Artificial Intelligence Testing
AI testing is the process of evaluating artificial intelligence systems to ensure they function accurately, efficiently, and ethically. It involves validating machine learning models, checking for biases, and ensuring AI-driven applications perform as expected. Key testing methods include functional testing, performance testing, security testing, and bias detection. AI testing helps improve model accuracy, enhance user trust, and prevent unintended outcomes. With the rapid adoption of AI in various industries, rigorous testing is essential to maintain reliability, fairness, and compliance with regulatory standards.
Multiple testing techniques:
- Functional Testing – Validates whether the AI system meets intended requirements.
- Performance Testing – Checks speed, efficiency, and scalability under various workloads.
- Bias & Fairness Testing – Ensures AI models do not exhibit discriminatory behavior.
- Security Testing – Identifies vulnerabilities to prevent cyber threats and data breaches.
- Data Validation – Ensures high-quality training data for improved model accuracy.