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Explainable artificial intelligence and machine learning: A reality rooted perspective
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DOI：10.1002/widm.1368.png)
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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 XAIcan 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
Keyword:
artificial intelligence
data science
explainable Artificial Intelligence
machine learning
statistics
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11.7
论文数:
542
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5.3K
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead停止解释高风险决策的黑盒机器学习模型，而改用可解释的模型OAAI
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