title: Ensuring AI Reliability & Safety theme: white transition: slide class: center, middle
Supporting APS Agencies
Reliability & Safety in AI Systems
How BixeLab Assists Under the AI Assurance Framework – Section 5
Our Role as a Test Lab
At BixeLab, we help APS agencies evaluate and assure:
- AI reliability under real-world conditions
- Data suitability and traceability
- System safety, fairness, and accountability
Aligned with:
✅ AI Assurance Framework
✅ GovAI platform
✅ AI Technical Standards
5.1 Data Suitability – How We Help
We assess data quality to ensure it is fit for purpose:
🔹 Accuracy, completeness, and consistency
🔹 Provenance, lineage, and labelling integrity
🔹 Volume sufficiency for reliable performance
✅ We provide structured data suitability reports
✅ We flag risks in training, testing, and evaluation datasets
5.2 Indigenous Data – How We Help
When systems involve Indigenous data or impact Indigenous peoples:
🔹 We advise on technical alignment with the Framework for Governance of Indigenous Data
🔹 We work with cultural governance experts to inform test protocols
🔹 We identify risks of misrepresentation or misuse
✅ We assist with evidence for ethical compliance and respectful design
5.3 Suitability of Procured Models – How We Help
If you're using open-source, vendor, or custom models:
🔹 We run external benchmarking and stress testing
🔹 We identify known model limitations and architecture risks
🔹 We validate documentation, versioning, and traceability
✅ Independent suitability assessments for procurement or reuse
✅ Support model selection in the ICT Investment Process
5.4 Testing – How We Help
We design and execute testing aligned with APS use cases:
🔹 Functional testing and performance benchmarking
🔹 Bias and edge-case evaluations
🔹 Liveness detection and spoof resistance (for biometric AI)
✅ We deliver test plans, traceable results, and corrective guidance
✅ Testing against APS standards, ISO/IEC, NIST
5.5 Pilots – How We Help
🔹 We support pre-deployment pilots with sandbox or live-in-field testing
🔹 We help agencies evaluate:
- Stability
- Failover handling
- Feedback mechanisms
🔹 We assist with pilot planning and lessons-learned reviews
✅ Pilot outcome analysis & system refinement guidance
5.6 Monitoring – How We Help
We help define monitoring frameworks for APS operations:
🔹 KPIs for drift, failure rates, demographic discrepancies
🔹 Threshold setting and alerting mechanisms
🔹 Re-validation triggers and audit log requirements
✅ Custom monitoring plans tailored to agency risk appetite
✅ Integration with BAU assurance cycles
5.7 Human Oversight & Disengagement – How We Help
🔹 We define safe fallback and disengagement pathways
🔹 We review decision traceability and override protocols
🔹 We validate human-in-the-loop controls
✅ Structured playbooks for intervention readiness
✅ Assist with documentation for contestability & accountability
Summary: Where We Add Value
| Element | Our Contribution |
|---|---|
| Data Suitability | Structured evaluation and risk analysis |
| Indigenous Data | Technical & ethical assurance |
| Model Suitability | Independent testing and benchmarking |
| Testing | End-to-end scenario-based assessments |
| Pilot | Real-world validation and adjustments |
| Monitoring | Operational risk detection frameworks |
| Disengagement | Oversight, intervention and safety plans |
Let’s Work Together
We help APS teams build AI systems that are reliable, fair, and ready for public service.
📍 Based in Canberra
📐 ISO/NIST-aligned methods
🔍 Trusted by governments globally
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