental Concepts of Data and Knowledge > Explainable AI

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