scholarly journals A Multi-Component Framework for the Analysis and Design of Explainable Artificial Intelligence

2021 ◽  
Vol 3 (4) ◽  
pp. 900-921
Author(s):  
Mi-Young Kim ◽  
Shahin Atakishiyev ◽  
Housam Khalifa Bashier Babiker ◽  
Nawshad Farruque ◽  
Randy Goebel ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Research Activity ◽  
High Expectations ◽  
Analysis And Design ◽  
Machine Learning Methods ◽  
Explainable Artificial Intelligence ◽  
History Of ◽  
Tools And Techniques ◽  
Perfect Storm ◽  
Modern Machine

The rapid growth of research in explainable artificial intelligence (XAI) follows on two substantial developments. First, the enormous application success of modern machine learning methods, especially deep and reinforcement learning, have created high expectations for industrial, commercial, and social value. Second, the emerging and growing concern for creating ethical and trusted AI systems, including compliance with regulatory principles to ensure transparency and trust. These two threads have created a kind of “perfect storm” of research activity, all motivated to create and deliver any set of tools and techniques to address the XAI demand. As some surveys of current XAI suggest, there is yet to appear a principled framework that respects the literature of explainability in the history of science and which provides a basis for the development of a framework for transparent XAI. We identify four foundational components, including the requirements for (1) explicit explanation knowledge representation, (2) delivery of alternative explanations, (3) adjusting explanations based on knowledge of the explainee, and (4) exploiting the advantage of interactive explanation. With those four components in mind, we intend to provide a strategic inventory of XAI requirements, demonstrate their connection to a basic history of XAI ideas, and then synthesize those ideas into a simple framework that can guide the design of AI systems that require XAI.

scholarly journals Transkribus im Archiv – Ein polnisch-deutsches Projekt… zur Handschriftentexterkennung an historischen Dokumenten

Archeion ◽  
2021 ◽  
Vol 122 ◽  
Author(s):  
Dirk Alvermann ◽  
Paweł Gut
Keyword(s):  
Artificial Intelligence ◽  
Handwriting Recognition ◽  
Historical Documents ◽  
Machine Learning Methods ◽  
Technical Requirements ◽  
Large Numbers ◽  
Historical Manuscripts ◽  
Work Input ◽  
Automated Processes ◽  
Modern Machine

[Transkribus in the archives – a Polish-German project of reading historical documents] Even 10 years ago, the idea that historical manuscripts, regardless of time of creation and origin, could be „read“ and searched using automated processes seemed unrealistic. However, thanks to modern machine learning methods and the use of artificial intelligence, it is now possible. Following the development of Transkribus platform (http://transkribus.eu/), a tool has become available that allows free open access to this technology. Handwriting recognition permits automatic conversion of large numbers of historical manuscripts into fully legible texts. This development will influence and change the work of archives over the next several years, especially with regard to how their collections are made accessible digitally. Using the example of a Polish-German cooperation project, the article presents the use of handwriting recognition technology in the context of an archival digitisation project and discusses the technical requirements, technological work input and results of using Transkribus in an archive. [Transkribus w archiwum – polsko-niemiecki projekt odczytania dokumentów historycznych] Jeszcze 10 lat temu pomysł, że rękopisy historyczne, niezależnie od czasu i pochodzenia, można „czytać” i przeszukiwać za pomocą zautomatyzowanych procesów, wydawał się nierealny. Dzięki nowoczesnym metodom uczenia się maszynowego i wykorzystaniu sztucznej inteligencji jest to obecnie możliwe. Wraz z rozwojem platformy Transkribus (http://transkribus.eu/) dostępne jest narzędzie, które pozwala na otwarty dostęp do tej technologii. Rozpoznawanie pisma ręcznego umożliwia automatyczną konwersję dużej liczby rękopisów historycznych na w pełni czytelne teksty. Ten rozwój wpłynie i zmieni pracę archiwów w perspektywie kilkunastu lat, zwłaszcza sposób cyfrowego udostępniania ich zbiorów. Na przykładzie polsko-niemieckiego projektu współpracy, w artykule przedstawiono wykorzystanie technologii rozpoznawania pisma ręcznego w kontekście projektu digitalizacji archiwalnej oraz omówiono wymagania techniczne, wkład prac technologicznych i rezultaty wykorzystania Transkribusa w archiwum.

