A Behavioral Economics Approach to Digitalization

2019 ◽  
pp. 107-122
Author(s):  
Dirk Beerbaum ◽  
Julia Margarete Puaschunder
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Social Media ◽  
Behavioral Economics ◽  
Field Research ◽  
Learning Systems ◽  
Business Reporting ◽  
Intelligent Machines ◽  
Private Investors ◽  
Coding Rules

Technological improvement in the age of information has increased the possibilities to control the innocent social media users or penalize private investors and reap the benefits of their existence in hidden persuasion and discrimination. This chapter takes as a case the transparency technology XBRL (eXtensible Business Reporting Language), which should make data more accessible as well as usable for private investors. Considering theoretical literature and field research, a representation issue for principles-based accounting taxonomies exists, which intelligent machines applying artificial intelligence (AI) nudge to facilitate decision usefulness. This chapter conceptualizes ethical questions arising from the taxonomy engineering based on machine learning systems and advocates for a democratization of information, education, and transparency about nudges and coding rules.

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 Reports on the 2017 AAAI Spring Symposium Series

AI Magazine ◽  
2017 ◽  
Vol 38 (4) ◽  
pp. 99-106
Author(s):  
Jeannette Bohg ◽  
Xavier Boix ◽  
Nancy Chang ◽  
Elizabeth F. Churchill ◽  
Vivian Chu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Science ◽  
User Experience ◽  
Object Perception ◽  
Learning Systems ◽  
Language Understanding ◽  
Social Good ◽  
Symposium Series ◽  
The Social

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2017 Spring Symposium Series, held Monday through Wednesday, March 27–29, 2017 on the campus of Stanford University. The eight symposia held were Artificial Intelligence for the Social Good (SS-17-01); Computational Construction Grammar and Natural Language Understanding (SS-17-02); Computational Context: Why It's Important, What It Means, and Can It Be Computed? (SS-17-03); Designing the User Experience of Machine Learning Systems (SS-17-04); Interactive Multisensory Object Perception for Embodied Agents (SS-17-05); Learning from Observation of Humans (SS-17-06); Science of Intelligence: Computational Principles of Natural and Artificial Intelligence (SS-17-07); and Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing (SS-17-08). This report, compiled from organizers of the symposia, summarizes the research that took place.

scholarly journals Automating Cyber Attacks

2020 ◽  
Author(s):  
Ben Buchanan ◽  
John Bansemer ◽  
Dakota Cary ◽  
Jack Lucas ◽  
Micah Musser
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cyber Attacks ◽  
Learning Systems ◽  
Depth Analysis ◽  
The Future

Based on an in-depth analysis of artificial intelligence and machine learning systems, the authors consider the future of applying such systems to cyber attacks, and what strategies attackers are likely or less likely to use. As nuanced, complex, and overhyped as machine learning is, they argue, it remains too important to ignore.

ARTIFICIAL INTELLIGENCE AND THE DEVELOPMENT OF EFFICIENT LEGAL TECHNOLOGY

2021 ◽  
pp. 41-48
Author(s):  
T.V. Zakharov ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Law Enforcement ◽  
Learning Systems ◽  
Legal Practice ◽  
Regulatory Law ◽  
Data Objects ◽  
Legal Technology

The review analyzes some publications of foreign researchers concerning the effectiveness of new legal technologies in legal practice and the work of lawyers, the construction of AI systems administration, the ability of machine learning systems to operate with texts of regulatory, law enforcement and other legal acts as data objects, the development of AI training mechanisms and conditions for transparency of its conclusions and decisions.

scholarly journals Artificial intelligence in architecture: Generating conceptual design via deep learning

2018 ◽  
Vol 16 (4) ◽  
pp. 306-327 ◽  
Author(s):  
Imdat As ◽  
Siddharth Pal ◽  
Prithwish Basu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Conceptual Design ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Field Research ◽  
Building Blocks ◽  
Learning System ◽  
Generative Adversarial Networks

Artificial intelligence, and in particular machine learning, is a fast-emerging field. Research on artificial intelligence focuses mainly on image-, text- and voice-based applications, leading to breakthrough developments in self-driving cars, voice recognition algorithms and recommendation systems. In this article, we present the research of an alternative graph-based machine learning system that deals with three-dimensional space, which is more structured and combinatorial than images, text or voice. Specifically, we present a function-driven deep learning approach to generate conceptual design. We trained and used deep neural networks to evaluate existing designs encoded as graphs, extract significant building blocks as subgraphs and merge them into new compositions. Finally, we explored the application of generative adversarial networks to generate entirely new and unique designs.

