scholarly journals Classification of Explainable Artificial Intelligence Methods through Their Output Formats

2021 ◽  
Vol 3 (3) ◽  
pp. 615-661
Author(s):  
Giulia Vilone ◽  
Luca Longo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Classification System ◽  
Hierarchical Classification ◽  
Future Research ◽  
Explainable Artificial Intelligence ◽  
Scientific Papers ◽  
The Many ◽  
Application Fields ◽  
Future Work

Machine and deep learning have proven their utility to generate data-driven models with high accuracy and precision. However, their non-linear, complex structures are often difficult to interpret. Consequently, many scholars have developed a plethora of methods to explain their functioning and the logic of their inferences. This systematic review aimed to organise these methods into a hierarchical classification system that builds upon and extends existing taxonomies by adding a significant dimension—the output formats. The reviewed scientific papers were retrieved by conducting an initial search on Google Scholar with the keywords “explainable artificial intelligence”; “explainable machine learning”; and “interpretable machine learning”. A subsequent iterative search was carried out by checking the bibliography of these articles. The addition of the dimension of the explanation format makes the proposed classification system a practical tool for scholars, supporting them to select the most suitable type of explanation format for the problem at hand. Given the wide variety of challenges faced by researchers, the existing XAI methods provide several solutions to meet the requirements that differ considerably between the users, problems and application fields of artificial intelligence (AI). The task of identifying the most appropriate explanation can be daunting, thus the need for a classification system that helps with the selection of methods. This work concludes by critically identifying the limitations of the formats of explanations and by providing recommendations and possible future research directions on how to build a more generally applicable XAI method. Future work should be flexible enough to meet the many requirements posed by the widespread use of AI in several fields, and the new regulations.

scholarly journals Automated Reasoning for Explainable Artificial Intelligence

10.29007/4b7h ◽  
2018 ◽  
Author(s):  
Maria Paola Bonacina
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Automated Reasoning ◽  
Position Statement ◽  
Short Paper ◽  
Future Research ◽  
Grand Challenge ◽  
New Paradigm ◽  
Research Areas ◽  
Explainable Artificial Intelligence

Reasoning and learning have been considered fundamental features of intelligence ever since the dawn of the field of artificial intelligence, leading to the development of the research areas of automated reasoning and machine learning. This short paper is a non-technical position statement that aims at prompting a discussion of the relationship between automated reasoning and machine learning, and more generally between automated reasoning and artificial intelligence. We suggest that the emergence of the new paradigm of XAI, that stands for eXplainable Artificial Intelligence, is an opportunity for rethinking these relationships, and that XAI may offer a grand challenge for future research on automated reasoning.

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.

scholarly journals Software Testing: Issues and Challenges of Artificial Intelligence & Machine Learning

2021 ◽  
Vol 12 (1) ◽  
pp. 101-112
Author(s):  
Kishore Sugali ◽  
Chris Sprunger ◽  
Venkata N Inukollu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Software Testing ◽  
Future Research ◽  
Critical Component ◽  
Development Methodology ◽  
Testing Strategies ◽  
History Of ◽  
Shed Light ◽  
The Way

The history of Artificial Intelligence and Machine Learning dates back to 1950’s. In recent years, there has been an increase in popularity for applications that implement AI and ML technology. As with traditional development, software testing is a critical component of an efficient AI/ML application. However, the approach to development methodology used in AI/ML varies significantly from traditional development. Owing to these variations, numerous software testing challenges occur. This paper aims to recognize and to explain some of the biggest challenges that software testers face in dealing with AI/ML applications. For future research, this study has key implications. Each of the challenges outlined in this paper is ideal for further investigation and has great potential to shed light on the way to more productive software testing strategies and methodologies that can be applied to AI/ML applications.

An artificial intelligence-enabled industry classification and its interpretation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Daejin Kim ◽  
Hyoung-Goo Kang ◽  
Kyounghun Bae ◽  
Seongmin Jeon
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Classification System ◽  
Standard Industrial Classification ◽  
American Industry ◽  
Content Type ◽  
Global Industry ◽  
Machine Learning Methods ◽  
Industry Classification ◽  
Traditional Industry

PurposeTo overcome the shortcomings of traditional industry classification systems such as the Standard Industrial Classification Standard Industrial Classification, North American Industry Classification System North American Industry Classification System, and Global Industry Classification Standard Global Industry Classification Standard, the authors explore industry classifications using machine learning methods as an application of interpretable artificial intelligence (AI).Design/methodology/approachThe authors propose a text-based industry classification combined with a machine learning technique by extracting distinguishable features from business descriptions in financial reports. The proposed method can reduce the dimensions of word vectors to avoid the curse of dimensionality when measuring the similarities of firms.FindingsUsing the proposed method, the sample firms form clusters of distinctive industries, thus overcoming the limitations of existing classifications. The method also clarifies industry boundaries based on lower-dimensional information. The graphical closeness between industries can reflect the industry-level relationship as well as the closeness between individual firms.Originality/valueThe authors’ work contributes to the industry classification literature by empirically investigating the effectiveness of machine learning methods. The text mining method resolves issues concerning the timeliness of traditional industry classifications by capturing new information in annual reports. In addition, the authors’ approach can solve the computing concerns of high dimensionality.

