Teaching Responsible Data Science: Charting New Pedagogical Territory

Autor/inn/en	Lewis, Armanda; Stoyanovich, Julia
Titel	Teaching Responsible Data Science: Charting New Pedagogical Territory
Quelle	In: International Journal of Artificial Intelligence in Education, 32 (2022) 3, S.783-807 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Lewis, Armanda)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1560-4292
DOI	10.1007/s40593-021-00241-7
Schlagwörter	Statistics Education; Ethics; Artificial Intelligence; Compliance (Legal); Mathematics; Accountability; Privacy; Information Security; Data Interpretation; Decision Making; Best Practices; Learning Analytics; Metacognition; Models + Suchen Sie Ihr Suchwort? Ethik; Künstliche Intelligenz; Mathematik; Verantwortung; Privatsphäre; Data evaluation; Datenauswertung; Decision-making; Entscheidungsfindung; Meta cognitive ability; Meta-cognition; Metakognitive Fähigkeit; Metakognition; Analogiemodell
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01