Machine reengineering: robots and people working smarter together

2017 ◽  
Vol 45 (6) ◽  
pp. 50-54 ◽  
Author(s):  
Prashant Shukla ◽  
H. James Wilson ◽  
Allan Alter ◽  
David Lavieri
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Design Methodology ◽  
It Professionals ◽  
Content Type ◽  
Public Agency ◽  
Intelligent Machines ◽  
Intelligent Machine ◽  
Working Together ◽  
Practical Implications

Purpose The authors explore the potential of machine learning, computers employ that an algorithm to sort data, make decisions and then continuously assess and improve their functionality. They suggest that it be used to power a radical redesign of company processes that they call machine reengineering. Design/methodology/approach The authors interpret a survey of more than a thousand corporate public agency IT professionals on their use of artificial intelligence and machine learning. Findings Companies that embrace machine learning find that it adds value to the work product of their employees and provides companies with new capabilities. Practical implications Working together with an intelligent machine, workers become custodians of powerfully smart tools, tools that personalize work to maximize their most productive ways of working. Originality/value A guide to establishing a culture that empowers employees to thrive alongside intelligent machines.

scholarly journals Could a Conversational AI Identify Offensive Language?

Information ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 418
Author(s):  
Daniela America da Silva ◽  
Henrique Duarte Borges Louro ◽  
Gildarcio Sousa Goncalves ◽  
Johnny Cardoso Marques ◽  
Luiz Alberto Vieira Dias ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Unethical Behavior ◽  
Historical Data ◽  
Language Models ◽  
The Internet ◽  
Proof Of Concept ◽  
Offensive Language

In recent years, we have seen a wide use of Artificial Intelligence (AI) applications in the Internet and everywhere. Natural Language Processing and Machine Learning are important sub-fields of AI that have made Chatbots and Conversational AI applications possible. Those algorithms are built based on historical data in order to create language models, however historical data could be intrinsically discriminatory. This article investigates whether a Conversational AI could identify offensive language and it will show how large language models often produce quite a bit of unethical behavior because of bias in the historical data. Our low-level proof-of-concept will present the challenges to detect offensive language in social media and it will discuss some steps to propitiate strong results in the detection of offensive language and unethical behavior using a Conversational AI.

scholarly journals The AI Triad and What It Means for National Security Strategy

2020 ◽  
Author(s):  
Ben Buchanan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
National Security ◽  
Security Policy ◽  
Learning Systems ◽  
Security Strategy ◽  
National Security Policy ◽  
Computing Power ◽  
National Security Strategy

One sentence summarizes the complexities of modern artificial intelligence: Machine learning systems use computing power to execute algorithms that learn from data. This AI triad of computing power, algorithms, and data offers a framework for decision-making in national security policy.

scholarly journals Artificial Intelligence and Machine Learning: An Instructor’s Exoskeleton in the Future of Education

2021 ◽  
pp. 79-105
Author(s):  
Stephanie E. August ◽  
Audrey Tsaima
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Systems ◽  
Future Research ◽  
Technology Support ◽  
Student Knowledge ◽  
Customized Learning ◽  
Future Of Education ◽  
Authentic Learning Experiences ◽  
The Impact

AbstractThe role of artificial intelligence in US education is expanding. As education moves toward providing customized learning paths, the use of artificial intelligence (AI) and machine learning (ML) algorithms in learning systems increases. This can be viewed as growing metaphorical exoskeletons for instructors, enabling them to provide a higher level of guidance, feedback, and autonomy to learners. In turn, the instructor gains time to sense student needs and support authentic learning experiences that go beyond what AI and ML can provide. Applications of AI-based education technology support learning through automated tutoring, personalizing learning, assessing student knowledge, and automating tasks normally performed by the instructor. This technology raises questions about how it is best used, what data provides evidence of the impact of AI and ML on learning, and future directions in interactive learning systems. Exploration of the use of AI and ML for both co-curricular and independent learnings in content presentation and instruction; interactions, communications, and discussions; learner activities; assessment and evaluation; and co-curricular opportunities provide guidance for future research.

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