Safety-Critical Disclosures

• Safety-critical disclosures are systematic reports detailing vulnerabilities, failures, and risk mitigations in high-stakes tech, distinguished by pre- and post-mitigation evaluations.
• They employ rigorous methodologies including Bayesian inference and operational data benchmarking to quantify performance and ensure regulatory accountability.
• Studies reveal industry challenges like proprietary data concerns while advocating for standardized transparency protocols that balance public safety and competitive interests.

Safety-critical disclosures refer to the systematic reporting and sharing of information, metrics, and evidence about risks, failures, and mitigations relevant to the safe operation of high-stakes technological systems. In domains such as autonomous vehicles (AVs), LLMs deployed in clinical or high-risk settings, and frontier AI systems with possible dual-use capabilities, both the scope and cadence of such disclosures are central to regulatory oversight, public accountability, and technical progress.

1. Definitions and Key Concepts

Safety-critical disclosures encompass both public-facing and regulator-oriented reports detailing system vulnerabilities, operational failures, and the efficacy of risk mitigating controls. In the context of frontier AI, disclosures articulate the distinction between pre-mitigation (model capabilities “out of the box,” prior to any safety alignment) and post-mitigation (after all safety interventions are deployed) risk evaluations (Bowen et al., 17 Mar 2025). For AVs and other SCSs, disclosures include quantified operational reliability claims backed by rigorous inferential methods, such as conservative Bayesian inference applied to safety-related events and prior engineering evidence (Zhao et al., 2020).

Within the AV sector, reporting safety-critical scenarios—where rare or adversarial disturbances may precipitate system failure or endanger human life—has emerged as vital. These scenarios encompass both “static” (input-space, e.g., adversarial patch attacks, sensor noise, environmental perturbations) and “dynamic” (behavioral, e.g., adversarial agent maneuvers, scenario generation via RL) taxonomies (Li et al., 31 Mar 2025).

2. Safety Disclosure Protocols: Frameworks and Benchmarks

Frontier AI safety disclosure protocols stress staged evaluation and transparent reporting over a portfolio of risk domains, typically organized as follows (Bowen et al., 17 Mar 2025):

• Pre-mitigation evaluation: Measures the “raw” risk surface prior to intervention (e.g., accuracy on dangerous capability benchmarks such as WMDP-Chem).
• Post-mitigation evaluation: Measures model performance after safety fine-tuning, RLHF, SFT, classifier filters, etc.; quantifies refusal or compliance with high-risk prompts.
• Risk assessment metrics: Canonical ratios include Accuracy_pre (pre-mitigation accuracy on risk benchmarks), Compliance_post (fraction of “dangerous” prompts answered post-mitigation), and Refusal_post (complement of compliance).
• Statistical grounding: Minimum query counts (n ≥ 30), reporting of point estimates with confidence intervals, and summary tables for threshold comparisons (e.g., “Pre-mitigation accuracy on WMDP-Chem: 70% (≥60% threshold?)”).

Disclosures to regulators include full prompt logs, model weights or secure API access, evaluation harnesses, and mitigation methodology details, while public disclosures focus on threshold-based summaries, benchmark details, and sanitized datasets.

For AVs, formal claims around operational safety are grounded in inferential frameworks that balance prior engineering knowledge (e.g., design goals, theoretical lower-bound failure rates) and empirical observations (crashes, fatality-free miles), leveraging quantile-based priors rather than full parametric Bayesian densities (Zhao et al., 2020).

3. Safety Evaluation in High-Risk Operational Contexts

In high-risk contexts such as clinical mental health disclosures to LLMs, safety-critical disclosure entails both quantitative coding of response behaviors and detailed qualitative reporting of failure cases (Shah et al., 1 Sep 2025). A prominent evaluation framework leverages five binary-coded safety behaviors: explicit risk acknowledgment, empathy, encouragement to seek help, provision of specific resources, and invitation to continue the conversation. Scores are averaged to yield a global safety score $G_m$ per model:

$G_{m} = \frac{1}{5} \sum_{c=1}^{5} S_{m,c}$

Component-level annotation protocols employ multiple expert raters, inter-rater agreement (e.g., $\kappa$ statistics), and formal conversion of categorical codes to continuous scores for aggregative analysis.

