Testing Your AI Tools
Build a structured bias testing protocol for your HR AI tools, then review sample results on a mock dashboard to understand how pass/fail analysis works in practice.
Test Protocol Builder
Work through five steps to generate a complete bias testing protocol tailored to your organisation's AI tools and risk profile.
Results Dashboard Template
A mock dashboard showing how bias test results are displayed with pass/fail indicators per attribute and metric. Use this as a template for your own reporting.
Testing Frequency Recommendations
Key Insight: Bias Drifts When You Stop Looking
A tool that passes bias testing today can fail six months from now. AI models are regularly retrained on new data, and that new data reflects shifting patterns in hiring, performance reviews, and workforce demographics. Continuous testing is not optional -- it is essential.
Treat bias testing like financial auditing: schedule it, document it, assign accountability, and act on the results. If a tool fails a test, have a documented escalation path that includes suspending the tool's use until the issue is resolved.