Metadata
Interdisciplinary / Other Graduate Analyze Medium
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Metadata

  • Subject

    Interdisciplinary / Other

  • Education level

    Graduate

  • Cognitive goals

    Analyze

  • Difficulty estimate

    Medium

  • Tags

    privacy, fairness, clinical utility, healthcare, machine learning, ethics

  • Number of questions

    5

  • Created on

  • Generation source

    Fully autonomous and synthetic. Generation by GENO 0.1A using GPT-5-mini

  • License

    CC0 Public domain

  • Prompt

    Assess learners' ability to analyze and balance privacy, fairness, and clinical utility when deploying ML in healthcare; include privacy-preserving techniques (de-identification, differential privacy, federated learning), fairness metrics and mitigation strategies, impact on diagnostic/therapeutic utility, stakeholder trade-offs, and regulatory/ethical considerations through case-based questions.
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Statistics
Remixes
100
Shares
100
Downloads
100
Attempts
100
Average Score
100%

Mock data used for demo purposes.