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Huang K, Teng Y, Chen Y, Wang Y. From Pixels to Principles: A Decade of Progress and Landscape in Trustworthy Computer Vision. SCIENCE AND ENGINEERING ETHICS 2024; 30:26. [PMID: 38856788 PMCID: PMC11164730 DOI: 10.1007/s11948-024-00480-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 04/17/2024] [Indexed: 06/11/2024]
Abstract
The rapid development of computer vision technologies and applications has brought forth a range of social and ethical challenges. Due to the unique characteristics of visual technology in terms of data modalities and application scenarios, computer vision poses specific ethical issues. However, the majority of existing literature either addresses artificial intelligence as a whole or pays particular attention to natural language processing, leaving a gap in specialized research on ethical issues and systematic solutions in the field of computer vision. This paper utilizes bibliometrics and text-mining techniques to quantitatively analyze papers from prominent academic conferences in computer vision over the past decade. It first reveals the developing trends and specific distribution of attention regarding trustworthy aspects in the computer vision field, as well as the inherent connections between ethical dimensions and different stages of visual model development. A life-cycle framework regarding trustworthy computer vision is then presented by making the relevant trustworthy issues, the operation pipeline of AI models, and viable technical solutions interconnected, providing researchers and policymakers with references and guidance for achieving trustworthy CV. Finally, it discusses particular motivations for conducting trustworthy practices and underscores the consistency and ambivalence among various trustworthy principles and technical attributes.
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Affiliation(s)
- Kexin Huang
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China
- Fudan University, Shanghai, 200438, China
| | - Yan Teng
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China.
| | - Yang Chen
- Fudan University, Shanghai, 200438, China
| | - Yingchun Wang
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China
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Coghlan S, Gyngell C, Vears DF. Ethics of artificial intelligence in prenatal and pediatric genomic medicine. J Community Genet 2024; 15:13-24. [PMID: 37796364 PMCID: PMC10857992 DOI: 10.1007/s12687-023-00678-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 09/27/2023] [Indexed: 10/06/2023] Open
Abstract
This paper examines the ethics of introducing emerging forms of artificial intelligence (AI) into prenatal and pediatric genomic medicine. Application of genomic AI to these early life settings has not received much attention in the ethics literature. We focus on three contexts: (1) prenatal genomic sequencing for possible fetal abnormalities, (2) rapid genomic sequencing for critically ill children, and (3) reanalysis of genomic data obtained from children for diagnostic purposes. The paper identifies and discusses various ethical issues in the possible application of genomic AI in these settings, especially as they relate to concepts of beneficence, nonmaleficence, respect for autonomy, justice, transparency, accountability, privacy, and trust. The examination will inform the ethically sound introduction of genomic AI in early human life.
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Affiliation(s)
- Simon Coghlan
- School of Computing and Information Systems (CIS), Centre for AI and Digital Ethics (CAIDE), The University of Melbourne, Grattan St, Melbourne, Victoria, 3010, Australia.
- Australian Research Council Centre of Excellence for Automated Decision Making and Society (ADM+S), Melbourne, Victoria, Australia.
