Chinese, Western surveillance tech exports will differ

2019 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Facial Recognition ◽  
Ethical Standards ◽  
Surveillance Systems ◽  
Content Type ◽  
Surveillance Technology ◽  
Risk And Benefit ◽  
Recognition Software

Subject Exports of surveillance technology. Significance Surveillance tools are increasingly driven by underlying artificial intelligence (AI) systems, which (opaquely) process big data to predict events and outcomes. This is both the risk and benefit of AI: it saves time while potentially skewing the data unbeknown to -- or at the direction of -- its operators, creating opportunities for misuse. With Chinese and Western firms competing for the same security projects, the differences in the underlying ethics of vendors and their systems are under a spotlight. Impacts Facial recognition software will be the most publicly divisive surveillance technology. Ethical standards for surveillance systems will emerge gradually. However, not all Western vendors will adopt them.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

scholarly journals Preface

2021 ◽  
Vol 2050 (1) ◽  
pp. 011001
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Industrial Applications ◽  
Program Committee ◽  
Technical Session ◽  
Technical Program ◽  
Content Type ◽  
The World ◽  
Travel Restrictions ◽  
Virtual Conference

Considering the current situation of COVID-19 and travel restrictions, the 3rd International Conference on Industrial Applications of Big Data and Artificial Intelligence (BDAI 2021) which was planned to be held in Wuhan. China from Sept. 23 to 25, 2021 was changed into virtual conference on Sept. 24, 2021 via Tencent Meeting (Voov) software. BDAI 2021 was organized by China University of Geosciences (Wuhan), sponsored by Hong Kong Society of Mechanical Engineers (HKSME). The Technical Program committee received a total of 38 paper submissions from all over the world, among which 20 papers were accepted, and more than 30 participants attended the conference online, they were from China, Australia, Thailand, Malaysia, India, Japan, UK and more. Four renowned speakers given speeches about their latest research and reports. They are: Prof. Dan Zhang from York University, Canada; Prof. Lefei Zhang from Wuhan University. China: Prof. Deze Zeng from China University of Geosciences (Wuhan), China and Assoc. Prof. Simon James Fong from University of Macau. Macau S.A.R., China. The conference also had 1 technical session and 1 poster sessions. This conference aims to provide a platform for researchers and engineers to share their ideas, recent developments, and successful practices in energy engineering. The participants of the conference were from almost every part of the world, with various background such as academia, industry, and well-known entrepreneurs. Each keynote speech lasted 40 minutes, and authors presentation 15 minutes. Each presentation was included with questions and answers. BDAI 2021 became an effective communication platform for all the participants over the world and unlike some that claim international reach this conference was truly international. The conference proceeding is a compilation of the accepted papers and represent an interesting outcome of the conference. This book covers 3 chapters: 1. Artificial Intelligence: 2. Big Data Technology; 3. Robot System. We would like to acknowledge all of those who supported BDAI 2021. Each individual and institutional help were very important for the success of this conference. Especially we would like to thank the committee chairs, committee members and reviewers, for their tremendous contribution in conference organization and peer review of the papers. We sincerely hope that BDAI 2021 will be a fomrn for excellent discussions that will put forward new ideas and promote collaborative research and support researchers as they take their work forward. We are sure that the proceedings will serve as an important research source of references and the knowledge, which will lead to not only scientific and engineering progress but also other new products and processes. Dan Zhang, York University, Canada

EU-US will clash on artificial intelligence regulation

2020 ◽  
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Law Enforcement ◽  
Facial Recognition ◽  
The United States ◽  
Public Authorities ◽  
Content Type ◽  
Regulatory Strategy ◽  
Innovative Potential

Significance Its guidance will probably be at odds with the approach adopted by the United States. Impacts European AI firms will criticise a tougher regulatory strategy, fearing restrictions on their innovative potential, as well as from abroad. Larger tech companies will be better able to adapt to AI regulations than their smaller competitors. Public authorities, and law enforcement especially, will push back against facial recognition bans.

