Metadata
Interdisciplinary / Other Any Level Understand Medium
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Metadata

  • Subject

    Interdisciplinary / Other

  • Education level

    Any Level

  • Cognitive goals

    Understand

  • Difficulty estimate

    Medium

  • Tags

    algorithmic bias, fairness, ethics, data bias, mitigation, social impact

  • 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

    Evaluate learners' understanding of how algorithmic bias emerges (data, model, and human factors), techniques for detecting and measuring bias, ethical and legal implications, societal harms, and mitigation strategies through conceptual questions and scenario analysis.
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Remix
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Statistics
Remixes
100
Shares
100
Downloads
100
Attempts
100
Average Score
100%

Mock data used for demo purposes.