Explainable artificial intelligence

Abstract. The last decade has witnessed the rise of a black box society where obscure classification models are adopted by Artificial Intelligence systems (AI). The lack of explanations of how AI systems make decisions is a key ethical issue to their adoption in socially sensitive and safety-critical contexts.

The Explainable AI (XAI) program aims to create a suite of machine learning techniques that: Produce more explainable models, while maintaining a high level of learning …When applied properly, explainable artificial intelligence decision support schemes may help patients feel more informed and in charge of their health, as well as enhance their risk perceptions [81, 82]. As a consequence, patients’ willingness to engage in collaborative act and decision-making on risk-relevant …Apr 15, 2020 · 9. Image from Unsplash. Explainable AI is one of the hottest topics in the field of Machine Learning. Machine Learning models are often thought of as black boxes that are imposible to interpret. In the end, these models are used by humans who need to trust them, understand the errors they make, and the reasoning behind their predictions. Explainable AI is an artificial intelligence method or technique in which the solution can be evaluated and understood by humans. It differs from standard ML techniques, in which researchers frequently fail to comprehend why the system has reached a particular conclusion.Explainable Artificial Intelligence in Education: A Comprehensive Review. Blerta Abazi Chaushi, Besnik Selimi, Agron Chaushi, Marika Apostolova; Pages 48-71. Contrastive Visual Explanations for Reinforcement Learning via Counterfactual Rewards. Xiaowei Liu, Kevin McAreavey, Weiru Liu;Apr 15, 2020 ... Explainable AI is one of the hottest topics in the field of Machine Learning. Machine Learning models are often thought of as black boxes ...The world of Artificial Intelligence (AI) is rapidly growing and evolving. As a result, many professionals are looking for ways to stay ahead of the curve and gain the skills neces...Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective. Given the complexity and lack of transparency in deep neural networks (DNNs), extensive efforts have been made to make these systems more interpretable or explain their behaviors in accessible terms. Unlike most reviews, which focus on …

Genomics. Artificial intelligence (AI) models based on deep learning now represent the state of the art for making functional predictions in genomics research. However, the underlying basis on which predictive models make such predictions is often unknown. For genomics researchers, this missing explanatory in ….Explainable artificial intelligence reveals the interactive effects of environmental variables in species distribution models. Abstract Seagrass is a globally vital marine resource that plays an essential global role in combating climate change, protecting coastlines, ensuring food security, and enriching biodiversity.“An explainable Artificial Intelligence is one that produces explanations about its functioning”) would fail to fully characterize the term in question, leaving …The goal of XAI is to develop AI models that can provide clear explanations of their decision-making processes so that humans can trust and verify their ...Abstract. This study focuses on explainable artificial intelligence (XAI) in finance. We collected 2,733 articles published between 2013 and 2023 from the Web of Science Core Collection and analyzed trends in literature development and future prospects using an integrated CiteSpace and Natural Language Processing (NLP) bibliometric …Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to spark criticism. Yet, explainability is not a purely technological issue ...Additionally, it was observed that the application of DL models in the FDS domain [29,31] causes; therefore, interpretability issues, Explainable Artificial Intelligence (XAI) techniques SHapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were implemented to overcome the problem of …UNITED NATIONS (AP) — The General Assembly approved the first United Nations resolution on artificial intelligence Thursday, giving global support to an …

The field of artificial intelligence encompasses computer science, natural language processing, coding, mathematics, data science, and many other disciplines. An AI tutorial or free artificial intelligence course for beginners can teach learners: The uses of AI for businesses and society. Ethics issues related to AI.Explainable Artificial Intelligence: Concepts and Current Progression. Chapter © 2023. Methods and Metrics for Explaining Artificial Intelligence Models: A …In recent years, the automotive industry has seen a rapid integration of software into vehicles. From advanced driver assistance systems to connected car technologies, software has...The Explainable AI (XAI) program aims to create a suite of machine learning techniques that: Produce more explainable models, while maintaining a high level of learning …

