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Responsible Data Science Objectives (RDSO)

An RDSO is a core learning unit in the ConCReTE Curriculum that prompts students to think critically about the ethical and contextual dimensions of data science within real-world scenarios. Unlike technical data science tasks, RDSOs challenge learners to engage with decision-making—before, during, or after data use—by weighing stakeholder needs, organizational values, and potential trade-offs.

Each RDSO is designed to:

  • Build digital leadership attributes like agency, confidence, and accountability.
  • Highlight decision points that reveal multiple valid paths, not just a single solution.
  • Blend principles with performance, showing that responsible data science requires both technical outputs and human-centered insight.
  • Connect directly to business objectives within a scenario’s context, reinforcing relevance to real-world roles. Used alongside data science tasks, RDSOs ensure that learners practice applying responsible data science—not just mastering tools.

Work culminates in a Learner’s Report, where students reflect on how context informed their decisions and how they balanced ethical and technical considerations to optimize outcomes.

C+DS items are built out into RDSO Scenarios that are broken down into Data Science Tasks with buildable skills.

Explore RDSOs

  • PGH

  • PGH.001 PGH.001

    • Data Quality

    Question

    What is the best way to extract data from a PDF?

    Learning Goal

    Comparing and evaluating data quality

  • PGH.002 PGH.002

    • Transparency
    • Privacy
    • Data Quality
    • Security

    Question

    Is AI currently a viable tool for data extraction from a technical standpoint? From a security and privacy standpoint?

    Learning Goal

    Evaluating trust in AI from multiple perspectives

  • PGH.003 PGH.003

    • Transparency
    • Accessibility
    • Impact Assessment

    Question

    How much of model building are you comfortable with being abstracted from you? Do you want to see things you don’t understand?

    Learning Goal

    Weighing black-box model skepticism with realistic assessment of knowledge

  • Parcels

  • Parcels.001 Parcels.001

    • Fairness
    • Transparency
    • Privacy
    • Ethics
    • Context Awareness
    • Accessibility

    Question

    Does visualizing parcels on a map facilitate responsible planning and stakeholder perspective?

    Learning Goal

    Considering how delivery of data affects response

  • Parcels.002 Parcels.002

    • Fairness
    • Transparency
    • Ethics
    • Context Awareness
    • Beneficence

    Question

    Is eligibility for consideration determined in an responsible manner? How or how not?

    Learning Goal

    Critically evaluating ethics of methodology

  • Parcels.003 Parcels.003

    • Fairness
    • Ethics
    • Data Quality

    Question

    Should the location of the parcels impact the selection process? Why or why not?

    Learning Goal

    Integrating multiple parameters into decision making

  • Parcels.004 Parcels.004

    • Transparency
    • Ethics
    • Context Awareness
    • Beneficence

    Question

    Walk through your decision-making process for identifying the top five parcels. What questions or concerns does your approach raise?

    Learning Goal

    Introducing intentionality into the decision-making process

  • Student Success

  • SSS.001 SSS.001

    • Beneficence
    • Criticality

    Question

    Can you explain the rationale behind your variable selections?

    Learning Goal

    Introducing intentionality into the decision-making process

  • SSS.002 SSS.002

    • Transparency
    • Impact Assessment

    Question

    How will you balance organizational goals with ethical data use?

    Learning Goal

    Balancing efficacy with responsibility