A Multi-Disciplinary Perspective for Conducting Artificial Intelligence-enabled Privacy Analytics

2021 ◽  
Vol 12 (1) ◽  
pp. 1-18
Author(s):  
Sagar Samtani ◽  
Murat Kantarcioglu ◽  
Hsinchun Chen
Keyword(s):  
Artificial Intelligence ◽  
Web Sites ◽  
Data Aggregation ◽  
National Science Foundation ◽  
Modern Society ◽  
Data Sources ◽  
Risk Modeling ◽  
Legal Documents ◽  
Funding Sources ◽  
Assistance Systems

Events such as Facebook-Cambridge Analytica scandal and data aggregation efforts by technology providers have illustrated how fragile modern society is to privacy violations. Internationally recognized entities such as the National Science Foundation (NSF) have indicated that Artificial Intelligence (AI)-enabled models, artifacts, and systems can efficiently and effectively sift through large quantities of data from legal documents, social media, Dark Web sites, and other sources to curb privacy violations. Yet considerable efforts are still required for understanding prevailing data sources, systematically developing AI-enabled privacy analytics to tackle emerging challenges, and deploying systems to address critical privacy needs. To this end, we provide an overview of prevailing data sources that can support AI-enabled privacy analytics; a multi-disciplinary research framework that connects data, algorithms, and systems to tackle emerging AI-enabled privacy analytics challenges such as entity resolution, privacy assistance systems, privacy risk modeling, and more; a summary of selected funding sources to support high-impact privacy analytics research; and an overview of prevailing conference and journal venues that can be leveraged to share and archive privacy analytics research. We conclude this paper with an introduction of the papers included in this special issue.

scholarly journals Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys

2021 ◽  
Vol 13 (11) ◽  
pp. 6038
Author(s):  
Sergio Alonso ◽  
Rosana Montes ◽  
Daniel Molina ◽  
Iván Palomares ◽  
Eugenio Martínez-Cámara ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Sustainable Development Goals ◽  
Modern Society ◽  
Agenda 2030 ◽  
Model Based ◽  
Development Goals ◽  
Sustainability Challenges ◽  
New Research ◽  
Decision Making Model

The United Nations Agenda 2030 established 17 Sustainable Development Goals (SDGs) as a guideline to guarantee a sustainable worldwide development. Recent advances in artificial intelligence and other digital technologies have already changed several areas of modern society, and they could be very useful to reach these sustainable goals. In this paper we propose a novel decision making model based on surveys that ranks recommendations on the use of different artificial intelligence and related technologies to achieve the SDGs. According to the surveys, our decision making method is able to determine which of these technologies are worth investing in to lead new research to successfully tackle with sustainability challenges.

scholarly journals ARTIFICIAL INTELLIGENCE: NEW CHALLENGES AND PROSPECTS

2020 ◽  
Vol 8 (5) ◽  
pp. 42-48
Author(s):  
Yulia Matyuk
Keyword(s):  
Artificial Intelligence ◽  
Industrial Revolution ◽  
New Technologies ◽  
Rapid Development ◽  
Modern Society ◽  
Fourth Industrial Revolution ◽  
New Challenges ◽  
And Robotics

The article analyzes the risks and new opportunities that arise before man and modern society in the light of the development of artificial intelligence and robotics in the conditions of the fourth industrial revolution. The rapid development of AI indicates the absence of uniform approaches to assessing the risks and prospects associated with the use of AI. Using PESTEL analysis, the article examines the key areas of interaction between AI and humans, new challenges and prospects that open to humanity in the era of new technologies.

Quantifying human resource management: a literature review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Clotilde Coron
Keyword(s):  
Artificial Intelligence ◽  
Resource Management ◽  
Literature Review ◽  
Human Resource Management ◽  
Human Resource ◽  
Design Methodology ◽  
Theoretical Framework ◽  
Data Sources ◽  
Content Type

PurposeWith a focus on the evolution of human resource management (HRM) quantification over 2000–2020, this study addresses the following questions: (1) What are the data sources used to quantify HRM? (2) What are the methods used to quantify HRM? (3) What are the objectives of HRM quantification? (4) What are the representations of quantification in HRM?Design/methodology/approachThis study is based on an integrative synthesis of 94 published peer-reviewed empirical and non-empirical articles on the use of quantification in HRM. It uses the theoretical framework of the sociology of quantification.FindingsThe analysis shows that there have been several changes in HRM quantification over 2000–2020 in terms of data sources, methods and objectives. Meanwhile, representations of quantification have evolved relatively little; it is still considered as a tool, and this ignores the possible conflicts and subjectivity associated with the use of quantification.Originality/valueThis literature review addresses the use of quantification in HRM in general and is thus larger in scope than previous reviews. Notably, it brings forth new insights on possible differences between the main uses of quantification in HRM, as well as on artificial intelligence and algorithms in HRM.

