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Digital Ethics Society

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Digital Ethics Society

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Research Frontiers in AI Transparency and Explainability Standards

Developing frameworks for making black-box AI systems interpretable and accountable to users and regulators.

Interpretability Paradox: When Explanations Obscure Model Intent
Standardizing Opacity: Metrics for Quantifying AI Black Boxes
Explainability Across Cultural and Epistemic Boundaries
Adversarial Transparency: Gaming and Manipulating Explanations
Temporal Drift in Model Accountability and Feature Attribution
Stakeholder-Specific Explanations: Technical vs Narrative Intelligibility
Regulatory Capture in AI Transparency Standards Development
Mechanistic Interpretability Beyond Post-hoc Rationales
Trust Calibration: When Explanations Exceed Actual Reliability
Right to Explanation Enforcement in Decentralized AI Systems

All Digital Ethics & Society PhD categories