Evaluation & methodology
The thesis proposes a mixed‑methods research program: community‑grounded qualitative work plus formal verification, simulation, and statistical robustness testing.
Qualitative grounding
- Ethnographic interviews with community advocates, regulators, and model developers
- Participatory design workshops to co‑create restorative remedies and legitimacy weighting schemes
- Design justice principles so marginalized voices drive architectural decisions
Quantitative + formal rigor
- Agent‑based simulations that model validator behavior under strategic incentives
- Monte Carlo analyses of observability noise to quantify detection reliability under adversarial perturbations
- Counterfactual reasoning to evaluate social outcomes of enforcement choices
- Theorem‑proving and formal verification of DSL semantics
- Usability studies to assess accessibility of validator tooling
External benchmarking (design hypothesis)
The thesis describes a potential future benchmarking exercise—e.g., with independent auditors—sampling enforcement cases across finance and healthcare and comparing outcomes to statutory benchmarks.
A suggested target is that ledgered outcomes could match legal standards (e.g., EU AI Act and U.S. Fair Credit Reporting Act) with high concordance while also documenting remediation effectiveness that exceeds regulatory minimums.
| Element | What it tests |
|---|---|
| Case sampling | Randomly sampled enforcement cases across pilots (e.g., finance + healthcare) |
| Concordance | How often AMME decisions align with statutory benchmarks and audit expectations |
| Remediation outcomes | Whether restorative actions produce measurable improvement beyond minimum compliance |
| Transparency | Machine‑readable logs enabling independent review and replay of contested decisions |
Gauge integration layer
The treatise uses “Gauge integration” to describe a cooperative observability bridge between AMME and the Gauge AI platform. In plain terms: observability signals help spot harm; AMME’s enforcement delivers concrete fixes such as retraining and restitution.
Data ethics canon
The thesis proposes a jointly maintained Data Ethics Canon: a living document outlining acceptable data practices, cultural considerations, and escalation procedures—co‑authored with community representatives and updated annually.
Regulatory mapping
Connectors can map observability metadata to EU AI Act risk categories and tag deliberation logs with IEEE P7000 principles, narrowing the gap between live enforcement and formal certification reporting.