From Pixels to Principles: A Decade of Progress and Landscape in Trustworthy Computer Vision
-
Published:2024-06-10
Issue:3
Volume:30
Page:
-
ISSN:1471-5546
-
Container-title:Science and Engineering Ethics
-
language:en
-
Short-container-title:Sci Eng Ethics
Author:
Huang Kexin, Teng YanORCID, Chen Yang, Wang Yingchun
Abstract
AbstractThe 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.
Publisher
Springer Science and Business Media LLC
Reference61 articles.
1. Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E. D., Gutierrez, J. B., & Kochut, K. (2017). A brief survey of text mining: Classification, clustering and extraction techniques. 2. Andraško, J., Mesarčík, M., & Hamul’ák, O. (2021). The regulatory intersections between artificial intelligence, data protection and cyber security: Challenges and opportunities for the EU legal framework. AI and Society, 1–14. 3. Bau, D., Zhu, J.-Y., Strobelt, H., Zhou, B., Tenenbaum, J. B., Freeman, W. T., & Torralba, A. (2018). GAN dissection: Visualizing and understanding generative adversarial networks. 4. Beery, S., Wu, G., Edwards, T., Pavetic, F., Majewski, B., Mukherjee, S., Chan, S., Morgan, J., Rathod, V., & Huang, J. (2022). The auto arborist dataset: A large-scale benchmark for multiview urban forest monitoring under domain shift. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 21294–21307). 5. Beliga, S. (2014). Keyword extraction: A review of methods and approaches. University of Rijeka, Department of Informatics, Rijeka 1(9).
|
|