How Artificial Intelligence and Big Data Are Transforming Repression

2021 ◽  
pp. 212-253
Author(s):  
Steven Feldstein
Keyword(s):  
Artificial Intelligence ◽  
Public Policy ◽  
Social Media ◽  
Big Data ◽  
Global Index ◽  
Detailed Explanation ◽  
Surveillance Technology ◽  
Recognition Systems ◽  
Data Surveillance ◽  
Big Data Technology

This chapter examines how artificial intelligence (AI) and big-data technology are reshaping repression strategies and why they are a boon for autocratic leaders. It explores two in-depth scenarios that describe potential state deploy AI and big-data techniques to accomplish political objectives. It presents a global index of AI and big-data surveillance that measures the use of these tools in 179 countries. It then presents a detailed explanation for specific types of AI and big-data surveillance: safe cities, facial recognition systems, smart policing, and social media surveillance. Subsequently, it examines China’s role in proliferating AI and big-data surveillance technology, and it reviews public policy considerations regarding use of this technology by democracies.

Consumer electronics show 2019, from connected devices and big data to artificial intelligence: implications for libraries

2019 ◽  
Vol 36 (5) ◽  
pp. 11-14
Author(s):  
Martin A. Kesselman ◽  
Wilson Esquivel
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Design Methodology ◽  
Consumer Electronics ◽  
Content Type

Purpose Report of the CES 2019 Conference Design/methodology/approach Attendance at the conference Findings Implications for libraries Originality/value Original thoughts

US tech regulation will develop locally

2019 ◽  
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Racial Discrimination ◽  
The United States ◽  
Privacy And Security ◽  
Content Type ◽  
Ethical Concerns ◽  
Advanced Technologies ◽  
Facial Identification ◽  
Recognition Software

Subject Tech regulation in the United States. Significance San Francisco’s Board of Supervisors on December 10 amended its ban on facial recognition software to allow for Apple iPhones that use facial identification for unlocking the phone. The move reflects the difficulty cities and states face in regulating artificial intelligence (AI) and other advanced technologies that have privacy and security implications: while they are legally permitted to regulate, the efficacy of localities’ reforms runs into challenges in a largely unregulated national scene. Impacts AI in policing will attract attention, since municipalities are the jurisdiction for most US police. AI adoption will accentuate ethical concerns about racial discrimination in policing as reports of bias in algorithms proliferate. AI has a low chance of becoming polarised; the tech sector has Republican and Democratic friends.

Artificial intelligence and big data in tourism: a systematic literature review

2020 ◽  
Vol 11 (2) ◽  
pp. 343-367 ◽  
Author(s):  
Dimitra Samara ◽  
Ioannis Magnisalis ◽  
Vassilios Peristeras
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
New Technologies ◽  
Recent Literature ◽  
Content Type ◽  
Analysis Methodology ◽  
Tourism Sector

Purpose This paper aims to research, identify and discuss the benefits and overall role of big data and artificial intelligence (BDAI) in the tourism sector, as this is depicted in recent literature. Design/methodology/approach A systematic literature review was conducted under the McKinsey’s Global Institute (Talwar and Koury, 2017) methodological perspective that identifies the four ways (i.e. project, produce, promote and provide) in which BDAI creates value. The authors enhanced this analysis methodology by depicting relevant challenges as well. Findings The findings imply that BDAI create value for the tourism sector through appropriately identified disseminations. The benefits of adopting BDAI strategies include increased efficiency, productivity and profitability for tourism suppliers combined with an extremely rich and personalized experience for travellers. The authors conclude that challenges can be bypassed by adopting a BDAI strategy. Such an adoption will stand critical for the competitiveness and resilience of existing established and new players in the tourism sector. Originality/value Besides identifying the benefits that BDAI brings in the tourism sector, the research proposes a guidebook to overcome challenges when introducing such new technologies. The exploration of the BDAI literature brings important implication for managers, academicians and consumers. This is the first systematic review in an area and contributes to the broader e-commerce marketing, retailing and e-tourism research.

Relationships between industry 4.0, sustainable manufacturing and circular economy: proposal of a research framework

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Surajit Bag ◽  
Jan Harm Christiaan Pretorius
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Circular Economy ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Sustainable Manufacturing ◽  
Big Data Analytics ◽  
Content Type ◽  
Research Framework ◽  
Challenges And Opportunities

