Moving Beyond Subjective Risk Assessment
Traditional IRB review often relies on subjective interpretations of "minimal risk." While valuable, this qualitative approach can lead to inconsistencies across different review boards and studies. IRB-GPT introduces a quantitative layer to ethical review by utilizing risk probability matrices derived from large-scale observable compute simulations.
The Risk Probability Matrix (RPM)
The RPM assigns a numerical value to potential adverse events based on two primary dimensions: Probability of Occurrence (P) and Magnitude of Harm (M). By plotting these on a grid, researchers can visualize the risk profile of their study.
Example Calculation: Breach of Confidentiality (Online Survey)
- Probability (P): Low (1/5) - Assuming secure platform usage.
- Magnitude (M): Moderate (3/5) - Potential social stigma if sensitive data revealed.
- Risk Score (R = P x M): 3 (Low Risk)
Compare to: Experimental Drug Trial (P: 2, M: 5, R: 10 - High Risk requiring full board review).
Observable Compute and Ethical Modeling
Our models are trained on data from observable compute experiments, which simulate thousands of potential research scenarios to identify edge cases where standard protocols might fail. This allows for a more robust prediction of "black swan" ethical events.
Key Metrics Evaluated:
- Vulnerability Index: Assessment of participant population (e.g., children, prisoners, cognitive impairment).
- Coercion Factor: Analysis of compensation vs. effort to detect undue influence.
- Data Sensitivity Score: Classification of data types (PII, PHI) against security protocols.
By integrating these mathematical models into the drafting process, researchers can proactively address ethical concerns before submission, significantly reducing review time.