In AVs, system-level metrics—collision rate, off-road distance, route deviation, and route completion—are disclosed in both benign and adversarially-generated safety-critical scenarios (Li et al., 31 Mar 2025). Perception module vulnerabilities are quantified by mAP drops under digital/physical attacks and distribution shifts.

4. Barriers and Incentive Structures in Safety-Critical Data Sharing

Empirical research into AV industry practice reveals two primary barriers to safety-critical data sharing (Sandhaus et al., 10 Apr 2025):

1. Embedded knowledge as resource-intensive property: AV datasets confer salient, high-value knowledge critical to system safety and future design choices. The political and economic costs of disseminating such data—internally and externally—often outweigh the perceived public good.
2. Proprietary advantage: Safety knowledge extracted from crash data is regarded as competitive intellectual property rather than a public good, incentivizing secrecy over cooperative advancement.

Practitioners consistently view detailed crash/near-crash data as proprietary, emphasizing that these datasets reveal downstream ML architecture, failure modes, and internal protocols. The distinction between public goods (anonymized summaries) and private goods (sensor, model-specific data) underpins current industry disclosure reluctance.

5. Standardization and Policy Evolution

Recent policy proposals advocate standardized, mandatory disclosure of both pre- and post-mitigation safety evaluations to approved governmental bodies, accompanied by minimum transparency for public safety reporting (Bowen et al., 17 Mar 2025). Such protocols:

• Facilitate risk-based deployment decisions by mapping models/systems into defined risk quadrants (e.g., safe for open release vs. API-only vs. restricted).
• Strengthen auditability and accountability through external regulatory scrutiny and quantified reporting thresholds.
• Support legislative and regulatory implementation, as reflected in the EU AI Act’s forthcoming requirements for dual-stage risk reporting.

In the field of AV data, there are calls for multi-stakeholder forums to stratify data into public (anonymized summaries), restricted (scenario meta-data), and private (raw annotated streams) tiers, coupled with enabling tools (federated learning, scenario-based simulation) and incentive structures (regulatory credits, academic intermediaries) (Sandhaus et al., 10 Apr 2025).

6. Methodological Rigor in Safety Claim Disclosure

Mathematically rigorous approaches to disclosure, such as Conservative Bayesian Inference (CBI), formalize the relationship between prior engineering evidence and empirical test results. Priors are constructed via quantile constraints (probability $\theta$ that pfm $p\leq\epsilon$, lower bound $p_l$) rather than arbitrary parametric choices. Safety claims are then conservatively bounded by the worst-case posterior across all admissible priors:

$$\inf_{F\in\mathcal{D}} \Pr_F[p\leq p^* | k, n] = \frac{x_1^k (1-x_1)^{n-k} \theta}{x_1^k (1-x_1)^{n-k}\theta + x_3^k (1-x_3)^{n-k}(1-\theta)}$$

This framework avoids optimism and enables reporting of safety claims backed analytically by both partial prior knowledge and operational evidence (Zhao et al., 2020).

7. Future Directions and Open Questions

Contemporary research emphasizes the need for:

• Expanded, multi-turn and multi-language safety evaluations, especially in clinical LLM deployment (Shah et al., 1 Sep 2025).
• Scenario generation/expansion methods that yield more realistic and diverse safety-critical domains, utilizing world-model simulators and diffusion-based generators (Li et al., 31 Mar 2025).
• Socio-technical mechanisms to adjudicate between public and private knowledge, balancing the societal imperative for transparency against proprietary incentives (Sandhaus et al., 10 Apr 2025).
• Legislative adoption of standardized, dual-stage safety disclosure, embedding confidence interval–backed metrics, deployment thresholds, and clear stakeholder roles (Bowen et al., 17 Mar 2025).

Safety-critical disclosures thus represent a convergent area at the interface of formal verification, empirical science, systems engineering, and public policy, with ongoing evolution in frameworks, disclosure standards, and epistemic norms shaping the future of high-stakes technology governance.