Artificial Intelligence and Human: Beyond the Positive and Negative Impact of AI

被引:0
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作者
Jian, Qin [1 ]
机构
[1] School of Information Studies, Syracuse University, United States
关键词
Laws and legislation;
D O I
10.11925/infotech.2096-3467.2024.0711
中图分类号
学科分类号
摘要
The explosive growth of Artificial intelligence (AI) has both positive and negative impacts on many aspects of human society and environment. While academia and industry are optimistic about the positive impacts on enhancing education, environment sustainability, healthcare systems and quality, and transportation of people and goods, experts are concerned about the potential harm and danger resulting from AI’s negative impacts if they are not contained. This essay discusses why it is imperative to emphasize the trustworthiness of AI and current developments in assuring trustworthy AI. The convenience and efficiency brought about by AI applications are embraced by both experts and general public, but how to contain the potential negative impacts and possible harms and dangers that come from ill- and/or even evil-purposed actors is a tremendous, complex challenge. Establishing trustworthy AI is considered as a major approach to fighting and containing the negative impacts of AI. Efforts in trustworthy AI include two broad areas: policies and regulations from governments and research and development (R&D) from academia and industry sectors. The policies and regulations focus on the ethical, legal, and robustness principles to provide guidance for R&D in trustworthy AI. In research publications, a commonly shared view is that trustworthy AI should have properties of reliability, safety, security, privacy, availability, and usability. For different population groups, the requirements for trustworthy AI may vary. One important development in trustworthy AI is the shift from model-centric AI to data-centric AI. The paradigm of data-centric AI emphasizes data quality through systematic design of datasets used for machine learning modeling, which include data design, data sculpting, and data strategies with data policy throughout the whole data design, sculpting, and strategy process. Both policy and technical developments in shaping trustworthy AI and containing the negative impact of AI present many new research and development opportunities for academia and industry. © 2024 Chinese Academy of Sciences. All rights reserved.
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