A workshop on artificial intelligence biases and its effect on high school students’ perceptions

Q1 Social Sciences
Marcos J. Gómez , Julián Dabbah , Luciana Benotti
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引用次数: 0

Abstract

This paper introduces a workshop aimed at concurrently addressing technical concepts and ethical considerations on artificial intelligence (AI) biases, with an emphasis on societal and automation biases. Unlike conventional approaches that often prioritize either the technical intricacies or the ethical implications of AI, our workshop integrates these dimensions in parallel. Through a series of activities, we illustrate how design decisions made by individuals involved in AI development, such as defining classes and selecting training sets, can introduce biases into AI models. We also explore errors or biases in model decisions, shedding light on the nuanced challenges of AI development.
The workshop’s impact on high school students’ perceptions of AI technology was assessed through pre and post-tests. Statistical analysis revealed a significant reduction in students’ agreement with statements regarding the absence of AI societal biases, the lack of influence of AI designers on AI behavior, and the superiority of AI solutions over human alternatives. While perceived as a highly positive and engaging experience, the workshop was also recognized as a practical and motivating endeavor, aligning with our didactic approach emphasizing experiential learning over theoretical exposition.
一个关于人工智能偏见及其对高中生认知的影响的研讨会
本文介绍了一个研讨会,旨在同时解决人工智能(AI)偏见的技术概念和伦理考虑,重点是社会和自动化偏见。与通常优先考虑人工智能技术复杂性或伦理影响的传统方法不同,我们的研讨会并行地整合了这些维度。通过一系列活动,我们说明了参与人工智能开发的个人所做的设计决策,如定义类和选择训练集,如何将偏见引入人工智能模型。我们还探讨了模型决策中的错误或偏见,揭示了人工智能开发的微妙挑战。研讨会通过前后测试评估了对高中生对人工智能技术认知的影响。统计分析显示,学生对人工智能不存在社会偏见、人工智能设计师对人工智能行为缺乏影响、人工智能解决方案优于人类替代方案等观点的认同程度显著降低。虽然被认为是一个非常积极和引人入胜的经历,但研讨会也被认为是一个实践和激励的努力,与我们强调经验学习而不是理论阐述的教学方法相一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
0.00%
发文量
73
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