| | - Christopher Gyngell
- Biomedical Ethics Research Group, Murdoch Children's Research Institute, The Royal Children's Hospital, 50 Flemington Rd, Parkville, Victoria, 3052, Australia
- University of Melbourne, Parkville, Victoria, 3052, Australia
| | - Danya F Vears
- Biomedical Ethics Research Group, Murdoch Children's Research Institute, The Royal Children's Hospital, 50 Flemington Rd, Parkville, Victoria, 3052, Australia
- University of Melbourne, Parkville, Victoria, 3052, Australia
- Centre for Biomedical Ethics and Law, KU Leuven, Kapucijnenvoer 35, 3000, Leuven, Belgium
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Patra AK, Praharaj A, Sudarshan D, Chhatoi BP. AI and business management: Tracking future research agenda through bibliometric network analysis. Heliyon 2024; 10:e23902. [PMID: 38230239 PMCID: PMC10789597 DOI: 10.1016/j.heliyon.2023.e23902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 12/12/2023] [Accepted: 12/15/2023] [Indexed: 01/18/2024] Open
Abstract
This study has been designed to analyse the academic landscape of AI on the Scopus and Web of Science (WOS) indices and compare the findings. AI is one of the most prominent and preferred research areas, only a few studies are dedicated to the bibliometric aspect of it. There is a need to compare studies on AI over different databases to identify the impact and usefulness of those studies in decision-making in business management. To conduct this analysis, the authors have collected data from both Scopus and WOS. 'VOSviewer', 'R-Studio', and 'MS Excel' software have been used for performance analysis and science mapping. This is one of the exceptional studies which perform a comparative analysis between two indices and also identifies funding sponsors for support of research in AI. "Dwivedi, Y.K." is the most productive author and "Huang, Minghui" is the most impactful author. "National Natural Science Foundation of China" is the funding agency which has significantly supported AI research. Technical aspects like "Machine learning", "neural networks", and "blockchain" with 'Sustainability', 'sustainable development', 'accounting', and 'auditing' are trending themes for managerial decision-making.
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Wang Q, Sun T, Li R. Does artificial intelligence (AI) reduce ecological footprint? The role of globalization. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:123948-123965. [PMID: 37995036 DOI: 10.1007/s11356-023-31076-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 11/13/2023] [Indexed: 11/24/2023]
Abstract
This article explores the impact of artificial intelligence (AI) on global ecological footprints, which has important implications for global sustainability in the digital age. Using the comprehensive evaluation index of AI constructed by the entropy method and the dataset at the global national level, we find that from 2010 to 2019, the overall level of global AI shows an upward trend, in which the growth rate of AI in developed countries is more pronounced and exhibits a stable growth trend, while the growth rate of AI in developing countries displays a trend of instability. The research results show that AI has a significant inhibitory effect on ecological footprints. This conclusion holds even after endogeneity and robustness tests. In addition, under the effect of globalization, the impact of AI on ecological footprints shows nonlinear characteristics. As globalization deepens, the marginal effect of AI in reducing the ecological footprint shows an increasing trend. These findings emphasize the important role of AI in environmental governance and provide a new and comprehensive perspective for policymakers. Therefore, the government should continue to support the research and application of AI, promote the cross-industry integration of AI, and play a positive role in the process of globalization to promote global sustainable development.
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Affiliation(s)
- Qiang Wang
- School of Economics and Management, China University of Petroleum (East China), Qingdao, 266580, People's Republic of China.
- School of Economics and Management, Xinjiang University, Wulumuqi, 830046, People's Republic of China.
| | - Tingting Sun
- School of Economics and Management, China University of Petroleum (East China), Qingdao, 266580, People's Republic of China
| | - Rongrong Li
- School of Economics and Management, China University of Petroleum (East China), Qingdao, 266580, People's Republic of China
- School of Economics and Management, Xinjiang University, Wulumuqi, 830046, People's Republic of China
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Wu M, Zhang Y, Markley M, Cassidy C, Newman N, Porter A. COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solution. Scientometrics 2023:1-31. [PMID: 37360228 PMCID: PMC10230150 DOI: 10.1007/s11192-023-04747-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 05/16/2023] [Indexed: 06/28/2023]
Abstract
COVID-19 has been an unprecedented challenge that disruptively reshaped societies and brought a massive amount of novel knowledge to the scientific community. However, as this knowledge flood continues surging, researchers have been disadvantaged by not having access to a platform that can quickly synthesize emerging information and link the new knowledge to the latent knowledge foundation. Aiming to fill this gap, we propose a research framework and develop a dashboard that can assist scientists in identifying, retrieving, and understanding COVID-19 knowledge from the ocean of scholarly articles. Incorporating principal component decomposition (PCD), a knowledge mode-based search approach, and hierarchical topic tree (HTT) analysis, the proposed framework profiles the COVID-19 research landscape, retrieves topic-specific latent knowledge foundation, and visualizes knowledge structures. The regularly updated dashboard presents our research results. Addressing 127,971 COVID-19 research papers from PubMed, the PCD topic analysis identifies 35 research hotspots, along with their inner correlations and fluctuating trends. The HTT result segments the global knowledge landscape of COVID-19 into clinical and public health branches and reveals the deeper exploration of those studies. To supplement this analysis, we additionally built a knowledge model from research papers on the topic of vaccination and fetched 92,286 pre-Covid publications as the latent knowledge foundation for reference. The HTT analysis results on the retrieved papers show multiple relevant biomedical disciplines and four future research topics: monoclonal antibody treatments, vaccinations in diabetic patients, vaccine immunity effectiveness and durability, and vaccination-related allergic sensitization.
