AI App Testing Webinar: Bias, Trust, Scale — Full Recording and Key Insights

AI systems don’t fail like regular apps — they fail quietly, unpredictably, and sometimes unfairly. In our recent webinar, QA experts Twinkle Joshi, Michael Tomara, and moderator Bruce Mason shared practical methods for testing AI applications for bias, trust, and scale, with an eye toward the upcoming EU AI Act and its compliance demands.

If you missed the live session or want to revisit the key takeaways, we’ve summarized the main points below — including insights on bias detection, explainability, and continuous validation. You’ll also find the full webinar recording at the end of this post.

Why Traditional QA Fails for AI Systems

AI applications don’t behave like traditional software — they learn, adapt, and make probabilistic decisions based on data patterns. This fundamental difference means your standard testing playbook needs a complete overhaul. Teams need to test not just for functional correctness, but for fairness, transparency, and ethical implications.

Words by

Twinkle Joshi, Senior Software Engineer & Lead QA Automation Expert

“A model showing 94% accuracy can still systematically discriminate against protected groups. We need to look beyond aggregate metrics to understand how our AI behaves across different demographics.”

Detecting and Preventing Algorithmic Bias

The speakers demonstrated practical bias detection using tools like AI Fairness 360 and demographic parity testing. Rather than treating bias as a binary pass/fail metric, they emphasized continuous monitoring across multiple fairness definitions — because what’s “fair” often depends on context and stakeholder perspectives.

Making the Black Box Transparent

Explainable AI isn’t just about regulatory compliance — it’s about building systems your users can trust. The panel showcased real-world applications of SHAP and LIME tools, showing how to decode model decisions and present them in ways that both technical and non-technical stakeholders can understand.

Words by

Michael Tomara, QA Lead, TestFort

“Every unexplainable decision is a trust deficit. We’ve seen teams lose months of work because they couldn’t explain why their model rejected certain loan applications. Explainability testing needs to be baked in from day one.”

EU AI Act Compliance: Beyond the Checkboxes

With penalties up to 7% of global revenue, the EU AI Act isn’t something you can ignore. The speakers broke down the key requirements: transparency reports, risk assessments, and human oversight mechanisms. More importantly, they shared how to integrate these requirements into your existing development workflow without grinding progress to a halt.

Building Continuous Validation Pipelines

AI testing doesn’t stop at deployment. The panel outlined essential components of continuous validation:

• Model retraining triggers based on degradation thresholds

• Data quality monitoring to catch drift before it impacts users

• Adversarial testing to probe model boundaries and failure modes

• Performance tracking across demographic segments, not just averages

• Feedback loops that capture real-world edge cases

Words by

Bruce Mason, Delivery Director, TestFort

“The biggest mistake we see is teams treating AI testing as a one-time gate. Your model’s behaviour changes with every new data point — your testing approach needs to evolve just as continuously.”

How to Start Testing AI Responsibly Today

The speakers emphasized starting small but thinking systematically. Their recommended approach:

1. Audit your current testing to identify AI-specific gaps;
2. Implement basic fairness metrics for your highest-risk features;
3. Add explainability checks to your model review process;
4. Document decision-making logic for compliance;
5. Build monitoring dashboards that track beyond accuracy;
6. Create adversarial test suites that probe edge cases;
7. Establish clear escalation paths for AI-related issues.

Metrics That Matter for AI Testing

To demonstrate AI testing value and ensure compliance, track these key indicators:

• Demographic parity ratios across protected groups;
• Explanation consistency scores;
• Adversarial robustness percentages;
• Data drift detection rates;
• Time to identify and mitigate bias;
• Compliance documentation completeness;
• Human-in-the-loop intervention frequency.

Watch the Full Webinar Recording

Ready to dive deeper into AI testing strategies? Watch the complete discussion, including live Q&A where our experts tackled specific challenges from attendees working with LLMs, computer vision, and recommendation systems:

Need help implementing these AI testing strategies? Our team offers AI Testing Audits, comprehensive AI Application Testing services, and expert Testing Consulting to help you build responsible, compliant AI systems. Contact us to ensure your AI applications are ready for the trust and scale challenges of 2026.