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Title: Human-centered artificial intelligence in education: Seeing the invisible through the visible
Authors: Yang, J.H., Stephen
Ogata, Hiroaki  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0001-5216-1576 (unconfirmed)
Matsui, Tatsunori
Chen, Nian-Shing
Author's alias: 緒方, 広明
Keywords: Human-centered AI
Sustainable AI
Explainable AI
Interpretable ML
Smart learning
Issue Date: 2021
Publisher: Elsevier BV
Journal title: Computers and Education: Artificial Intelligence
Volume: 2
Thesis number: 100008
Abstract: The inevitable rise and development of artificial intelligence (AI) was not a sudden occurrence. The greater the effect that AI has on humans, the more pressing the need is for us to understand it. This paper addresses research on the use of AI to evaluate new design methods and tools that can be leveraged to advance AI research, education, policy, and practice to improve the human condition. AI has the potential to educate, train, and improve the performance of humans, making them better at their tasks and activities. The use of AI can enhance human welfare in numerous respects, such as through improving the productivity of food, health, water, education, and energy services. However, the misuse of AI due to algorithm bias and a lack of governance could inhibit human rights and result in employment, gender, and racial inequality. We envision that AI can evolve into human-centered AI (HAI), which refers to approaching AI from a human perspective by considering human conditions and contexts. Most current discussions on AI technology focus on how AI can enable human performance. However, we explore AI can also inhibit the human condition and advocate for an in-depth dialog between technology- and humanity-based researchers to improve understanding of HAI from various perspectives.
Rights: © 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
URI: http://hdl.handle.net/2433/261148
DOI(Published Version): 10.1016/j.caeai.2021.100008
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