Data Engineering - Patronus AI: Building robust evaluation frameworks for AI accuracy
This is a guest post for the Computer Weekly Developer Network written by Anand Kannappan, CEO and founder of model adversarial testing platform Patronus AI.
Kannappan writes in full as follows…
Let’s have some fun and compare evaluating an AI model is a bit like judging an Olympic athlete.
Just as a gymnast’s abilities can’t be measured by a single backflip on a backyard lawn, an AI model’s true capabilities can’t be assessed through casual testing.
Both need rigorous, meticulous evaluation and adversarial tests.
For AI in particular, this process hinges on the precision and expertise of data engineering – a discipline that lays the foundation for accurate, meaningful assessments of AI accuracy and hallucination rates.
Now you can think of data engineers like the Olympic judges and course designers. They create the “challenging arenas” i.e. evaluation datasets that reveal whether an AI model is merely functional or truly exceptional. Their goal isn’t to set up easy wins but to replicate the complexity of the real world and expose the model’s potential blind spots.
Creating complex evaluation datasets
Take self-driving car algorithms as an example. It’s not enough to assess performance on sunny days with perfect road conditions. True evaluation requires testing against edge cases: snowy nights, sudden downpours, or unmarked construction zones. Similarly, when evaluating AI, data engineers build datasets that include both typical scenarios and rare, high-risk situations to ensure models are ready for real-world complexity.
Data engineers employ stratified sampling techniques to ensure representation of both common and rare scenarios.
They utilise techniques like importance sampling to oversample rare events, ensuring adequate representation of edge cases.
For self-driving car algorithms, this might involve using generative adversarial networks (GANs) to synthesise realistic adverse weather conditions or leveraging domain randomisation to create diverse virtual environments.
Precision in data curation
Data engineers must scrutinise each data point for quality, ensuring it contributes to the model’s evaluation.
To fill gaps in real-world data, they often employ advanced techniques, generating synthetic scenarios to test how models handle edge cases too rare or risky to capture naturally.
This precision ensures the evaluation dataset is comprehensive, unbiased and truly challenging.
Data engineers implement rigorous quality control measures, including automated anomaly detection algorithms and statistical analyses to identify outliers and inconsistencies. They employ techniques like active learning to efficiently label edge cases and use data augmentation methods such as SMOTE (Synthetic Minority Over-sampling Technique) to balance class distributions.
For synthetic data generation, they might utilise advanced generative models like variational autoencoders (VAEs) or flow-based models to create realistic and diverse scenarios.
Documentation & reproducibility
Kannappan: Robust evaluation datasets are not just about performance; they validate trusted system responsibilities.
Engineers must maintain a detailed record of how datasets are built and tests are conducted, ensuring reproducibility and accountability. This level of rigor shifts AI evaluation from subjective experimentation to an objective process rooted in data science. When a model excels in testing, businesses and stakeholders can be confident it will perform similarly under real-world conditions.
Engineers can utilise version control systems like Git LFS or DVC (Data Version Control) to track changes in datasets over time. They implement data lineage tracking tools to maintain a comprehensive audit trail of data transformations and preprocessing steps. Documentation includes detailed metadata schemas, following standards like the ML Schema or DCAT (Data Catalog Vocabulary), to ensure interoperability and reproducibility. They also employ containerisation technologies like Docker to encapsulate the entire evaluation environment, ensuring consistency across different systems and facilitating reproducibility of results.
This is important work
AI systems now influence critical decisions in industries like healthcare, finance and public safety. If these systems are deployed without thorough evaluation, the risks – incorrect diagnoses, financial losses, or even threats to public safety – are immense.
Robust evaluation datasets are not just about performance; they validate whether these systems can be trusted to handle the responsibilities assigned to them.
Designing rigorous tests that mimic real-world complexity and challenge models to their limits, can help data engineers ensure AI systems are up to the task.
Their work goes beyond testing technology – it safeguards the trust we place in AI, making them indispensable to the future of innovation and public confidence in these transformative systems.
