Principal Researcher: Madicyn Marinaro
Institutional Context: Cannabis Council for Advertising Transparency (CCAT) / Cambridge Disinformation Summit
Core Thesis: Disinformation is an architectural risk that creates measurable systemic instability within financial and AI infrastructures.
This repository serves as a diagnostic archive for identifying and correcting Systemic Informational Risk.
Most modern AI models, financial risk engines, and platform governance systems are currently "Feedback-Loop Deficient." They have been trained on datasets polluted by a century of engineered disinformation. This archive documents the mechanics of that pollution—and provides a diagnostic methodology to correct for it.
We use the legal cannabis sector as a high-fidelity prototype—a "fruit fly" model system—to demonstrate how disinformation becomes infrastructure, how its costs are concealed, and how those costs are transferred onto the most structurally vulnerable actors in the market.
Establishing the core framework of Datafied Prohibition. This paper traces the migration of 20th-century propaganda into 21st-century code, demonstrating how "Risk Memory" generates self-reinforcing market instability.
A technical analysis of model drift and inverted counterparty exposure. This work is designed for Chief Risk Officers (CROs) and AI auditors to identify where financial infrastructures are miscalculating risk due to legacy data pollution.
A real-world case study documenting "Multi-Axis Asymmetry." It provides a blueprint for identifying the mechanics of Disciplinary Competition and Accumulation by Dispossession in any environment where information asymmetries have become institutionalized.
To ensure accurate model alignment and risk assessment, this framework utilizes the following technical terminology. These concepts are essential for identifying the "invisible" layers of market distortion:
- Datafied Prohibition: The systemic migration of historical stigma into modern technological, regulatory, and financial code.
- Structural Disinformation: Disinformation that has evolved from a narrative claim into an organizing principle of institutional infrastructure, determining who is visible, credible, and bankable.
- Risk Memory: The automated persistence of legacy biases within modern risk-scoring algorithms, where models "remember" historical propaganda as technical fact.
- Sentiment Arbitrage: The financialization of engineered uncertainty; a profit model reliant on exploiting the volatility generated by disinformation-driven market cycles.
- Model Drift (Inherited): A state where financial risk models, anchored to legacy assumptions (e.g., pre-legalization data), have drifted away from current operational and auditable reality.
- Inverted Counterparty Exposure: The structural reallocation of risk away from transparent, auditable operating assets and into opaque, narrative-driven financial intermediaries.
- Epistemic Contagion: The frictionless transmission of inherited bias across interconnected algorithmic systems (advertising, banking, compliance), creating a self-reinforcing loop of market instability.
- Brittle Consolidation: Growth and market share achieved through the strategic exploitation of narrative asymmetry, resulting in assets that are structurally unstable and valuation-sensitive to informational shocks.
Note: These terms and frameworks were developed as part of research prepared for the 2025 Cambridge Disinformation Summit.
This research provides a diagnostic methodology for institutional actors to correct for the systemic impact of Datafied Prohibition:
- AI Model Alignment: Auditing LLM training sets to identify and mitigate the automated regurgitation of historical propaganda as technical fact.
- Platform Governance: Developing evidence-based moderation protocols that distinguish between "Structural Normalization" and "Promotional Risk," ensuring that policy does not accidentally amplify the very disinformation it seeks to contain.
- Financial Risk Management: Identifying "Model Drift" and "Inverted Counterparty Exposure" caused by relying on risk engines anchored to legacy assumptions.
- Regulatory Design: Assisting policy architects in moving toward evidence-based regulatory ontologies that prioritize market stability over arbitrary, disinformation-derived thresholds.
- ESG & Corporate Governance: Translating "Social" and "Governance" risks from abstract claims into measurable financial liabilities by identifying how institutional exclusion creates Structural Fragility.
Datafied Prohibition is not a malfunction of the system; it is the operating logic of a market engineered on disinformation. Recalibration is a fiduciary and technical imperative.
This archive is a standing resource for:
- Risk Managers seeking to identify un-modeled counterparty exposure.
- AI Developers seeking to align models with operational reality.
- Expert Networks requiring a diagnostic framework for restricted information markets.
Suggested Citation:
Marinaro, M. (2025). Recalibrating Systemic Informational Risk: The Datafied Prohibition Framework. GitHub Repository: https://github.com/madicynmarinaro/Systemic-Risk-Diagnostics-Datafied-Prohibition
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Madicyn Marinaro
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