Research Questions¶
This study examines RGDS across ten questions. Each question is analyzed with supporting evidence from the 96-source bibliography, quantified where the evidence supports it, and scoped explicitly where it does not.
Structure of Each Question¶
Each research question page follows a consistent structure:
- Answer in brief — the core finding in one paragraph
- Evidence base — quantified claims with inline citations
- Mechanism — how the proposed intervention produces the stated outcome
- Limitations — what the analysis does not address
- Implementation guidance — what a practitioner needs to act on the finding
The Ten Questions¶
Foundational (Q1–Q2): The reconstructability crisis and its AI-governance analog.
- Q1: Decision Reconstructability — How can organizations reconstruct decision logic when FDA requests justification months or years after decisions were made?
- Q2: AI Accountability — How can AI-assisted regulatory processes preserve human accountability while leveraging AI's efficiency gains?
Operational (Q3–Q5): Integration overhead, deficiency prevention, and decision cycle compression.
- Q3: Integration Without Overhead — How can decision governance frameworks integrate with existing biopharma/biotech project management practices?
- Q4: FDA Deficiency Prevention — How can schema-validated decision logs reduce FDA deficiency rates and clinical hold risks?
- Q5: Decision Velocity — How can decision cycle time be compressed while maintaining decision quality and regulatory defensibility?
Strategic (Q6–Q8): Alignment, ROI, and implementation sequencing.
- Q6: Strategic Alignment — How will decision governance reshape regulatory interactions, investor due diligence, and board governance?
- Q7: ROI Measurement — How can organizations measure the ROI of decision governance infrastructure across portfolio-level timelines and outcomes?
- Q8: Implementation Roadmap — How should implementation roadmaps balance pilot validation, organizational integration, and positioning?
Regulatory (Q9–Q10): FDA disclosure requirements and mandate trajectory.
- Q9: AI Governance Disclosure — How can AI governance disclosure frameworks satisfy evolving FDA expectations for transparency in algorithmic decision-making?
- Q10: Regulatory Mandate Trajectory — How should regulatory frameworks evolve to mandate or incentivize decision documentation as standard practice?