scholarly journals Explanation in Constraint Satisfaction: A Survey

2021 ◽  
Author(s):  
Sharmi Dev Gupta ◽  
Begum Genc ◽  
Barry O'Sullivan
Keyword(s):  
Artificial Intelligence ◽  
Constraint Satisfaction ◽  
Boolean Satisfiability ◽  
Machine Learning Methods ◽  
General Field ◽  
History Of ◽  
Maintenance Systems ◽  
Truth Maintenance ◽  
Truth Maintenance Systems ◽  
Explanation Generation

Much of the focus on explanation in the field of artificial intelligence has focused on machine learning methods and, in particular, concepts produced by advanced methods such as neural networks and deep learning. However, there has been a long history of explanation generation in the general field of constraint satisfaction, one of the AI's most ubiquitous subfields. In this paper we survey the major seminal papers on the explanation and constraints, as well as some more recent works. The survey sets out to unify many disparate lines of work in areas such as model-based diagnosis, constraint programming, Boolean satisfiability, truth maintenance systems, quantified logics, and related areas.

scholarly journals Thesis on Machine Learning Methods and Its Applications

2021 ◽  
Vol 9 (10) ◽  
pp. 746-757
Author(s):  
Dharmapriya M S
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Methods ◽  
Processing Data ◽  
History Of ◽  
Learning Research ◽  
Learning Machine

Abstract: In the 1950s, the concept of machine learning was discovered and developed as a subfield of artificial intelligence. However, there were no significant developments or research on it until this decade. Typically, this field of study has developed and expanded since the 1990s. It is a field that will continue to develop in the future due to the difficulty of analysing and processing data as the number of records and documents increases. Due to the increasing data, machine learning focuses on finding the best model for the new data that takes into account all the previous data. Therefore, machine learning research will continue in correlation with this increasing data. This research focuses on the history of machine learning, the methods of machine learning, its applications, and the research that has been conducted on this topic. Our study aims to give researchers a deeper understanding of machine learning, an area of research that is becoming much more popular today, and its applications. Keywords: Machine Learning, Machine Learning Algorithms, Artificial Intelligence, Big Data.

Explainable artificial intelligence for education and training

2021 ◽  
pp. 154851292110286
Author(s):  
Krzysztof Fiok ◽  
Farzad V Farahani ◽  
Waldemar Karwowski ◽  
Tareq Ahram
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Black Box ◽  
Target User ◽  
Explainable Artificial Intelligence ◽  
Explainable Ai ◽  
History Of ◽  
User Groups ◽  
And Training ◽  
Intelligent Action

Researchers and software users benefit from the rapid growth of artificial intelligence (AI) to an unprecedented extent in various domains where automated intelligent action is required. However, as they continue to engage with AI, they also begin to understand the limitations and risks associated with ceding control and decision-making to not always transparent artificial computer agents. Understanding of “what is happening in the black box” becomes feasible with explainable AI (XAI) methods designed to mitigate these risks and introduce trust into human-AI interactions. Our study reviews the essential capabilities, limitations, and desiderata of XAI tools developed over recent years and reviews the history of XAI and AI in education (AIED). We present different approaches to AI and XAI from the viewpoint of researchers focused on AIED in comparison with researchers focused on AI and machine learning (ML). We conclude that both groups of interest desire increased efforts to obtain improved XAI tools; however, these groups formulate different target user groups and expectations regarding XAI features and provide different examples of possible achievements. We summarize these viewpoints and provide guidelines for scientists looking to incorporate XAI into their own work.

scholarly journals Building Resilience against COVID-19 Pandemic Using Artificial Intelligence, Machine Learning, and IoT: A Survey of Recent Progress