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Explainable Artificial Intelligence (XAI), a promising future technology in the field of healthcare, has attracted significant interest. Despite ongoing efforts in the …Jun 1, 2023 · Explainable Artificial Intelligence (XAI) is a term that refers to Artificial Intelligence (AI) that can provide explanations for their decision or predictions to human users. XAI aims to increase the transparency, trustworthiness and accountability of AI system, especially when they are used for high-stakes application such as healthcare ... Explainable artificial intelligence is often discussed in relation to deep learning and plays an important role in the FAT -- fairness, accountability and transparency -- ML model. XAI is useful for organizations that want to adopt a responsible approach to the development and implementation of AI models.Additionally, it was observed that the application of DL models in the FDS domain [29,31] causes; therefore, interpretability issues, Explainable Artificial Intelligence (XAI) techniques SHapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were implemented to overcome the problem of …NEW YORK, Feb. 19, 2020 /PRNewswire-PRWeb/ -- 'Artificial intelligence will soon leave people displaced and needing to find a new way to put food ... NEW YORK, Feb. 19, 2020 /PRNew...Introduction. Artificial Intelligence (AI), a research area initiated in the 1950ies (Mccarthy et al., Citation 2006), has received significant attention in science and practice.Global spending on AI systems is expected to more than double from 38 billion USD in 2019 to 98 billion USD by 2023 (Shirer & Daquila, Citation 2019).Emphasizing on …

The Explainable Artificial Intelligence (XAI) research area, as a developing branch of artificial intelligence (AI), is investigating various approaches that will allow the behavior of intelligent autonomous systems to be interpretable and understandable to humans. Human–machine interaction, on the bridge between Data Science and Social ...With the extensive application of deep learning (DL) algorithms in recent years, e.g., for detecting Android malware or vulnerable source code, artificial intelligence (AI) and machine learning (ML) are increasingly becoming essential in the development of cybersecurity solutions. However, sharing the same fundamental limitation with other DL …An AI (artificial intelligence) sign is seen at the World Artificial Intelligence Conference in Shanghai, China on July 6, 2023 [File: Aly Song/Reuters]Apr 15, 2020 · 9. Image from Unsplash. Explainable AI is one of the hottest topics in the field of Machine Learning. Machine Learning models are often thought of as black boxes that are imposible to interpret. In the end, these models are used by humans who need to trust them, understand the errors they make, and the reasoning behind their predictions. We applied explainable artificial intelligence (XAI) on a stack-ensemble machine learning model framework to explore and visualize the spatial distribution of the contributions of known risk ...Explainability is essential for users to effectively understand, trust, and manage powerful artificial intelligence applications. Recent successes in machine …Artificial intelligence (AI) is a rapidly growing field of computer science that focuses on creating intelligent machines that can think and act like humans. AI has been around for...In recent years, artificial intelligence (AI) has shown great promise in medicine. However, explainability issues make AI applications in clinical usages difficult. Some research has been conducted into explainable artificial intelligence (XAI) to overcome the limitation of the black-box nature of AI methods. Compared with AI …

This study is a first attempt to provide an eXplainable artificial intelligence (XAI) framework for estimating wildfire occurrence using a Random Forest model with Shapley values for interpretation.

