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受各国人工智能发展规划影响，教育人工智能伦理原则研究起步晚于其他领域，塑造了其参照已有原则“共识”的构建取向。目前，对“共识”的反思及对教育领域个性要素的探寻逐渐受到研究者关注，超越“共识”成为教育人工智能伦理原则构建的发展趋势。“共识”暗含的“非共识”要素是制约教育人工智能伦理原则实践效果的根本原因，包括国别间的历史与文化差异，领域间的结构与指向差异，应用间的内容与对象差异，自身间的概念与关系差异。超越“共识”实际上便是在已有研究的基础上弥合“非共识”要素。更直接的说，就是承认教育人工智能伦理原则的有限性，即价值匹配的有限性、领域作用的有限性、指导时效的有限性，从而在构建教育人工智能伦理原则时坚持本土立场，明确目的导向，保持持续开放。
Abstract：Influenced by the development plans of various countries on artificial intelligence, research on ethical principles of AI education started later than other areas and shaped its construction orientation with reference to existing “consensus” principles.Currently, researchers are gradually paying attention to reflecting on the “consensus” and exploring the individual elements of the education field, surpassing the “consensus” becoming the development trend for constructing ethical principles in AI education.The fundamental reason that limits the practical effect of ethical principles in AI education is the “non-consensus” element implied by the “consensus”, including historical and cultural differences between countries, structural and directional differences between fields, content and object differences between applications, and conceptual and relational differences within themselves. To surpass the “consensus” means bridging the “non-consensus” elements based on existing research. More directly, it is acknowledging the limitations of ethical principles in AI education, including the limitations in value matching,domain applicability, and guidance timeliness, thus adhering to a local stance, clarifying purpose orientation, and maintaining continuous openness when constructing ethical principles in AI education.
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(1)教育人工智能伦理原则在政策文件与学术研究中通常不会以单个原则的形式出现，而是一个包含了多个伦理原则的框架，并冠以“指导方针”“责任”“指南”“行为准则”等名称。
(1)教育人工智能(AIED)的双重角色是指既可以通过人工智能技术促进教育发展，也可以通过教育促进人工智能技术及其相关领域的发展，亦即“AI for Education”与“Education for AI”。
(1)即使是达成共识的伦理原则，不同群体对其的价值排序也有所不同。例如，“隐私”在崇尚个人主义的地区的重视优先级可能高于崇尚集体主义的地区。
(2)欧洲人工智能伦理的语料库包含一套价值观和原则体系，将规范应用于具体案例并转化为符合伦理的行动，即一种将道德理想融入公民和社会现实的机制。参见Dubber M D,Pasquale F,et al.The Oxford Handbook of Ethics of AI[M].Oxford:Oxford University Press,2020.649-666。
(1)例如，目前人工智能可能比医生更能准确的诊断医疗状况，但不一定能够提供令人满意的解释。
(1)例如，当前对于公平的算法定义至少有21种，且没有一种公平性算法能够同时满足不同的公平性条件。参见Mitchell S,Potash E,et al.Algorithmic fairness:Choices,assumptions,and definitions[J].Annual Review of Statistics and Its Application,2021,8(1):141-163.及Kleinberg J,Mullainathan S,et al.Inherent trade-offs in the fair determination of risk scores[EB/OL].https://arxiv.org/abs/1609.05807,2016-11-17.
(2)即教育的目的是为了知识构建、社会化还是自我实现。
基本信息:
中图分类号:G434
引用信息:
[1]白钧溢,于伟.超越“共识”：教育人工智能伦理原则构建的发展方向[J].中国电化教育,2023,No.437(06):9-17+24.
基金信息:
2022吉林省社会科学基金项目“中国‘Z世代’亚文化圈层语言的区隔性研究”(项目编号：2022C78); 2022东北师范大学教师教育“揭榜领题”一般项目“教师‘社会与情感能力’的国际框架与本土转化研究”(项目编号：JSJY20220302)研究成果
2023-06-08
2023-06-08