This article addresses three key questions related to the ethical facets of algorithmic literacy. First, it synthesizes existing literature to identify six core ethical components, including bias, privacy, transparency, accountability, accuracy, and non-maleficence. Second, a crosswalk maps the intersections of these principles across the Association of College and Research Libraries' Framework for Information Literacy for Higher Education and the Association of Computing Machinery's Code of Ethics and Professional Conduct and Joint Statement on Principles for Responsible Algorithmic Systems. This analysis reveals significant overlap on issues like unfairness and transparency, helping prioritize topics for instruction. Finally, case studies showcase pedagogical strategies for teaching ethical considerations, informed by the crosswalk. Workshops for diverse undergraduates and computer science students employed reallife instances of algorithmic bias to prompt reflection on unintended harm, contestability, and responsible development. Pre-post surveys indicated expanded critical perspectives after the interventions. By systematically examining shared values and testing instructional approaches, this study provides practical tools to shape ethical thinking on algorithms. It also demonstrates promising practices for responsibly advancing algorithmic literacy across disciplines. Ultimately, fostering interdisciplinary awareness and multipronged educational initiatives can empower students to question algorithmic authority and biases.

中文翻译：

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大学生算法素养的道德维度：案例研究和跨学科联系

本文解决了与算法素养的道德方面相关的三个关键问题。首先，它综合了现有文献，确定了六个核心道德要素，包括偏见、隐私、透明度、问责制、准确性和非恶意。其次，人行横道绘制了大学和研究图书馆协会的高等教育信息素养框架、计算机协会的道德和专业行为准则以及负责任的算法系统原则联合声明中这些原则的交叉点。该分析揭示了不公平和透明度等问题的显着重叠，有助于确定教学主题的优先顺序。最后，案例研究展示了以人行横道为背景的道德考虑的教学策略。为不同的本科生和计算机科学专业的学生举办的研讨会利用现实生活中的算法偏见实例来促使人们反思意外伤害、可竞争性和负责任的发展。事前调查显示干预后批评观点有所扩大。通过系统地检验共同价值观和测试教学方法，本研究提供了塑造算法道德思维的实用工具。它还展示了负责任地提高跨学科算法素养的有希望的实践。最终，培养跨学科意识和多管齐下的教育举措可以让学生质疑算法的权威和偏见。