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🛣️ Specializing in AI Testing

Data Quality and Preparation​

  • Data bias: Identifying and mitigating bias in datasets.
  • Data cleaning and preprocessing: Ensuring data is suitable for AI models.

Model Evaluation & Metrics​

  • Accuracy, Precision, Recall, F1-score: Understanding when to use which metric.
  • ROC Curves, Confusion Matrices: Visualizing model performance.
  • Overfitting/Underfitting: Diagnosing and addressing these problems.

AI-Specific Testing Challenges​

  • Explainability: Testing the reasoning behind AI decisions.
  • Robustness: Testing how models handle unexpected or adversarial inputs.
  • Fairness: Ensuring AI systems don't perpetuate discrimination.