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Teaching Responsible Data Science: Charting New Pedagogical Territory.

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This study introduces a technical course for responsible data science (AI) education, emphasizing interpretability tools like "nutritional labels" for ethical AI development and data analysis.

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Area of Science:

  • Computer Science
  • Artificial Intelligence Ethics
  • Data Science Pedagogy

Background:

  • Existing ethical data science and AI courses often lack technical, hands-on approaches.
  • Pedagogical methods predominantly rely on textual analysis rather than algorithmic or data-driven exercises.
  • There is a growing need for practical training in responsible artificial intelligence.

Purpose of the Study:

  • To detail the development and teaching of a technical course on responsible data science.
  • To integrate ethical considerations, legal compliance, and data protection into AI education.
  • To explore pedagogical best practices for teaching AI interpretability and its connection to responsible data science.

Main Methods:

  • Developed and taught a technical course covering AI ethics, fairness, transparency, privacy, and data protection.
  • Focused on interpretability of machine-assisted decision-making as a core concept.
  • Introduced the "object-to-interpret-with" methodological notion and "nutritional labels" as interpretability tools.

Main Results:

  • The course successfully integrated technical and ethical aspects of data science.
  • Interpretability was shown to be a key element for understanding broader responsible data science issues.
  • "Nutritional labels" proved effective for teaching machine learning model interpretation.

Conclusions:

  • Technical, hands-on approaches are crucial for effective responsible data science education.
  • Interpretability tools like "nutritional labels" enhance student understanding of complex AI ethics.
  • Integrating learning science research can improve AI and data science pedagogy.