scholarly journals Study on Significant Drift in the Domain of Explainable Artificial Intelligence

2021 ◽  
Author(s):  
Tavishee Chauhan ◽  
Hemant Palivela
Keyword(s):  
Artificial Intelligence ◽  
Business Strategy ◽  
Web Of Science ◽  
Daily Basis ◽  
Black Box ◽  
Quantitative Review ◽  
Multiple Resources ◽  
Explainable Artificial Intelligence ◽  
Routine Tasks ◽  
Machine Learning Models

Artificial Intelligence (AI) is required since multiple resources are in need to complete depending on a daily basis. As a result, automating routine tasks is an excellent idea. This reduces the foundation's work schedules while also improving efficiency. Furthermore, the business can obtain talented personnel for the business strategy through Artificial Intelligence. Explainability in XAI derives from a combination of strategies that improve machine learning models' environmental flexibility and interpretability. When Artificial Intelligence is trained with a large number of variables to which we apply alterations, the entire processing is turned into a black box model which is in turn difficult to understand. The data for this research's quantitative analysis is gathered from the IEEE, Web of Science, and Scopus databases. This study looked at a variety of fields engaged in the (Explainable Artificial Intelligence) XAI trend, as well as the most commonly employed techniques in domain of XAI, the location from which these studies were conducted, the year-by-year publishing trend, and the most frequently occurring keywords in the abstract. Ultimately, the quantitative review reveals that employing Explainable Artificial Intelligence or XAI methodologies, there is plenty of opportunity for more research in this field.

XHAC

2022 ◽  
pp. 146-164
Author(s):  
Duygu Bagci Das ◽  
Derya Birant
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Window Size ◽  
Classification Performance ◽  
Black Box ◽  
Data Exploration ◽  
Learning Models ◽  
Explainable Artificial Intelligence ◽  
Machine Learning Models

Explainable artificial intelligence (XAI) is a concept that has emerged and become popular in recent years. Even interpretation in machine learning models has been drawing attention. Human activity classification (HAC) systems still lack interpretable approaches. In this study, an approach, called eXplainable HAC (XHAC), was proposed in which the data exploration, model structure explanation, and prediction explanation of the ML classifiers for HAR were examined to improve the explainability of the HAR models' components such as sensor types and their locations. For this purpose, various internet of things (IoT) sensors were considered individually, including accelerometer, gyroscope, and magnetometer. The location of these sensors (i.e., ankle, arm, and chest) was also taken into account. The important features were explored. In addition, the effect of the window size on the classification performance was investigated. According to the obtained results, the proposed approach makes the HAC processes more explainable compared to the black-box ML techniques.

scholarly journals Explainable AI: A Review of Machine Learning Interpretability Methods

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 18
Author(s):  
Pantelis Linardatos ◽  
Vasilis Papastefanopoulos ◽  
Sotiris Kotsiantis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Black Box ◽  
Learning Systems ◽  
Model Complexity ◽  
Learning Models ◽  
New Methods ◽  
Industrial Adoption ◽  
Machine Learning Models ◽  
The Way

Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.

scholarly journals Artificial Cognition: How Experimental Psychology Can Help Generate Explainable Artificial Intelligence

2021 ◽  
Author(s):  
J. Eric T. Taylor ◽  
Graham Taylor
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Neural Networks ◽  
Experimental Approach ◽  
Well Being ◽  
Human Mind ◽  
Black Box ◽  
Psychological Science ◽  
Black Boxes ◽  
Explainable Artificial Intelligence

Artificial intelligence powered by deep neural networks has reached a levelof complexity where it can be difficult or impossible to express how a modelmakes its decisions. This black-box problem is especially concerning when themodel makes decisions with consequences for human well-being. In response,an emerging field called explainable artificial intelligence (XAI) aims to increasethe interpretability, fairness, and transparency of machine learning. In thispaper, we describe how cognitive psychologists can make contributions to XAI.The human mind is also a black box, and cognitive psychologists have overone hundred and fifty years of experience modeling it through experimentation.We ought to translate the methods and rigour of cognitive psychology to thestudy of artificial black boxes in the service of explainability. We provide areview of XAI for psychologists, arguing that current methods possess a blindspot that can be complemented by the experimental cognitive tradition. Wealso provide a framework for research in XAI, highlight exemplary cases ofexperimentation within XAI inspired by psychological science, and provide atutorial on experimenting with machines. We end by noting the advantages ofan experimental approach and invite other psychologists to conduct research inthis exciting new field.

Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models for Cyber-Physical Systems

2021 ◽  
pp. 42-67
Author(s):  
Evren Daglarli
Keyword(s):  
Artificial Intelligence ◽  
Cyber Physical Systems ◽  
Black Box ◽  
The Internet ◽  
Learning Models ◽  
Physical Systems ◽  
Box Models ◽  
Explainable Artificial Intelligence ◽  
Meta Learning ◽  
Black Box Models

Today, the effects of promising technologies such as explainable artificial intelligence (xAI) and meta-learning (ML) on the internet of things (IoT) and the cyber-physical systems (CPS), which are important components of Industry 4.0, are increasingly intensified. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. For these reasons, it is necessary to make serious efforts on the explanability and interpretability of black box models. In the near future, the integration of explainable artificial intelligence and meta-learning approaches to cyber-physical systems will have effects on a high level of virtualization and simulation infrastructure, real-time supply chain, cyber factories with smart machines communicating over the internet, maximizing production efficiency, analysis of service quality and competition level.

scholarly journals Explainable Artificial Intelligence (XAI) to Enhance Trust Management in Intrusion Detection Systems Using Decision Tree Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Basim Mahbooba ◽  
Mohan Timilsina ◽  
Radhya Sahal ◽  
Martin Serrano
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Trust Management ◽  
Decision Tree Model ◽  
Learning Models ◽  
Tree Model ◽  
Explainable Artificial Intelligence ◽  
Machine Learning Models

Despite the growing popularity of machine learning models in the cyber-security applications (e.g., an intrusion detection system (IDS)), most of these models are perceived as a black-box. The eXplainable Artificial Intelligence (XAI) has become increasingly important to interpret the machine learning models to enhance trust management by allowing human experts to understand the underlying data evidence and causal reasoning. According to IDS, the critical role of trust management is to understand the impact of the malicious data to detect any intrusion in the system. The previous studies focused more on the accuracy of the various classification algorithms for trust in IDS. They do not often provide insights into their behavior and reasoning provided by the sophisticated algorithm. Therefore, in this paper, we have addressed XAI concept to enhance trust management by exploring the decision tree model in the area of IDS. We use simple decision tree algorithms that can be easily read and even resemble a human approach to decision-making by splitting the choice into many small subchoices for IDS. We experimented with this approach by extracting rules in a widely used KDD benchmark dataset. We also compared the accuracy of the decision tree approach with the other state-of-the-art algorithms.

scholarly journals Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models

2020 ◽  
Author(s):  
Evren Dağlarli
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Black Box ◽  
Learning Models ◽  
Learning Methods ◽  
Data Set ◽  
Box Models ◽  
Explainable Artificial Intelligence ◽  
Artificial Neural ◽  
Black Box Models

The explainable artificial intelligence (xAI) is one of the interesting issues that has emerged recently. Many researchers are trying to deal with the subject with different dimensions and interesting results that have come out. However, we are still at the beginning of the way to understand these types of models. The forthcoming years are expected to be years in which the openness of deep learning models is discussed. In classical artificial intelligence approaches, we frequently encounter deep learning methods available today. These deep learning methods can yield highly effective results according to the data set size, data set quality, the methods used in feature extraction, the hyper parameter set used in deep learning models, the activation functions, and the optimization algorithms. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network-based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. This is an important open point in artificial neural networks and deep learning models. For these reasons, it is necessary to make serious efforts on the explainability and interpretability of black box models.

scholarly journals The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1406
Author(s):  
Salih Sarp ◽  
Murat Kuzlu ◽  
Emmanuel Wilson ◽  
Umit Cali ◽  
Ozgur Guler
Keyword(s):  
Artificial Intelligence ◽  
Data Science ◽  
Chronic Wounds ◽  
Chronic Wound ◽  
Hybrid Approach ◽  
Black Box ◽  
Large Matrix ◽  
Explainable Artificial Intelligence ◽  
Wound Classification ◽  
Application Fields

