Explainable artificial intelligence and machine learning: A reality rooted perspective
Research output: Contribution to journal › Article › Scientific › peer-review
|Number of pages||8|
|Journal||Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery|
|Publication status||E-pub ahead of print - 2020|
|Publication type||A1 Journal article-refereed|
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.