jphall663/awesome-machine-learning-interpretability

Desired bibliographies

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  • Accessibility and Inclusion

    • Adaptive Technologies
    • Design Principles
    • User Studies

  • Algorithmic Bias

    • Sources of Bias
    • Mitigation Strategies
    • Impact Assessment

  • BibTeX (Various)

  • Cultural and Geographical Context

    • Localized Practices
    • Cross-Cultural Studies
    • Cultural Sensitivity

  • Data Privacy

    • Anonymization Techniques
    • Legal Frameworks
    • Privacy vs Utility

  • Environmental Impact

    • Energy Consumption
    • Sustainable Practices
    • Ecological Ethics

  • Ethical ML

    • Foundational Theories
    • Case Studies
    • Ethical Guidelines

  • Explainability and Interpretability

    • Technical Approaches
    • User Experience
    • Legal Requirements

  • Feminist Approaches to ML

    • Gender Bias
    • Feminist Theory
    • Empowerment

  • Historical Perspectives

    • Early Computational Models
    • Historical Context
    • Future Histories

  • Human-Centered ML

    • User-Centric Design
    • Human-in-the-Loop
    • Cognitive Psychology

  • Indigenous Perspectives

    • Data Sovereignty
    • Cultural Respect
    • Case Studies

  • ML in Healthcare

    • Clinical Applications
    • Ethical Considerations
    • Regulatory Landscape

  • Regulation and Policy

    • Current Legislation
    • Policy Proposals
    • Enforcement and Oversight

  • Social Impact of ML

    • Positive Impacts
    • Negative Impacts
    • Societal Discussions

  • Sociotechnical Systems

    • Integration Challenges
    • System Theory
    • Ethnographic Studies

  • Web (Various)