Terms and Conditions

DISCLAIMER AND LIMITATION OF LIABILITY

The A-OSP (Augmented Ontological-Semantic Platform) framework is provided as open-source software to assist small and medium-sized enterprises (SMEs) in performing structured self-assessments regarding regulatory compliance. The framework incorporates computational components, structured knowledge bases, and semantic-normative inference modules powered by artificial intelligence (via LLM API integrations).

USER RESPONSIBILITIES AND ACCEPTANCE OF TERMS

The A-OSP framework is provided “as is,” without warranties or guarantees of any kind. By using the framework, users fully accept the MIT open-source license terms and explicitly acknowledge the following conditions:

• No Legal Advice: Outputs, analyses, and interpretations generated by A-OSP’s AI-based components must not be considered as professional legal advice, nor do they replace the opinion or guidance of licensed legal professionals.

• No Warranty of Regulatory Compliance: The adequacy, accuracy, and completeness of regulatory compliance assessments must be independently verified and formally approved by the appropriate corporate governance bodies (e.g., Board of Directors, Supervisory Boards, Compliance Officers).
  
  

TRACEABILITY AND METHOD CERTIFIABILITY

The A-OSP project team explicitly ensures:

• The methodological approach is structured, well-documented, and epistemically traceable at every analytical step.

• Each intermediate and final artifact generated by the framework (e.g., risk assessments, the Modello 231 document, gap analyses) can be traced directly back to original inputs and computationally replicable inferences.

• The entire analytical process and generated documentation can be transparently provided in legal or regulatory proceedings, offering clear evidence to support organizational diligence and regulatory compliance efforts.
  
  

DEPENDENCE ON HUMAN INPUT AND VALIDATION

The quality, accuracy, and legal validity of compliance artifacts produced by A-OSP critically depend on:

• The correctness, precision, truthfulness, and intellectual honesty of responses provided during the initial self-assessment phase.

• Mandatory human review, adjustment, validation, and contextual integration of intermediate and final compliance artifacts by qualified corporate roles (e.g., Board of Directors, Compliance Officers, Supervisory Bodies).

• Contextual adaptation and legal interpretation of AI-generated recommendations to ensure alignment with the company’s specific circumstances and applicable regulatory environment.
  
  

AUDITABILITY AND EPISTEMIC TRANSPARENCY

All processes within A-OSP are systematically recorded and structured, including:

• Raw data inputs provided by users (original assessment data).

• Intermediate semantic-normative analysis chunks (231chunks).

• Requests to and responses from AI models.

• Comprehensive system-generated logs.
  
  

This structured approach enables organizations to fully reconstruct the epistemic decision-making chain of each recommendation or assessment provided by the system. This explicit epistemic transparency significantly reduces interpretative ambiguity and strengthens legal defensibility in case of regulatory inquiries or disputes.