Building software-driven systems that are easily understood becomes a challenge, with their ever-increasing complexity and autonomy. Accordingly, recent research efforts strive to aid in designing explainable systems. Nevertheless, a common notion of what it takes for a system to be explainable is still missing. To address this problem, we propose a characterization of explainable systems that consolidates existing research. By providing a unified terminology, we lay a basis for the classification of both existing and future research, and the formulation of precise requirements towards such systems.
Explainable AI (XAI) principles are a set of guidelines for the fundamental properties that explainable AI systems should adopt. Explainable AI seeks to explain the way that AI systems work. These four principles capture a variety of disciplines that contribute to explainable AI, including computer science, engineering and psychology. The four explainable AI principles apply individually, so the presence of one does not imply that others will be present. The NIST suggests that each principle should be evaluated in its own right.
Explainable Artificial Intelligence and Machine Learning: A reality rooted perspective Frank Emmert-Streib 1,2, Olli Yli-Harja 2, and Matthias Dehmer 3 1 Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland 2 Institute of Biosciences and Medical Technology, Tampere University of Technology, Tampere, Finland 3 Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Steyr Campus, 4040 Steyr, Austria January 26, 2020 Abstract We are used to the availability of big data generated in nearly all fields of science as a consequence of technological progress. However, the analysis of such data possess vast challenges. One of these relates to the explainability of artificial intelligence (AI) or machine learning methods. Currently, many of such methods are non-transparent 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. In this paper, we do not assume the usual perspective presenting explainable AI as it should be, but rather we provide a discussion what explainable AI can be . The difference is that we do not present wishful thinking but reality grounded properties in relation to a scientific theory beyond physics. 1 Introduction Artificial intelligence (AI) and machine learning (ML) have achieved great successes in a number of different learning tasks including image recognition and speech processing [1-3].
Artificial Intelligence is creating cutting-edge technologies for more efficient workflow in multiple industries across the world in this tech-driven era. There are machine learning and deep learning algorithms that are too complicated for people to understand besides AI engineers or related employees. Artificial Intelligence has generated self-explaining algorithms for stakeholders and partners to comprehend the entire process of transforming enormous complex sets of real-time data into meaningful in-depth insights. This is known as Explainable Artificial Intelligence or XAI in which the results of these solutions can be easily understood by humans. It helps AI designers to explain how AI machines have generated a specific kind of insight or outcome for businesses to thrive in the market. Multiple online courses and platforms are available for a better understanding of Explainable AI by designing interpretable and inclusive Artificial Intelligence.
Explainable artificial intelligence is an emerging method for boosting reliability, accountability, and dependence in critical areas. This is done by merging machine learning approaches with explanatory methods that reveal what the decision criteria are or why they have been established and allow people to better understand and control AI-powered tools. Below here, we have discussed some of the important milestones, in no particular order, on explainable AI (XAI) in 2020. Fairlearn is a popular explainable AI toolkit that enables data scientists as well as developers to evaluate and enhance the fairness of their AI systems. The toolkit has two components, an interactive visualisation dashboard and unfairness mitigation algorithms.