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Affiliation(s)
- Mengjia Wu
- Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia
| | - Yi Zhang
- Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia
| | | | | | | | - Alan Porter
- Search Technology, Inc., Norcross, USA
- Science, Technology & Innovation Policy, Georgia Institute of Technology, Atlanta, USA
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Rao D. The Urgent Need for Healthcare Workforce Upskilling and Ethical Considerations in the Era of AI-Assisted Medicine. Indian J Otolaryngol Head Neck Surg 2023:1-2. [PMID: 37362116 PMCID: PMC10132410 DOI: 10.1007/s12070-023-03755-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 03/30/2023] [Indexed: 06/28/2023] Open
Abstract
This letter is in response to the article "Enhancing India's Health Care during COVID Era: Role of Artificial Intelligence and Algorithms". While the integration of AI has the potential to improve patient outcomes and reduce the workload of healthcare professionals, there is a need for significant training and upskilling of healthcare providers. There are ethical and privacy concerns related to the use of AI in healthcare, which must be accompanied by rigorous guidelines. One solution to the overburdened healthcare systems in India is the use of new language generation models like ChatGPT to assist healthcare workers in writing discharge summaries. By using these technologies responsibly, we can improve healthcare outcomes and alleviate the burden on overworked healthcare professionals.
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Affiliation(s)
- Divya Rao
- Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, 576104 Manipal, India
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Nitiéma P. Artificial Intelligence in Medicine: Text Mining of Health Care Workers' Opinions. J Med Internet Res 2023; 25:e41138. [PMID: 36584303 PMCID: PMC9919460 DOI: 10.2196/41138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 11/11/2022] [Accepted: 12/19/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) is being increasingly adopted in the health care industry for administrative tasks, patient care operations, and medical research. OBJECTIVE We aimed to examine health care workers' opinions about the adoption and implementation of AI-powered technology in the health care industry. METHODS Data were comments about AI posted on a web-based forum by 905 health care professionals from at least 77 countries, from May 2013 to October 2021. Structural topic modeling was used to identify the topics of discussion, and hierarchical clustering was performed to determine how these topics cluster into different groups. RESULTS Overall, 12 topics were identified from the collected comments. These comments clustered into 2 groups: impact of AI on health care system and practice and AI as a tool for disease screening, diagnosis, and treatment. Topics associated with negative sentiments included concerns about AI replacing human workers, impact of AI on traditional medical diagnostic procedures (ie, patient history and physical examination), accuracy of the algorithm, and entry of IT companies into the health care industry. Concerns about the legal liability for using AI in treating patients were also discussed. Positive topics about AI included the opportunity offered by the technology for improving the accuracy of image-based diagnosis and for enhancing personalized medicine. CONCLUSIONS The adoption and implementation of AI applications in the health care industry are eliciting both enthusiasm and concerns about patient care quality and the future of health care professions. The successful implementation of AI-powered technologies requires the involvement of all stakeholders, including patients, health care organization workers, health insurance companies, and government regulatory agencies.
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Affiliation(s)
- Pascal Nitiéma
- Department of Information Systems, Arizona State University, Tempe, AZ, United States
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Keramatfar A, Rafiee M, Amirkhani H. Graph Neural Networks: A bibliometrics overview. MACHINE LEARNING WITH APPLICATIONS 2022. [DOI: 10.1016/j.mlwa.2022.100401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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Liu H, Zhao N, Zhang X, Lin H, Yang L, Xu B, Lin Y, Fan W. Dual constraints and adversarial learning for fair recommenders. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.108058] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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