SAFE-AI Framework for System-Level AI Safety

• SAFE-AI is a system-centric framework that integrates evaluation of AI safety across AI models, data pipelines, non-AI components, and environmental affordances.
• The framework introduces harmonized terminology, a structured taxonomy, and lifecycle mappings that assign clear accountability to multiple stakeholders.
• It provides an actionable end-to-end assessment workflow that enhances safety, governance, and auditability in complex, real-world AI deployments.

The SAFE-AI Framework is a system-centric paradigm for the evaluation and assurance of AI safety in complex, real-world deployments. Originating from the recognition that traditional, model-centric evaluation approaches are insufficient for modern AI systems—which comprise intertwined models, data pipelines, non-AI software, environmental affordances, and multi-stakeholder processes—SAFE-AI provides harmonized terminology, a structured taxonomy of evaluation elements, and lifecycle mappings that assign clear accountability across the AI supply chain. This framework establishes an end-to-end, accountable foundation for AI safety evaluation, emphasizing the interaction of all system components and environmental contexts rather than focusing solely on isolated models (Xia et al., 2024).

1. Motivation and Conceptual Foundation

SAFE-AI arises from the need to address emergent risks that occur beyond the model boundary, including failures due to complex component interactions, environment-induced behaviors, and distributed stakeholder responsibility. The framework rejects the sufficiency of metrics such as accuracy, robustness, or fairness at the model level alone, arguing instead for a system-level perspective where safety is a function of the entire deployed system and its contextual affordances (Xia et al., 2024). The issues identified include:

• Emergent safety failures from interaction between AI models, data pipelines, and non-AI software.
• Non-trivial influence of deployment context (such as access to tools, guardrails, and user behavior) on system safety.
• Multi-actor accountability where AI producers, deployers, users, and affected parties share safety obligations.

SAFE-AI directly addresses these by proposing a unifying framework for AI system evaluation that bridges AI, software engineering, and governance practice.

2. Harmonized Terminology and Formalization

To enable rigorous, cross-disciplinary AI system evaluation, SAFE-AI introduces precise formal definitions:

• AI System: $\text{AI\_System} = \{M, D, C, E\}$ where $M$ is the set of AI models; $D$ is data pipelines; $C$ is non-AI components such as software modules, APIs, UIs; $E$ is environmental affordances, such as external tools, guardrails, or deployment context.
• Subsystem: Any functionally cohesive subset of components from the AI System.
• Affordance: $$\{ a_i | \text{ specific external capability or constraint from } E \}$$, examples include access to external knowledge bases or runtime guardrails.
• Stakeholders: Enumerated as $$\{\text{Producer}, \text{Provider}, \text{Partner}, \text{Deployer}, \text{User}, \text{Affected}\}$$, each with distinct rights and safety responsibilities within the AI supply chain (Xia et al., 2024).

System elements and their relationships are thus fixed in a language precise enough to underwrite lifecycle accountability and traceable safety evaluation.

3. Taxonomy of Evaluation Elements

SAFE-AI partitions the evaluation space along two dimensions (component-level and system-level) and three objectives (Quality/Risk, Accuracy/Correctness, Capability):

• Component-Level ($\mathcal{C}$):
  • Data: $$\{\text{Quality}, \text{Fairness}, \text{Privacy}\}$$
  • Model: $$\{\text{Quality\_Risk}, \text{Accuracy}, \text{Capability}\}$$
  • Quality_Risk subsumes robustness, security, fairness analytics
  • Accuracy covers classical metrics (precision, recall, F1, BLEU) on held-out test suites
  • Capability includes benchmarking and red-teaming targeted at emergent behavior
  • Guardrails: $M$0
• System-Level ($M$1):
  • Narrow AI System: $M$2
  • General AI System: $M$3 (with explicit treatment of environmental affordances $M$4)

The total taxonomy set is $M$5 (Xia et al., 2024).

4. Lifecycle Mapping, Stakeholder Roles, and Accountability

SAFE-AI connects evaluation requirements to project lifecycle stages and stakeholder responsibilities through a mapping function:

$M$6

where “Stage” ∈ {Design, Development, Deployment, Monitoring}. For each $M$7 pair, $M$8 identifies the subset of evaluations required. For example:

• $M$9
• $D$0
• $D$1 (affected parties are only considered via impact assessment)

This mapping structures accountability, making explicit not only what evaluations are required, but which actor or organization is responsible for each at every point in the system lifecycle.

5. Integrated Assessment Workflow and Example

The SAFE-AI framework is operationalized through a workflow that weaves together terminology, taxonomy, and lifecycle mapping:

1. Harmonization: Define system elements, stakeholders, lifecycle stages, and key definitions.
2. Taxonomy: Instantiate all relevant evaluation elements for the specific system.
3. Mapping: Assign evaluation tasks to all stage-stakeholder pairs.
4. Execution: Sequentially run evaluations as dictated by lifecycle ordering, aggregating results into a consolidated Safety Evaluation Report.

This process can be formalized in pseudocode:

$D$3

An illustrative application to an autonomous parking assistant demonstrates practical instantiation: system elements (object detection, path planning, interfaces, guardrails); lifecycle-anchored tasks (e.g., Data Quality and Model Quality_Risk at design, System Accuracy_Correctness at deployment); and integrated reporting of component and system-level metrics, risk scores, and mitigations (Xia et al., 2024).

6. Comparison with Model-Centric Approaches

SAFE-AI diverges fundamentally from classic model-centric safety evaluation, which typically:

• Restricts scope to the model component ($D$2), evaluating only metrics such as accuracy or fairness on fixed datasets.
• Neglects the influence and vulnerabilities introduced by environmental affordances, deployment context, and non-AI system layers.
• Lacks explicit mapping of safety responsibilities to stakeholders and lifecycle phases.

By contrast, SAFE-AI:

• Mandates inclusion of data pipelines, non-AI system software, and situational affordances.
• Structures safety evaluation as an end-to-end, lifecycle-spanning process with clear assignment of evaluation duties and ownership.
• Produces safety reports reflecting both technical (e.g., benchmarks, red-teaming, impact assessments) and governance (e.g., stakeholder accountability, standards alignment) perspectives.

Adoption of SAFE-AI supports a more complete, real-world-aligned, and auditable safety posture for advanced AI deployments (Xia et al., 2024).

7. Implications and Significance

SAFE-AI provides an operational backbone for integrating safety engineering, software practice, and organizational governance in the era of system-integrated, high-capability AI. It facilitates communication across disciplinary boundaries via standardized terminology, anchors safety requirements in structured, accountable workflows, and highlights emergent risks from full-system interactions and context. As AI systems grow in complexity and influence, frameworks of this form are essential for credible, transparent, and reliable societal deployment (Xia et al., 2024).