Opacity (industry opacity)
Opacity is not solely due to the technological complexity that limits developers’ and users’ understanding of how generative models function on a technical level. It is further exacerbated by the practices of organizations and companies that are advancing the field. Many are private companies that choose to withhold from the public many of the precise characteristics of their most advanced models.
ENTITY
1 - Human
INTENT
1 - Intentional
TIMING
3 - Other
Risk ID
mit728
Domain lineage
6. Socioeconomic and Environmental
6.4 > Competitive dynamics
Mitigation strategy
1. Mandatory Regulatory and Governance Standards: Implement and enforce industry-wide regulatory frameworks (e.g., the EU AI Act or NIST AI RMF principles) that mandate comprehensive disclosure requirements for generative AI models. This must cover training data provenance, model architecture, and risk assessment methodologies to counteract the competitive incentive to withhold information and establish a systemic baseline for accountability. 2. Deploy Advanced Explainability and Monitoring Technologies: Require the integration of Explainable AI (XAI) techniques (e.g., LIME or SHAP) to provide clear, contestable rationales for model decisions (interpretability). Simultaneously, establish robust, continuous Monitoring, Reporting, and Verification (MRV) systems throughout the model lifecycle to track performance, detect algorithmic bias, and ensure ongoing compliance, transforming opaque model outputs into verifiable evidence. 3. Strengthen Third-Party Due Diligence and Contractual Transparency: For all externally sourced or vendor-supplied generative models, implement stringent Third-Party Risk Management (TPRM) protocols. This must include contractual mandates for access to critical model documentation, audit rights, timely notification of model updates, and the provision of comprehensive validation studies to mitigate the systemic operational risks inherent in relying on proprietary, black-box systems.