Essential Features and Critical Issues With Educational Chatbots

2021 ◽  
pp. 246-262
Author(s):  
Jeremy Riel
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Formal Learning ◽  
Automated Systems ◽  
Conversational Agents ◽  
Teacher Instruction ◽  
Practical Applications ◽  
Significant Potential ◽  
Critical Issues ◽  
Future Work

Conversational agents, also known as chatbots, are automated systems for engaging in two-way dialogue with human users. These systems have existed in one form or another for at least 60 years but have recently demonstrated significant potential with advances in machine learning and artificial intelligence technologies. The use of conversational agents or chatbots for education can potentially reduce costs and supplement teacher instruction in transformative ways for formal learning. This chapter examines the design and status of chatbots and conversational agents for educational purposes. Common design functions and goals of educational chatbots are described, along with current practical applications of chatbots for educational purposes. Finally, this chapter considers issues about pedagogical commitments, ethics, and equity to suggest future work in the field.

scholarly journals A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

2020 ◽  
Vol 12 (9) ◽  
pp. 3760 ◽  
Author(s):  
Manuel Woschank ◽  
Erwin Rauch ◽  
Helmut Zsifkovits
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Scientific Literature ◽  
Future Research ◽  
Industrial Enterprises ◽  
Smart Logistics ◽  
Research Findings ◽  
Ongoing Development ◽  
Research Initiatives

Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

scholarly journals How to translate artificial intelligence? Myths and justifications in public discourse

2020 ◽  
Vol 7 (1) ◽  
pp. 205395172091996 ◽  
Author(s):  
Jonathan Roberge ◽  
Marius Senneville ◽  
Kevin Morin
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Science And Technology Studies ◽  
Public Discourse ◽  
Social Expectations ◽  
Specific Language ◽  
The Public ◽  
Gradual Introduction ◽  
Congressional Hearing ◽  
The Many

Automated technologies populating today’s online world rely on social expectations about how “smart” they appear to be. Algorithmic processing, as well as bias and missteps in the course of their development, all come to shape a cultural realm that in turn determines what they come to be about. It is our contention that a robust analytical frame could be derived from culturally driven Science and Technology Studies while focusing on Callon’s concept of translation. Excitement and apprehensions must find a specific language to move past a state of latency. Translations are thus contextual and highly performative, transforming justifications into legitimate claims, translators into discursive entrepreneurs, and power relations into new forms of governance and governmentality. In this piece, we discuss three cases in which artificial intelligence was deciphered to the public: (i) the Montreal Declaration for a Responsible Development of Artificial Intelligence, held as a prime example of how stakeholders manage to establish the terms of the debate on ethical artificial intelligence while avoiding substantive commitment; (ii) Mark Zuckerberg’s 2018 congressional hearing, where he construed machine learning as the solution to the many problems the platform might encounter; and (iii) the normative renegotiations surrounding the gradual introduction of “killer robots” in military engagements. Of interest are not only the rational arguments put forward, but also the rhetorical maneuvers deployed. Through the examination of the ramifications of these translations, we intend to show how they are constructed in face of and in relation to forms of criticisms, thus revealing the highly cybernetic deployment of artificial intelligence technologies.

Artificial Intelligence, Machine Learning, Automation, Robotics, Future of Work and Future of Humanity

2019 ◽  
Vol 30 (1) ◽  
pp. 61-79 ◽  
Author(s):  
Weiyu Wang ◽  
Keng Siau
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Industrial Revolution ◽  
Future Research ◽  
Way Of Life ◽  
Negative Effects ◽  
Future Research Directions ◽  
Negative Impacts ◽  
The Impact ◽  
The Way

The exponential advancement in artificial intelligence (AI), machine learning, robotics, and automation are rapidly transforming industries and societies across the world. The way we work, the way we live, and the way we interact with others are expected to be transformed at a speed and scale beyond anything we have observed in human history. This new industrial revolution is expected, on one hand, to enhance and improve our lives and societies. On the other hand, it has the potential to cause major upheavals in our way of life and our societal norms. The window of opportunity to understand the impact of these technologies and to preempt their negative effects is closing rapidly. Humanity needs to be proactive, rather than reactive, in managing this new industrial revolution. This article looks at the promises, challenges, and future research directions of these transformative technologies. Not only are the technological aspects investigated, but behavioral, societal, policy, and governance issues are reviewed as well. This research contributes to the ongoing discussions and debates about AI, automation, machine learning, and robotics. It is hoped that this article will heighten awareness of the importance of understanding these disruptive technologies as a basis for formulating policies and regulations that can maximize the benefits of these advancements for humanity and, at the same time, curtail potential dangers and negative impacts.

scholarly journals Application of Machine Learning in Auditing Teaching: A Case Study of Predicting the Audit Report Type of China ST Listed Companies

2020 ◽  
Vol 2 (1) ◽  
pp. 023-040
Author(s):  
Shi-Ming Huang Shi-Ming Huang ◽  
Chang-ping Chen Shi-Ming Huang ◽  
Tzu-ching Wong Chang-ping Chen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Listed Company ◽  
Accounting Students ◽  
Computer Assisted ◽  
Research Database ◽  
Audit Report ◽  
Professional Fields ◽  
Future Work

<p>Artificial intelligence is an important emerging technology in the accounting industry. Fear and hype associated with artificial intelligence and its impact on accounting and auditing jobs have pervaded the professional fields of accounting and auditing. It is important to develop AI competency in accountants and auditors. This paper presents a teaching case for a professor or lecturer to use for teaching machine learning to accounting students. The case is based on openly available data from the China Stock Market & Accounting Research database and aims to teach students how to predict the future audit report type of a China ST listed company. Through case teaching, students can learn skills related to computer-assisted auditing tools and machine learning (such as ACL) develop the confidence to apply artificial intelligence in their education and future work.</p> <p>&nbsp;</p>

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