Identification of future signal based on the quantitative and qualitative text mining: a case study on ethical issues in artificial intelligence

2017 ◽  
Vol 52 (2) ◽  
pp. 653-667 ◽  
Author(s):  
Young-Joo Lee ◽  
Ji-Young Park
Keyword(s):  
Artificial Intelligence ◽  
Text Mining ◽  
Ethical Issues

scholarly journals Ethical Issues in Democratizing Digital Phenotypes and Machine Learning in the Next Generation of Digital Health Technologies

2021 ◽  
Author(s):  
Maurice D. Mulvenna ◽  
Raymond Bond ◽  
Jack Delaney ◽  
Fatema Mustansir Dawoodbhoy ◽  
Jennifer Boger ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Ethical Issues ◽  
Digital Health ◽  
Time Data ◽  
Digital Products ◽  
Health Technologies ◽  
Digital Phenotyping ◽  
Phenotype Data

AbstractDigital phenotyping is the term given to the capturing and use of user log data from health and wellbeing technologies used in apps and cloud-based services. This paper explores ethical issues in making use of digital phenotype data in the arena of digital health interventions. Products and services based on digital wellbeing technologies typically include mobile device apps as well as browser-based apps to a lesser extent, and can include telephony-based services, text-based chatbots, and voice-activated chatbots. Many of these digital products and services are simultaneously available across many channels in order to maximize availability for users. Digital wellbeing technologies offer useful methods for real-time data capture of the interactions of users with the products and services. It is possible to design what data are recorded, how and where it may be stored, and, crucially, how it can be analyzed to reveal individual or collective usage patterns. The paper also examines digital phenotyping workflows, before enumerating the ethical concerns pertaining to different types of digital phenotype data, highlighting ethical considerations for collection, storage, and use of the data. A case study of a digital health app is used to illustrate the ethical issues. The case study explores the issues from a perspective of data prospecting and subsequent machine learning. The ethical use of machine learning and artificial intelligence on digital phenotype data and the broader issues in democratizing machine learning and artificial intelligence for digital phenotype data are then explored in detail.

scholarly journals Research and Practice of AI Ethics: A Case Study Approach Juxtaposing Academic Discourse with Organisational Reality

2021 ◽  
Vol 27 (2) ◽  
Author(s):  
Mark Ryan ◽  
Josephina Antoniou ◽  
Laurence Brooks ◽  
Tilimbe Jiya ◽  
Kevin Macnish ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Ethical Issues ◽  
Case Study Approach ◽  
Detailed Understanding ◽  
Research And Practice ◽  
Ethical Concerns ◽  
Study Approach ◽  
Ai Ethics ◽  
Applications Of Ai

AbstractThis study investigates the ethical use of Big Data and Artificial Intelligence (AI) technologies (BD + AI)—using an empirical approach. The paper categorises the current literature and presents a multi-case study of 'on-the-ground' ethical issues that uses qualitative tools to analyse findings from ten targeted case-studies from a range of domains. The analysis coalesces identified singular ethical issues, (from the literature), into clusters to offer a comparison with the proposed classification in the literature. The results show that despite the variety of different social domains, fields, and applications of AI, there is overlap and correlation between the organisations’ ethical concerns. This more detailed understanding of ethics in AI + BD is required to ensure that the multitude of suggested ways of addressing them can be targeted and succeed in mitigating the pertinent ethical issues that are often discussed in the literature.

The Oxford Handbook of Ethics of AI

2020 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Ethical Issues ◽  
Rapid Development ◽  
Data Driven ◽  
International Human Rights Law ◽  
Human Knowledge ◽  
Human Rights Law ◽  
Ai Ethics ◽  
And Behavior ◽  
Control Complex