Purpose The digital revolution has brought many challenges and opportunities for the manufacturing firms. The impact of Industry 4.0 technology adoption on sustainable manufacturing and circular economy has been under-researched. This paper aims to review the latest articles in the area of Industry 4.0, sustainable manufacturing and circular economy and further developed a research framework showing key paths. Design/methodology/approach Qualitative research is performed in two stages. In the first stage, a review of the extant literature is performed to identify the barriers, drivers, challenges and opportunities. In the second stage, a research framework is proposed to integrate Industry 4.0 technology (big data analytics powered artificial intelligence) adoption, sustainable manufacturing and circular economy capabilities. Findings This research extends the knowledge base by providing a detailed review of Industry 4.0, sustainable manufacturing, and circular economy and proposes a research framework by integrating these three contemporary concepts in the context of supply chain management. Through an exploration of this integrative research framework, the authors propose a future research agenda and seven research propositions. Research limitations/implications It is important to understand the interplay between institutional pressures, tangible resources and human skills for Industry 4.0 technology (big data analytics powered artificial intelligence) adoption. Industry 4.0 technology (big data analytics powered artificial intelligence) adoption can positively influence sustainable manufacturing and circular economy capabilities. Managers must also put more attention to sustainable manufacturing to develop circular economic capabilities. Social implications Factory workers and the local communities generally suffer from various adverse effects resulting from the traditional manufacturing process. The quality of the environment is deteriorating to such an extent that people even staying miles away from the factory are also affected due to environmental pollution that is generated from factory operations. Hence, sustainable manufacturing is the only choice left to manufacturers that can help in the transition to a circular economy. The research framework can help firms to enhance circular economy capabilities. Originality/value This review paper contains the most updated work on Industry 4.0, sustainable manufacturing and circular economy. It also proposes a research framework to integrate these three concepts.

scholarly journals Optimization model: the innovation and future of e-ecotourism for sustainability

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fauziah Eddyono ◽  
Dudung Darusman ◽  
Ujang Sumarwan ◽  
Fauziah Sunarminto
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Sustainable Tourism ◽  
System Modeling ◽  
Secondary Data ◽  
Simulation Models ◽  
Content Type ◽  
Causal Loop ◽  
Artificial Intelligence Technology ◽  
Intelligent Technology

PurposeThis study aims to find a dynamic model in an effort to optimize tourism performance in ecotourism destinations. The model structure is built based on competitive performance in geographic areas and the application of ecotourism elements that are integrated with big data innovation through artificial intelligence technology.Design/methodology/approachData analysis is performed through dynamic system modeling. Simulations are carried out in three models: First, existing simulation models. Second, Scenario 1 is carried out by utilizing a causal loop through innovation of big data-based artificial intelligence technology to ecotourism elements. Third, Scenario 2 is carried out by utilizing a causal loop through big data-based artificial intelligence technology on aspects of ecotourism elements and destination competitiveness.FindingsThis study provides empirical insight into the competitiveness performance of destinations and the performance of implementing ecotourism elements if integrated with big data innovations that will be able to massively demonstrate the growth of sustainable tourism performance.Research limitations/implicationsThis study does not use a primary database, but uses secondary data from official sources that can be accessed by the public.Practical implicationsThe paper includes implications for the development of intelligent technology based on big data and also requires policy innovation.Social implicationsSustainable tourism development.Originality/valueThis study finds the expansion of new theory competitiveness of ecotourism destinations.

We’re Missing a Moral Framework of Justice in Artificial Intelligence

2020 ◽  
pp. 161-179
Author(s):  
Matthew Le Bui ◽  
Safiya Umoja Noble
Keyword(s):  
Artificial Intelligence ◽  
Facial Recognition ◽  
Community Organizations ◽  
Power Structures ◽  
Artificial Intelligence Research ◽  
Moral Framework ◽  
High Profile ◽  
Ai Ethics ◽  
Objective State ◽  
Recognition Software

This chapter assesses the concepts of fairness and bias in artificial intelligence research and interventions. In considering the explosive growth, emergence of, and investment in high-profile AI fairness and ethics interventions within both the academy and industry—alongside the mounting and proliferating calls for the interrogation, regulation, and, in some cases, dismantling and prohibition of AI—it contests and questions the extent to which such remedies can address the original concerns and problems they are designed to address. Indeed, many community organizations are organizing responses and challenging AI used in predictive technologies—facial-recognition software and biometrics technologies—with increasing success. Ultimately, the canon of AI ethics must interrogate and deeply engage with intersectional power structures that work to further consolidate capital in the hands of the elites and that will undergird digital informational systems of inequality: there is no neutral or objective state through which the flows and mechanics of data can be articulated as unbiased or fair.

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