IoT ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 506-528
Author(s):  
S. M. Abu Adnan Abir ◽  
Shama Naz Islam ◽  
Adnan Anwar ◽  
Abdun Naser Mahmood ◽  
Aman Maung Than Oo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Critical Discussion ◽  
Disease Spread ◽  
Treatment Practices ◽  
Causes Of Disease ◽  
Potential Applications ◽  
Life Threatening ◽  
History Of ◽  
Tools And Techniques

Coronavirus disease 2019 (COVID-19) has significantly impacted the entire world today and stalled off regular human activities in such an unprecedented way that it will have an unforgettable footprint on the history of mankind. Different countries have adopted numerous measures to build resilience against this life-threatening disease. However, the highly contagious nature of this pandemic has challenged the traditional healthcare and treatment practices. Thus, artificial intelligence (AI) and machine learning (ML) open up new mechanisms for effective healthcare during this pandemic. AI and ML can be useful for medicine development, designing efficient diagnosis strategies and producing predictions of the disease spread. These applications are highly dependent on real-time monitoring of the patients and effective coordination of the information, where the Internet of Things (IoT) plays a key role. IoT can also help with applications such as automated drug delivery, responding to patient queries, and tracking the causes of disease spread. This paper represents a comprehensive analysis of the potential AI, ML, and IoT technologies for defending against the COVID-19 pandemic. The existing and potential applications of AI, ML, and IoT, along with a detailed analysis of the enabling tools and techniques are outlined. A critical discussion on the risks and limitations of the aforementioned technologies are also included.

Work and Technological Change

2020 ◽  
Author(s):  
Stephen R. Barley
Keyword(s):  
Artificial Intelligence ◽  
Historical Context ◽  
Energy Transformation ◽  
History Of ◽  
Work And Organizations ◽  
Role Based ◽  
Control Technologies ◽  
And Control ◽  
Work And Employment

The four chapters of this book summarize the results of thirty-five years dedicated to studying how technologies change work and organizations. The first chapter places current developments in artificial intelligence into the historical context of previous technological revolutions by drawing on William Faunce’s argument that the history of technology is one of progressive automation of the four components of any production system: energy, transformation, and transfer and control technologies. The second chapter lays out a role-based theory of how technologies occasion changes in organizations. The third chapter tackles the issue of how to conceptualize a more thorough approach to assessing how intelligent technologies, such as artificial intelligence, can shape work and employment. The fourth chapter discusses what has been learned over the years about the fears that arise when one sets out to study technical work and technical workers and methods for controlling those fears.

AI Narratives

2020 ◽  
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Ancient Greece ◽  
Historical Overview ◽  
Intelligent Machines ◽  
Narrative History ◽  
History Of ◽  
Imaginative Thinking ◽  
The Relationship ◽  
Insight Into

This book is the first to examine the history of imaginative thinking about intelligent machines. As real artificial intelligence (AI) begins to touch on all aspects of our lives, this long narrative history shapes how the technology is developed, deployed, and regulated. It is therefore a crucial social and ethical issue. Part I of this book provides a historical overview from ancient Greece to the start of modernity. These chapters explore the revealing prehistory of key concerns of contemporary AI discourse, from the nature of mind and creativity to issues of power and rights, from the tension between fascination and ambivalence to investigations into artificial voices and technophobia. Part II focuses on the twentieth and twenty-first centuries in which a greater density of narratives emerged alongside rapid developments in AI technology. These chapters reveal not only how AI narratives have consistently been entangled with the emergence of real robotics and AI, but also how they offer a rich source of insight into how we might live with these revolutionary machines. Through their close textual engagements, these chapters explore the relationship between imaginative narratives and contemporary debates about AI’s social, ethical, and philosophical consequences, including questions of dehumanization, automation, anthropomorphization, cybernetics, cyberpunk, immortality, slavery, and governance. The contributions, from leading humanities and social science scholars, show that narratives about AI offer a crucial epistemic site for exploring contemporary debates about these powerful new technologies.

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