Testing

Question	Example response
What are the testing requirements (for example, unit testing, integration testing, end-to-end testing)?	Unit testing for individual components, integration testing with external systems, end-to-end testing for critical scenarios, and so on.
How do you ensure data quality and consistency across different sources for generative AI training?	We maintain data quality through automated data profiling tools, regular data audits, and a centralized data catalog. We've implemented data governance policies to ensure consistency across sources and to maintain data lineage.
How will the generative AI model be evaluated and validated?	By using a holdout dataset, human evaluation, A/B testing, and so on.
What are the criteria for evaluating the performance and accuracy of the generative AI model?	Precision, recall, F1 score, perplexity, human evaluation, and so on.
How will edge cases and corner cases be identified and handled?	By using a comprehensive test suite, human evaluation, adversarial testing, and so on.
How will you test for potential biases in the generative AI model?	By using demographic parity analysis, equal opportunity testing, adversarial de-biasing techniques, counterfactual testing, and so on.
Which metrics will be used to measure fairness in the model's outputs?	Disparate impact ratio, equalized odds, demographic parity, individual fairness metrics, and so on.
How will you ensure diverse representation in your test datasets for bias detection?	By using stratified sampling across demographic groups, collaboration with diversity experts, use of synthetic data to fill gaps, and so on.
Which process will be implemented for ongoing monitoring of model fairness post-deployment?	Regular fairness audits, automated bias detection systems, user feedback analysis, periodic retraining with updated datasets, and so on.
How will you address intersectional biases in the generative AI model?	By using intersectional fairness analysis, subgroup testing, collaboration with domain experts on intersectionality, and so on.
How will you test the model's performance across different languages and cultural contexts?	By using multilingual test sets, collaboration with cultural experts, localized fairness metrics, cross-cultural comparison studies, and so on.