Artificial Intelligence (AI) has been among the most emerging research and industrial application fields, especially in the healthcare domain, but operated as a black-box model with a limited understanding of its inner working over the past decades. AI algorithms are, in large part, built on weights calculated as a result of large matrix multiplications. It is typically hard to interpret and debug the computationally intensive processes. Explainable Artificial Intelligence (XAI) aims to solve black-box and hard-to-debug approaches through the use of various techniques and tools. In this study, XAI techniques are applied to chronic wound classification. The proposed model classifies chronic wounds through the use of transfer learning and fully connected layers. Classified chronic wound images serve as input to the XAI model for an explanation. Interpretable results can help shed new perspectives to clinicians during the diagnostic phase. The proposed method successfully provides chronic wound classification and its associated explanation to extract additional knowledge that can also be interpreted by non-data-science experts, such as medical scientists and physicians. This hybrid approach is shown to aid with the interpretation and understanding of AI decision-making processes.

scholarly journals Towards an Explainer-agnostic Conversational XAI

2021 ◽  
Author(s):  
Navid Nobani ◽  
Fabio Mercorio ◽  
Mario Mezzanzanica
Keyword(s):  
Artificial Intelligence ◽  
Black Box ◽  
The Other ◽  
Human Being ◽  
End User ◽  
Other Hand ◽  
Explainable Artificial Intelligence ◽  
Overall Performance

Explainable Artificial Intelligence (XAI) is gaining interests in both academia and industry, mainly thanks to the proliferation of darker more complex black-box solutions which are replacing their more transparent ancestors. Believing that the overall performance of an XAI system can be augmented by considering the end-user as a human being, we are studying the ways we can improve the explanations by making them more informative and easier to use from one hand, and interactive and customisable from the other hand.

scholarly journals Offenheit durch XAI bei ML-unterstützten Entscheidungen: Ein Baustein zur Optimierung von Entscheidungen im Unternehmen?

2021 ◽  
Author(s):  
Christian Lossos ◽  
Simon Geschwill ◽  
Frank Morelli
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Black Box ◽  
Künstliche Intelligenz ◽  
Gesetzliche Grundlagen ◽  
Mathematische Modelle ◽  
Explainable Artificial Intelligence ◽  
Post Hoc

ZusammenfassungKünstliche Intelligenz (KI) und Machine Learning (ML) gelten gegenwärtig als probate Mittel, um betriebswirtschaftliche Entscheidungen durch mathematische Modelle zu optimieren. Allerdings werden die Technologien häufig in Form von „Black Box“-Ansätze mit entsprechenden Risiken realisiert. Der Einsatz von Offenheit kann in diesem Kontext mehr Objektivität schaffen und als Treiber für innovative Lösungen fungieren. Rationale Entscheidungen im Unternehmen dienen im Sinne einer Mittel-Zweck-Beziehung dazu, Wettbewerbsvorteile zu erlangen. Im Sinne von Governance und Compliance sind dabei regulatorische Rahmenwerke wie COBIT 2019 und gesetzliche Grundlagen wie die Datenschutz-Grundverordnung (DSGVO) zu berücksichtigen, die ihrerseits ein Mindestmaß an Transparenz einfordern. Ferner sind auch Fairnessaspekte, die durch Bias-Effekte bei ML-Systemen beeinträchtigt werden können, zu berücksichtigen. In Teilaspekten, wie z. B. bei der Modellerstellung, wird in den Bereichen der KI und des ML das Konzept der Offenheit bereits praktiziert. Das Konzept der erklärbaren KI („Explainable Artificial Intelligence“ – XAI) vermag es aber, das zugehörige Potenzial erheblich steigern. Hierzu stehen verschiedene generische Ansätze (Ante hoc‑, Design- und Post-hoc-Konzepte) sowie die Möglichkeit, diese untereinander zu kombinieren, zur Verfügung. Entsprechend müssen Chancen und Grenzen von XAI systematisch reflektiert werden. Ein geeignetes, XAI-basiertes Modell für das Fällen von Entscheidungen im Unternehmen lässt sich mit Hilfe von Heuristiken näher charakterisieren.

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