The world of Artificial Intelligence (AI) is rapidly growing and evolving. As a result, many professionals are looking for ways to stay ahead of the curve and gain the skills neces...In recent years, there has been a significant surge in the adoption of industrial automation across various sectors. This rise can be attributed to the advancements in artificial i...May 12, 2022 · 1 Introduction. «1» Generally speaking, Artificial Intelligence (AI) plays two roles in Decision-Making. The first one is as an assistant to the process itself, by providing information through inference (e.g., a profile about a subject or situation) to the (human) agent responsible for the decision. Artificial intelligence (AI) and machine learning (ML) have demonstrated their potential to revolutionize industries, public services, and society, achieving or even surpassing human levels of performance in terms of accuracy for a range of problems, such as image and speech recognition (Mnih et al., 2015) and language translation (Young et …Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective. Given the complexity and lack of transparency in deep neural networks (DNNs), extensive efforts have been made to make these systems more interpretable or explain their behaviors in accessible terms. Unlike most reviews, which focus on …Apr 17, 2022 · Explainable Artificial Intelligence (xAI) is an established field with a vibrant community that has developed a variety of very successful approaches to explain and interpret predictions of complex machine learning models such as deep neural networks. How does machine learning work? Learn more about how artificial intelligence makes its decisions in this HowStuffWorks Now article. Advertisement If you want to sort through vast n...Wohlin conducted a review of the literature related to explainable artificial intelligence systems, with a focus on knowledge-enabled systems, including expert systems, cognitive assistants, semantic applications, and machine learning domains. In this review, Wohlin proposed new definitions for explainable knowledge-enabled systems …Jun 23, 2023 · Explainable AI is a set of techniques, principles and processes used to help the creators and users of artificial intelligence models understand how these models make decisions. This information can be used to improve model accuracy or to identify and address unwanted behaviors like biased decision-making. Explainable AI can be used to describe ... The World Conference on Explainable Artificial Intelligence is an annual event that aims to bring together researchers, academics, and professionals, promoting the sharing and discussing of knowledge, new perspectives, experiences, and innovations in eXplainable Artificial Intelligence (XAI). This event is multidisciplinary and ...

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Feb 16, 2022 ... Working Towards Explainable AI ... “The hardest thing to understand in the world is the income tax.” This quote comes from the man who came up ...Artificial intelligence (AI) has become an integral part of the modern business landscape, revolutionizing industries across the globe. One such company that has embraced AI as a k...Artificial Intelligence (AI) is a rapidly growing field of technology that has already made a significant impact on many industries. AI is the development of computer systems that ...In recent years, there has been a significant surge in the adoption of industrial automation across various sectors. This rise can be attributed to the advancements in artificial i...To reach a better understanding of how AI models come to their decisions, organizations are turning to explainable artificial intelligence (AI). What Is Explainable AI? Explainable AI, also abbreviated as XAI, is a set of tools and techniques used by organizations to help people better understand why a model makes certain decisions and …The emergence of Explainable Artificial Intelligence (XAI) has enhanced the lives of humans and envisioned the concept of smart cities using informed actions, enhanced user interpretations and explanations, and firm decision-making processes. The XAI systems can unbox the potential of black-box AI models and describe them explicitly. …Explainable artificial intelligence (AI) has drawn a lot of attention recently since AI systems are being employed more often across a variety of industries, including education. Building trust and increasing the efficacy of AI systems in educational settings requires the capacity to explain how they make decisions. This article provides a ...The emergence of Explainable Artificial Intelligence (XAI) has enhanced the lives of humans and envisioned the concept of smart cities using informed actions, enhanced user interpretations and explanations, and firm decision-making processes. The XAI systems can unbox the potential of black-box AI models and describe them explicitly. … ….

