Robustness Testing

Real-world inputs are noisy, incomplete, and sometimes adversarial. LQA checks that the AI holds up under these conditions instead of failing once inputs stray from its training data.

• Adversarial and edge-case testing using intentionally crafted inputs\
• Noise injection for incomplete or messy inputs
• Out-of-distribution detection
• Metamorphic testing, small input changes shouldn't cause wild output swings

Fairness & Bias Mitigation Testing

A functional game can still lose players to FPS drops, slow loading, or crashes under pressure. LQA checks the performance conditions that affect stability, responsiveness, and release confidence.

• Demographic parity and equalized odds testing
• Counterfactual fairness testing
• Regular audits using diverse datasets
• Fairness toolkits such as IBM's AI Fairness 360

Transparency & Explainability Testing

Regulators and users need to understand why an AI reached a decision, especially in high-stakes domains like loan denials, medical diagnoses, or legal predictions, where "the AI said so" isn't acceptable. LQA validates that decisions can actually be explained, not just produced.

• Model-agnostic methods such as LIME and SHAP
• Attention mechanisms for neural networks and decision trees for interpretable models
• Comprehensive logging and audit trails
• Accuracy-versus-interpretability trade-off checks

Privacy & Data Protection Testing

GDPR and similar laws worldwide require AI systems to protect personal data, avoid leaking training data back out, minimize data use, and support "right to be forgotten" requests. LQA verifies the system holds up against all four.

• Membership inference testing
• Model inversion attack testing to prevent training-data extraction
• Differential privacy verification
• Encryption and access-control testing

Safety & Harm Prevention Testing

AI systems must not cause physical, psychological, financial, or societal harm, including harm no one anticipated. LQA checks for proactive risk controls, safety constraints, and oversight at every critical decision point.

• Hazard analysis using FMEA for AI
• Red team exercises to deliberately try to trigger harm
• Monitoring for toxic outputs and safety boundary testing
• High-risk scenario checks (e.g., a medical AI should never recommend dangerous drug combinations, even for novel cases)