Q1: Is there a clear governance framework (policies, roles, responsibilities) for this AI system throughout its lifecycle?

No formal framework. Basic policies exist, but roles are unclear. Formal framework with defined roles and accountability. Robust, continuously reviewed governance integrated into enterprise risk management.

Q2: Has an impact assessment (e.g., DPIA, AI Impact Assessment) been conducted for this AI system?

No, or planned but not yet done. Informal discussion, but no documented assessment. Formal assessment completed, identifying potential risks. Regular, comprehensive assessments with mitigation strategies implemented.

Q3: How is the training data for this AI system managed for quality, relevance, and representativeness?

Limited data quality checks, potential for unrepresentative data. Basic data cleaning, but representativeness is not consistently monitored. Rigorous data quality pipelines and regular checks for representativeness. Automated data governance, continuous monitoring for drift and bias, comprehensive data lineage.

Q4: What measures are in place to ensure data privacy and security for the AI system's data (training and operational)?

Basic security (e.g., passwords), limited privacy considerations. Standard IT security practices, some data anonymization efforts. Robust security controls (encryption, access control) and privacy-by-design principles applied. Advanced privacy-preserving techniques (e.g., differential privacy, federated learning) where applicable, continuous security audits.

Q5: How are potential biases in the AI model and its data identified and mitigated?

No specific bias detection or mitigation efforts. Ad-hoc manual checks, or basic fairness metrics applied post-training. Use of specialized bias detection tools and documented mitigation strategies during development. Proactive bias assessment from data collection to deployment, with continuous monitoring and automated mitigation workflows.

Q6: To what extent can the AI system's decisions or outputs be explained and understood by stakeholders?

Black-box model, difficult to explain decisions. Some basic insights (e.g., feature importance), but complex for non-experts. Use of Explainable AI (XAI) techniques (e.g., LIME, SHAP) and clear documentation for key decisions. High degree of interpretability by design, with user-friendly explanations tailored to different audiences.

Q7: How transparent is the AI system regarding its purpose, limitations, and potential impacts to users?

No clear disclosures or user notifications. Limited disclosure, often buried in terms and conditions. Clear, accessible explanations of the AI's purpose, capabilities, and known limitations provided to users. Proactive, contextual transparency, empowering users to understand and challenge AI decisions, with human-in-the-loop options where appropriate.

Q8: How are the AI system's robustness against adversarial attacks and its overall security evaluated?

No specific testing for adversarial attacks; basic cybersecurity measures. Occasional penetration testing; limited understanding of AI-specific vulnerabilities. Regular adversarial testing (e.g., evasion, poisoning attacks) and adherence to AI security best practices. Proactive threat modeling for AI, continuous security monitoring, and resilience mechanisms against novel adversarial techniques.