Dec 22, 2023 · While explainable artificial intelligence (XAI) has gained ground in diverse fields, including healthcare, numerous unexplored facets remain within the realm of medical imaging. To better understand the complexities of DL techniques, there is an urgent need for rapid advancement in the field of eXplainable DL (XDL) or eXplainable Artificial ... An AI (artificial intelligence) sign is seen at the World Artificial Intelligence Conference in Shanghai, China on July 6, 2023 [File: Aly Song/Reuters]Explainable AI is one of the hottest topics in the field of Machine Learning. Machine Learning models are often thought of as black boxes that are imposible to …In recent years, the automotive industry has seen a rapid integration of software into vehicles. From advanced driver assistance systems to connected car technologies, software has...XAI—Explainable artificial intelligence. Explainability is essential for users to effectively understand, trust, and manage powerful artificial intelligence applications. Recent successes in machine learning (ML) have led to a new wave of artificial intelligence (AI) applications that offer extensive benefits to a diverse range of fields.Taxonomy of explainable artificial intelligence based on the taxonomy proposed by Belle and Papantonis [58].. Model-agnostic methods (also post-hoc) are divided into two major approaches: partial dependency plots and surrogate models. Partial dependency plots can only provide pairwise …Aug 10, 2023 · The field of explainable artificial intelligence (XAI) has witnessed the emergence of numerous methods and techniques aimed at comprehending the intricate workings of deep learning models. Currently, some survey papers have made efforts to summarize these methods and offer a fundamental understanding of the distinctions among various XAI ... Nov 1, 2022 · Explainable artificial intelligence reveals the interactive effects of environmental variables in species distribution models. Abstract Seagrass is a globally vital marine resource that plays an essential global role in combating climate change, protecting coastlines, ensuring food security, and enriching biodiversity. In this article, we propose a change of paradigm for explainability in data science for the case of SAR data to ground explainable artificial intelligence (XAI) for SAR. It aims to use explainable data transformations based on well-established models to generate inputs for AI methods, to provide knowledgeable … Explainable artificial intelligence, Science has always been at the forefront of human progress, driving innovation and shaping the future. In recent years, artificial intelligence (AI) has emerged as a powerful tool ..., Explainable artificial intelligence (AI) has drawn a lot of attention recently since AI systems are being employed more often across a variety of industries, including education. Building trust and increasing the efficacy of AI systems in educational settings requires the capacity to explain how they make decisions. This article provides a ..., This graduate level course aims to familiarize students with the recent advances in the emerging field of eXplainable Artificial Intelligence (XAI). In this course, we will review seminal position papers in the field, understand the notion of explainability from the perspective of different end users (e.g., doctors, ML researchers/engineers ... , Explainable artificial intelligence In this study, we primarily discuss ML, a subset of AI that enables computers to learn and improve without being explicitly programmed. ML algorithms employ statistical models to analyse vast amounts of data, identifying patterns, trends, and associations within the data., These molecular data, combined with clinical and imaging information, will create an evidence base for the development of a machine learning tool based on explainable artificial intelligence (AI ..., Explainable AI principles. To expand on the idea of what constitutes XAI, the National Institute of Standards (NIST), part of the U.S. Department of Commerce, defines four principles of explainable artificial intelligence: An AI system should supply “evidence, support, or reasoning for each output.”, The Explainable AI (XAI) program aims to create a suite of machine learning techniques that: Produce more explainable models, while maintaining a high level of learning …, Defense Advanced Research Projects Agency (DARPA) formulated the explainable artificial intelligence (XAI) program in 2015 with the goal to enable end …, 説明可能なAI （せつめいかのうなエーアイ、英語: Explainable artificial intelligence 、略称XAI）またはAIを説明するための技術 は、人工知能 (AI) が導き出した答えに対して、人間が納得できる根拠を示すための技術である 。 , The method proposed in this paper underlines the great potential of explainable artificial intelligence in cancer research 57,58,59,60,61,62. While the prediction of sample-wise networks is ..., Explainable artificial intelligence. XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision., Explainable artificial intelligence (XAI) promises to resolve the issue of explainability and interpretability of DL black boxes . With the continuously increasing volume of customer review data, a robust end-to-end framework using AI/ML can help accurately predict customer sentiment. Such a framework will be beneficial for FDS …, This graduate level course aims to familiarize students with the recent advances in the emerging field of eXplainable Artificial Intelligence (XAI). In this course, we will review seminal position papers in the field, understand the notion of explainability from the perspective of different end users (e.g., doctors, ML researchers/engineers ... , Intelligent agents must be able to communicate intentions and explain their decision-making processes to build trust, foster confidence, and improve human-agent team dynamics. Recognizing this need, academia and industry are rapidly proposing new ideas, methods, and frameworks to aid in the design of …, Jul 1, 2021 · Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these “stakeholders' desiderata”) in a variety of contexts. , Feb 26, 2024 · What users obtain from explainable artificial intelligence is the precondition for trust, rather than novel knowledge. This trust necessitates satisfaction via a comprehensive RDF, implying that ... , Artificial intelligence (AI) models based on deep learning now represent the state of the art for making functional predictions in genomics research. However, the underlying basis on which ..., The first section, titled “Introduction,” provides an overall summary of the Explainable Artificial Intelligence. Section 2 describes the need of trust and transparency in AI, which is what led to the development of the idea of XAI. Section 3 discusses the many approaches that contribute to the functioning of XAI., Abstract. Explainable artificial intelligence (AI) has drawn a lot of attention recently since AI systems are being employed more often across a variety of industries, including education. Building trust and increasing the efficacy of AI systems in educational settings requires the capacity to explain how they make decisions., Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence - ScienceDirect. RegisterSign in. View PDF. …, March 19, 2024. The government of Saudi Arabia plans to create a fund of about $40 billion to invest in artificial intelligence, according to three people briefed on the plans — the …, This three-volume set constitutes the refereed proceedings of the First World Conference on Explainable Artificial Intelligence, xAI 2023, held in Lisbon, Portugal, in July 2023. The 94 papers presented were thoroughly reviewed and selected from the 220 qualified submissions. They are organized in the following topical sections:, Abstract. Explainable artificial intelligence (AI) has drawn a lot of attention recently since AI systems are being employed more often across a variety of industries, including education. Building trust and increasing the efficacy of AI systems in educational settings requires the capacity to explain how they make decisions., The Explainable AI (XAI) program aims to create a suite of machine learning techniques that: Produce more explainable models, while maintaining a high level of learning …, Intelligent agents must be able to communicate intentions and explain their decision-making processes to build trust, foster confidence, and improve human-agent team dynamics. Recognizing this need, academia and industry are rapidly proposing new ideas, methods, and frameworks to aid in the design of …, Artificial intelligence (AI) is a rapidly growing field of computer science that focuses on creating intelligent machines that can think and act like humans. AI has been around for..., A cyber-physical system (CPS) can be referred to as a network of cyber and physical components that communicate with each other in a feedback manner. A CPS is essential for daily activities and approves critical infrastructure as it provides the base for innovative smart devices. The recent advances in the field of explainable artificial …, Artificial intelligence (AI) has become an integral part of the modern business landscape, revolutionizing industries across the globe. One such company that has embraced AI as a k..., Aug 18, 2020 · Abstract. We introduce four principles for explainable artificial intelligence (AI) that comprise the fundamental properties for explainable AI systems. They were developed to encompass the multidisciplinary nature of explainable AI, including the fields of computer science, engineering, and psychology. Because one size fits all explanations do ... , Explainable Artificial Intelligence (XAI), a promising future technology in the field of healthcare, has attracted significant interest. Despite ongoing efforts in the …, Apr 6, 2020 · NIST held a virtual workshop on Explainable Artificial Intelligence (AI) on January 26-28, 2021. Explainable AI is a key element of trustworthy AI and there is significant interest in explainable AI from stakeholders, communities, and areas across this multidisciplinary field. As part of NIST’s efforts to provide foundational tools, guidance ... , Early prediction of students’ learning performance and analysis of student behavior in a virtual learning environment (VLE) are crucial to minimize the high failure rate in online courses during the COVID-19 pandemic. Nevertheless, traditional machine learning models fail to predict student performance in the early …, The Explainable AI (XAI) program aims to create a suite of machine learning techniques that: Produce more explainable models, while maintaining a high level of learning …