Metadata
Interdisciplinary / Other Any Level Analyze Hard
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Metadata

  • Subject

    Interdisciplinary / Other

  • Education level

    Any Level

  • Cognitive goals

    Analyze

  • Difficulty estimate

    Hard

  • Tags

    algorithmic bias, data collection, model design, deployment, fairness, auditing

  • 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 how choices in data collection, feature engineering, model objectives, evaluation metrics, and deployment practices produce algorithmic bias and disparate impacts across demographic groups; scope includes root-cause identification, intersectional analysis, measurement approaches, trade-offs, case-study evaluation, and designing mitigation, monitoring, and governance strategies.
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Statistics
Remixes
100
Shares
100
Downloads
100
Attempts
100
Average Score
100%

Mock data used for demo purposes.