In the meantime, a wide variety of terminologies, motivations, approaches and evaluation criteria have been developed within the scope of research on explainable artificial intelligence (XAI). Many taxonomies can be found in the literature, each with a different focus, but also showing many points of overlap. In this paper, we summarize the most cited and current taxonomies in a meta-analysis in order to highlight the essential aspects of the state-of-the-art in XAI. We also present and add terminologies as well as concepts from a large number of survey articles on the topic. Last but not least, we illustrate concepts from the higher-level taxonomy with more than 50 example methods, which we categorize accordingly, thus providing a wide-ranging overview of aspects of XAI and paving the way for use case-appropriate as well as context-specific subsequent research.

In AI and law, systems that are designed for decision support should be explainable when pursuing justice. In order for these systems to be fair and responsible, they should make correct decisions and make them using a sound and transparent rationale. In this paper, we introduce a knowledge-driven method for model-agnostic rationale evaluation using dedicated test cases, similar to unit-testing in professional software development. We apply this new method in a set of machine learning experiments aimed at extracting known knowledge structures from artificial datasets from fictional and non-fictional legal settings. We show that our method allows us to analyze the rationale of black-box machine learning systems by assessing which rationale elements are learned or not. Furthermore, we show that the rationale can be adjusted using tailor-made training data based on the results of the rationale evaluation.

The field of explainable AI (XAI) has quickly become a thriving and prolific community. However, a silent, recurrent and acknowledged issue in this area is the lack of consensus regarding its terminology. In particular, each new contribution seems to rely on its own (and often intuitive) version of terms like "explanation" and "interpretation". Such disarray encumbers the consolidation of advances in the field towards the fulfillment of scientific and regulatory demands e.g., when comparing methods or establishing their compliance with respect to biases and fairness constraints. We propose a theoretical framework that not only provides concrete definitions for these terms, but it also outlines all steps necessary to produce explanations and interpretations. The framework also allows for existing contributions to be re-contextualized such that their scope can be measured, thus making them comparable to other methods. We show that this framework is compliant with desiderata on explanations, on interpretability and on evaluation metrics. We present a use-case showing how the framework can be used to compare LIME, SHAP and MDNet, establishing their advantages and shortcomings. Finally, we discuss relevant trends in XAI as well as recommendations for future work, all from the standpoint of our framework.

Explainable AI (XAI) is key to establishing trust among users and fighting the black-box nature of machine learning models. In general, XAI enhances accountability and reliability in machine learning models. For a long time, tech giants like Google, IBM and others have poured resources on explainable AI to explain the decision-making process of such models. Below are the top free resources to understand Explainable AI (XAI) in detail. About: Explainable Machine Learning with LIME and H2O in R is a hands-on, guided introduction to explainable machine learning.

"Trust is a must," she said. "The EU is spearheading the development of new global norms to make sure AI can be trusted. By setting the standards, we can pave the way to ethical technology worldwide." Any fast-moving technology is likely to create mistrust, but Vestager and her colleagues decreed that those in power should do more to tame AI, partly by using such systems more responsibly and being clearer about how these work. The landmark legislation – designed to "guarantee the safety and fundamental rights of people and businesses, while strengthening AI uptake, investment and innovation" – encourages firms to embrace so-called explainable AI.

A pervasive design issue of AI systems is their explainability--how to provide appropriate information to help users understand the AI. The technical field of explainable AI (XAI) has produced a rich toolbox of techniques. Designers are now tasked with the challenges of how to select the most suitable XAI techniques and translate them into UX solutions. Informed by our previous work studying design challenges around XAI UX, this work proposes a design process to tackle these challenges. We review our and related prior work to identify requirements that the process should fulfill, and accordingly, propose a Question-Driven Design Process that grounds the user needs, choices of XAI techniques, design, and evaluation of XAI UX all in the user questions. We provide a mapping guide between prototypical user questions and exemplars of XAI techniques to reframe the technical space of XAI, also serving as boundary objects to support collaboration between designers and AI engineers. We demonstrate it with a use case of designing XAI for healthcare adverse events prediction, and discuss lessons learned for tackling design challenges of AI systems.

Mumbai (Maharashtra) [India], May 7 (ANI/NewsVoir): Since the dawn of the 2000s, Artificial Intelligence (AI) has been making waves through its penetration into various sectors. While AI helps increase efficiency and speed in a system, the lack of feedback when faced with errors has been a glaring concern. Recently developed Explainable Artificial Intelligence (XAI) technology tackles this issue by analyzing data to provide users with explanations for given issues and activities. Utilizing this technology to create investment strategies, Elystar aims to increase net returns by reducing machine/AI-made errors and thereby successfully leveraging the superior insights provided by AI. "Artificial Intelligence in finance is a relatively new concept that is still being explored and experimented upon. While few of the firms experimenting are sparingly using it for short-term trading, we have spent the past 15 months developing models to use it for long-term investments. One simple way to look at this concept is to compare it with Microsoft Excel. While Excel is used in different fields and by different people, it is used in various ways and forms. Similarly, AI has a number of variations in which it can be utilized, so no two approaches may be completely the same. AI not only helps us scale and analyze data rapidly, but the integration of Explainable AI allows us to understand and eliminate unwarranted biases to create a sound investment strategy," said Dr Satya Gautam Vadlamudi, Founder and CEO of Elystar.

To realize autonomous collaborative robots, it is important to increase the trust that users have in them. Toward this goal, this paper proposes an algorithm which endows an autonomous agent with the ability to explain the transition from the current state to the target state in a Markov decision process (MDP). According to cognitive science, to generate an explanation that is acceptable to humans, it is important to present the minimum information necessary to sufficiently understand an event. To meet this requirement, this study proposes a framework for identifying important elements in the decision-making process using a prediction model for the world and generating explanations based on these elements. To verify the ability of the proposed method to generate explanations, we conducted an experiment using a grid environment. It was inferred from the result of a simulation experiment that the explanation generated using the proposed method was composed of the minimum elements important for understanding the transition from the current state to the target state. Furthermore, subject experiments showed that the generated explanation was a good summary of the process of state transition, and that a high evaluation was obtained for the explanation of the reason for an action.

It is commonly claimed that AI will replace most manual labor in the future; however, is this really the case? AI technologies do have higher image recognition accuracy compared to humans in some limited contexts, and have consistently outperformed humans in classical games such as Go and chess. Nonetheless, we believe that even advanced future developments based on current technology will not lead to robots replacing humans. AI systems' fundamental lack of ability to communicate naturally and effectively with humans is among the most significant reasons that they cannot replace human labor. Here, one may believe that such communication could be achieved via the development of natural language processing (NLP) technology [4]; however, NLP technologies are systems for estimating the content of human statements and their meanings; they do not constitute communication. That is, humans do not feel that robots using such systems truly understand and respond to them appropriately. Therefore, if effective communication is not achieved, robots will continue to function only as tools to assist humans. Advancements improving the accuracy or effectiveness of various specific tasks do not indicate that robots are equivalent to human beings. Under this scenario, how can we enable robots to communicate with humans?

In trying to describe phenomenon in the real world, we would need to build models that can closely replicate these events. In general, most modeling approaches could be grouped into two main categories: data-driven or theory-driven solutions. Data-driven approach relies on using data to make sense of the phenomenon around them, but often with limited understanding of the underlying theoretical explanation. For instance, you are told to predict the housing price in a particular neighborhood. You have a good working hypothesis to work with, such as, the size and distance to popular service amenities will have some bearing to the housing price.