As enterprises adopt AI, they inevitably encounter systems behaving in unexpected and incorrect ways. Complex neural networks become inscrutable black boxes, defying easy diagnosis. How can enterprises debug erratic AI and return them to reliable functionality? A combination of techniques dispels the black box to shed light on AI failures.
Log Everything for Visibility
Full visibility into the AI system is a prerequisite for debugging. Log all aspects of the data and model:
- Record model inputs, outputs, weights, Node activations, and gradients during training and inference. This illuminates model internals.
- Track data provenance end-to-end from sources to labeling to sampling. Know exactly how data was ingested.
- Monitor compute infrastructure like GPU usage and memory allocation. Hardware issues can induce bugs.
- Catalog the exact libraries, frameworks, parameters, and versions underlying the model. Environments often differ across machines.
Meticulous logging ensures all model pieces are accounted for when debugging. It also allows reconstructing the model state at any point post-mortem.
Inspect Predictions and Explanations
Analyze model outputs for patterns revealing quirky behavior:
- Aggregate predictions into dashboards highlighting unusual clusters and distributions. Drill-down by model, version, and input data subsets.
- Generate feature importance and partial dependence plots to understand relationships between inputs and outputs.
- Use techniques like LIME and Shapley values to generate local explanations for predictions. Inspect instances with unusual explanations.
Prediction inspection exposes cases where model reasoning diverges from expectations. This focuses debugging efforts on tractable subsets of inferencing.
Put Models under Stress
Test systems under stressful conditions to surface latent bugs:
- Assess performance on corrupted or adversarial data showing where logic breaks down.
- Try unreasonable values for parameters and hyperparameters that violate assumptions.
- Run models on larger datasets, longer texts, and edge cases than used in training.
- Push hardware by running large batch inferences that strain GPU memory.
Stress testing reveals boundaries where model logic frays and technical debt emerges. However, consult developers first to avoid invalidating models.
Leverage Hybrid AI Insights
New hybrid AI systems that combine neural networks, symbolic rules, and human knowledge aid debugging:
- Neural-symbolic models generate logic rules explaining model behavior at a high level.
- Models incorporating developer annotations highlight human-identified issues.
- Human-in-the-loop systems allow developers to step through predictions and pinpoint divergence.
Hybrid AI provides both bug detection and guidance on repairs. However, these techniques remain nascent. Temper expectations as they mature.
An Ounce of Prevention…
Debugging complex models is challenging. Where possible, prevent bugs through:
- Established software engineering practices like modularity, documentation, and version control
- Monitoring data quality and shifting distributions during retraining
- Ensuring reproducibility across machines, deployments, and frameworks
- Developer peer review of model architecture and code
With AI permeating enterprise operations, reliability is imperative. Proactive measures and debugging toolkits keep models dependable and business-critical applications running smoothly.
