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Abstract: Embedding Data Justice: A Framework for Ethical Decision-Making and Storytelling in Library and Information Science Education
Integrating ethical considerations with practical problem-solving in data use is crucial in academic, community, and industry contexts. Our paper will focus on how library education can integrate the ethical use of community data for decision-making, advocacy, and storytelling. We will share results from our on-going project that employs a critical framework to incorporate data ethics and justice into undergraduate Data Science (DS) and masters-level Library and Information Science (MLIS) curricula, emphasizing their relevance in pedagogical strategies and practical applications.
Our project aims to: 1) develop a cohesive pedagogical framework guided by the Data Science Ethos Lifecycle; 2) create and test sample assignments, focusing on synergizing ethics with technical content; and 3) promote diversity, inclusivity, and accessibility in course content. The expected outcomes will equip students with both theoretical insights and practical skills to use data responsibly as library workers and community partners.
Recent reforms underscore the importance of integrating justice and ethical considerations with technical skills, transitioning from traditional compliance-focused education to a practical, problem-solving approach (Baumer et al., 2022; Saltz et al., 2019; ALA, 2023). Although there is increased emphasis on ethics and justice, difficulties persist in integrating ethics into professional and technical education, including what constitutes comprehensive data ethics education, the complexity of ethical issues, and instructors' lack of formal ethics training (Brown et al., 2023). Instructional design poses challenges, such as a shortage of suitable resources and difficulty in assessing ethical knowledge in technical classes (Fiesler et al., 2021). Further, students often struggle to connect ethics with technical content due to limited ethics training (Cohen et al., 2021; Tseng et al., 2022), highlighting the need for shared educational strategies and resources (Smith et al., 2023).
Our first aim involves revising the PLOs of DS and MLIS programs using the Data Science Life Cycle Ethos Approach (Boenig-Liptsin et al., 2022). This approach encourages maintaining ethical practices throughout the data science workflow, from problem framing to result communication, by teaching how to pair ethical reflection with each stage of the lifecycle (Keller et al., 2020).
The second aim is the development of integrative assignments that align ethical content with technical skills based on the Embedded EthiCS approach used at Stanford (Grosz et al., 2019). This strategy focuses on the development of core curriculum that embeds ethical reasoning within specific technical implementations (Brown et al., 2023).
The third aim is to ensure diversity, inclusivity, and accessibility in course contents and assignments. We will follow the Columbia Guide for Inclusive Teaching and Learning (2017), focusing on diversity and inclusivity in content and accessibility in instructional design. It aligns with trends emphasizing critical and social perspectives in data ethics education (Beaulieu & Leonelli, 2022; Hoffman & Cross, 2021), informed by transdisciplinary knowledge and case studies on inequalities perpetuated by data and AI (Chi et al., 2021; Hicks, 2013; Nelsen, 2017).
We will provide results on the revision of program learning outcomes, sample assignments based on the utilization of the aforementioned frameworks, and the revision of the course, Public Library Management.
Our project aims to: 1) develop a cohesive pedagogical framework guided by the Data Science Ethos Lifecycle; 2) create and test sample assignments, focusing on synergizing ethics with technical content; and 3) promote diversity, inclusivity, and accessibility in course content. The expected outcomes will equip students with both theoretical insights and practical skills to use data responsibly as library workers and community partners.
Recent reforms underscore the importance of integrating justice and ethical considerations with technical skills, transitioning from traditional compliance-focused education to a practical, problem-solving approach (Baumer et al., 2022; Saltz et al., 2019; ALA, 2023). Although there is increased emphasis on ethics and justice, difficulties persist in integrating ethics into professional and technical education, including what constitutes comprehensive data ethics education, the complexity of ethical issues, and instructors' lack of formal ethics training (Brown et al., 2023). Instructional design poses challenges, such as a shortage of suitable resources and difficulty in assessing ethical knowledge in technical classes (Fiesler et al., 2021). Further, students often struggle to connect ethics with technical content due to limited ethics training (Cohen et al., 2021; Tseng et al., 2022), highlighting the need for shared educational strategies and resources (Smith et al., 2023).
Our first aim involves revising the PLOs of DS and MLIS programs using the Data Science Life Cycle Ethos Approach (Boenig-Liptsin et al., 2022). This approach encourages maintaining ethical practices throughout the data science workflow, from problem framing to result communication, by teaching how to pair ethical reflection with each stage of the lifecycle (Keller et al., 2020).
The second aim is the development of integrative assignments that align ethical content with technical skills based on the Embedded EthiCS approach used at Stanford (Grosz et al., 2019). This strategy focuses on the development of core curriculum that embeds ethical reasoning within specific technical implementations (Brown et al., 2023).
The third aim is to ensure diversity, inclusivity, and accessibility in course contents and assignments. We will follow the Columbia Guide for Inclusive Teaching and Learning (2017), focusing on diversity and inclusivity in content and accessibility in instructional design. It aligns with trends emphasizing critical and social perspectives in data ethics education (Beaulieu & Leonelli, 2022; Hoffman & Cross, 2021), informed by transdisciplinary knowledge and case studies on inequalities perpetuated by data and AI (Chi et al., 2021; Hicks, 2013; Nelsen, 2017).
We will provide results on the revision of program learning outcomes, sample assignments based on the utilization of the aforementioned frameworks, and the revision of the course, Public Library Management.