scholarly journals Reports of the AAAI 2011 Conference Workshops

AI Magazine ◽  
2012 ◽  
Vol 33 (1) ◽  
pp. 57-70
Author(s):  
Noa Agmon ◽  
Vikas Agrawal ◽  
David W. Aha ◽  
Yiannis Aloimonos ◽  
Donagh Buckley ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
San Francisco ◽  
Computational Models ◽  
Action Planning ◽  
Human Robot Interaction ◽  
Human Computation ◽  
Risk Modeling ◽  
Learning Plan ◽  
Wide Range ◽  
Representation Techniques

The AAAI-11 workshop program was held Sunday and Monday, August 7–18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages; Analyzing Microtext; Applied Adversarial Reasoning and Risk Modeling; Artificial Intelligence and Smarter Living: The Conquest of Complexity; AI for Data Center Management and Cloud Computing; Automated Action Planning for Autonomous Mobile Robots; Computational Models of Natural Argument; Generalized Planning; Human Computation; Human-Robot Interaction in Elder Care; Interactive Decision Theory and Game Theory; Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action and Language; Lifelong Learning; Plan, Activity, and Intent Recognition; and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events.

Role of artificial intelligence in operations environment: a review and bibliometric analysis

2020 ◽  
Vol 32 (4) ◽  
pp. 869-896 ◽  
Author(s):  
Pavitra Dhamija ◽  
Surajit Bag
Keyword(s):  
Artificial Intelligence ◽  
Bibliometric Analysis ◽  
Operations Management ◽  
Big Data Analytics ◽  
Unique Contribution ◽  
Content Type ◽  
Engineering Research ◽  
Sustainable Supply Chains ◽  
Funding Sources ◽  
Citation Statistics

Purpose“Technological intelligence” is the capacity to appreciate and adapt technological advancements, and “artificial intelligence” is the key to achieve persuasive operational transformations in majority of contemporary organizational set-ups. Implicitly, artificial intelligence (the philosophies of machines to think, behave and perform either same or similar to humans) has knocked the doors of business organizations as an imperative activity. Artificial intelligence, as a discipline, initiated by scientist John McCarthy and formally publicized at Dartmouth Conference in 1956, now occupies a central stage for many organizations. Implementation of artificial intelligence provides competitive edge to an organization with a definite augmentation in its social and corporate status. Mere application of a concept will not furnish real output until and unless its performance is reviewed systematically. Technological changes are dynamic and advancing at a rapid rate. Subsequently, it becomes highly crucial to understand that where have the people reached with respect to artificial intelligence research. The present article aims to review significant work by eminent researchers towards artificial intelligence in the form of top contributing universities, authors, keywords, funding sources, journals and citation statistics.Design/methodology/approachAs rightly remarked by past researchers that reviewing is learning from experience, research team has reviewed (by applying systematic literature review through bibliometric analysis) the concept of artificial intelligence in this article. A sum of 1,854 articles extracted from Scopus database for the year 2018–2019 (31st of May) with selected keywords (artificial intelligence, genetic algorithms, agent-based systems, expert systems, big data analytics and operations management) along with certain filters (subject–business, management and accounting; language-English; document–article, article in press, review articles and source-journals).FindingsResults obtained from cluster analysis focus on predominant themes for present as well as future researchers in the area of artificial intelligence. Emerged clusters include Cluster 1: Artificial Intelligence and Optimization; Cluster 2: Industrial Engineering/Research and Automation; Cluster 3: Operational Performance and Machine Learning; Cluster 4: Sustainable Supply Chains and Sustainable Development; Cluster 5: Technology Adoption and Green Supply Chain Management and Cluster 6: Internet of Things and Reverse Logistics.Originality/valueThe result of review of selected studies is in itself a unique contribution and a food for thought for operations managers and policy makers.

scholarly journals Artificial Intelligence and Integrated Genotype–Phenotype Identification

Genes ◽  
2018 ◽  
Vol 10 (1) ◽  
pp. 18 ◽  
Author(s):  
Lewis Frey
Keyword(s):  
Artificial Intelligence ◽  
Data Sources ◽  
The Future ◽  
Accelerated Growth ◽  
Phenotype Identification

The integration of phenotypes and genotypes is at an unprecedented level and offers new opportunities to establish deep phenotypes. There are a number of challenges to overcome, specifically, accelerated growth of data, data silos, incompleteness, inaccuracies, and heterogeneity within and across data sources. This perspective report discusses artificial intelligence (AI) approaches that hold promise in addressing these challenges by automating computable phenotypes and integrating them with genotypes. Collaborations between biomedical and AI researchers will be highlighted in order to describe initial successes with an eye toward the future.

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