This book explores the intertwining domains of artificial intelligence (AI) and ethics—two highly divergent fields which at first seem to have nothing to do with one another. AI is a collection of computational methods for studying human knowledge, learning, and behavior, including by building agents able to know, learn, and behave. Ethics is a body of human knowledge—far from completely understood—that helps agents (humans today, but perhaps eventually robots and other AIs) decide how they and others should behave. Despite these differences, however, the rapid development in AI technology today has led to a growing number of ethical issues in a multitude of fields, ranging from disciplines as far-reaching as international human rights law to issues as intimate as personal identity and sexuality. In fact, the number and variety of topics in this volume illustrate the width, diversity of content, and at times exasperating vagueness of the boundaries of “AI Ethics” as a domain of inquiry. Within this discourse, the book points to the capacity of sociotechnical systems that utilize data-driven algorithms to classify, to make decisions, and to control complex systems. Given the wide-reaching and often intimate impact these AI systems have on daily human lives, this volume attempts to address the increasingly complicated relations between humanity and artificial intelligence. It considers not only how humanity must conduct themselves toward AI but also how AI must behave toward humanity.

Ethics of Artificial Intelligence in Transport

2020 ◽  
pp. 667-683
Author(s):  
Bryant Walker Smith
Keyword(s):  
Artificial Intelligence ◽  
Ethical Issues ◽  
Ethical Question ◽  
Power Dynamics ◽  
Automated Vehicles ◽  
Automated Driving ◽  
The U.S ◽  
Policy Solutions

This chapter highlights key ethical issues in the use of artificial intelligence in transport by using automated driving as an example. These issues include the tension between technological solutions and policy solutions; the consequences of safety expectations; the complex choice between human authority and computer authority; and power dynamics among individuals, governments, and companies. In 2017 and 2018, the U.S. Congress considered automated driving legislation that was generally supported by many of the larger automated-driving developers. However, this automated-driving legislation failed to pass because of a lack of trust in technologies and institutions. Trustworthiness is much more of an ethical question. Automated vehicles will not be driven by individuals or even by computers; they will be driven by companies acting through their human and machine agents. An essential issue for this field—and for artificial intelligence generally—is how the companies that develop and deploy these technologies should earn people’s trust.

Prohibition, Legalization, and Political Consumerism

2018 ◽  
pp. 740-769 ◽  
Author(s):  
Elizabeth A. Bennett
Keyword(s):  
Social Movements ◽  
Legal Status ◽  
Ethical Issues ◽  
Drug Control ◽  
Psychoactive Substance ◽  
Political Consumerism ◽  
Control Laws ◽  
The Past ◽  
The Us

Cannabis (marijuana) is the most commonly consumed, universally produced, and frequently trafficked psychoactive substance prohibited under international drug control laws. Yet, several countries have recently moved toward legalization. In these places, the legal status of cannabis is complex, especially because illegal markets persist. This chapter explores the ways in which a sector’s legal status interacts with political consumerism. The analysis draws on a case study of political consumerism in the US and Canadian cannabis markets over the past two decades as both countries moved toward legalization. It finds that the goals, tactics, and leadership of political consumerism activities changed as the sector’s legal status shifted. Thus prohibition, semilegalization, and new legality may present special challenges to political consumerism, such as silencing producers, confusing consumers, deterring social movements, and discouraging discourse about ethical issues. The chapter concludes that political consumerism and legal status may have deep import for one another.

scholarly journals Does higher education properly prepare graduates for the growing artificial intelligence market? Gaps identification using text mining

2021 ◽  
pp. 1-13
Author(s):  
Lamiae Benhayoun ◽  
Daniel Lang
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Academic Training ◽  
Market Requirements ◽  
Job Advertisements ◽  
The Individual

BACKGROUND: The renewed advent of Artificial Intelligence (AI) is inducing profound changes in the classic categories of technology professions and is creating the need for new specific skills. OBJECTIVE: Identify the gaps in terms of skills between academic training on AI in French engineering and Business Schools, and the requirements of the labour market. METHOD: Extraction of AI training contents from the schools’ websites and scraping of a job advertisements’ website. Then, analysis based on a text mining approach with a Python code for Natural Language Processing. RESULTS: Categorization of occupations related to AI. Characterization of three classes of skills for the AI market: Technical, Soft and Interdisciplinary. Skills’ gaps concern some professional certifications and the mastery of specific tools, research abilities, and awareness of ethical and regulatory dimensions of AI. CONCLUSIONS: A deep analysis using algorithms for Natural Language Processing. Results that provide a better understanding of the AI capability components at the individual and the organizational levels. A study that can help shape educational programs to respond to the AI market requirements.

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