Teaching Responsible Data Science: Charting New Pedagogical Territory

Armanda Lewis, Julia Stoyanovich

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Although an increasing number of ethical data science and AI courses is available, with many focusing specifically on technology and computer ethics, pedagogical approaches employed in these courses rely exclusively on texts rather than on algorithmic development or data analysis. In this paper we recount a recent experience in developing and teaching a technical course focused on responsible data science, which tackles the issues of ethics in AI, legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, privacy, and data protection. Interpretability of machine-assisted decision-making is an important component of responsible data science that gives a good lens through which to see other responsible data science topics, including privacy and fairness. We provide emerging pedagogical best practices for teaching technical data science and AI courses that focus on interpretability, and tie responsible data science to current learning science and learning analytics research. We focus on a novel methodological notion of the object-to-interpret-with, a representation that helps students target metacognition involving interpretation and representation. In the context of interpreting machine learning models, we highlight the suitability of “nutritional labels”—a family of interpretability tools that are gaining popularity in responsible data science research and practice.

    Original languageEnglish (US)
    JournalInternational Journal of Artificial Intelligence in Education
    DOIs
    StateAccepted/In press - 2021

    Keywords

    • Constructivism
    • Data science pedagogy
    • Model interpretability
    • Responsible data science

    ASJC Scopus subject areas

    • Education
    • Computational Theory and Mathematics

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