AI Model Validation

This new revolution, which we call machine learning and artificial intelligence (ML/AI), is no longer simply for enthusiasts and academics seeking for a difficult challenge to solve; it has entered the mainstream. This isn’t a coincidence. ML/AI approaches have constantly improved and grown in sophistication since their inception.

As technology, processing power, and storage capacity have soared, so has firms’ ability to tackle complex, real-world business challenges using machine learning and artificial intelligence approaches and algorithms.

Framework

The following dimensions may be used to categorize the AI/ML model validation framework:

  1. Relevancy of the data – training AI models typically necessitates a massive quantity of data, much of which is unstructured. This would need securing the following:
  • Protection of PII or any other personal data. In addition, the data gathering and handling procedure should be considered.
  • Validation of the data’s integrity and appropriateness, so that it may be utilized for the intended purpose and in the proper manner.
  • Any pre-processing (such as transformations, normalization, missing value computation, and so on) is done on both the train and test data.
  • Examine the data’s completeness by looking at time periods, sources, and distributions, and if necessary, look up the labeling definition.
  1. Model testing and procedures – AI/ML models, unlike traditional models, are typically black boxes. As a result, verifying model parameters, output, and sensitivity to inputs becomes difficult. The validators must:
  • Understand the methodology’s objectives as well as the business requirement to guarantee that the model delivers expected outcomes and is stable over time.
  • Tune the model by reviewing the hyperparameters: optimization functions, activation functions, and loss functions.
  • Ascertain that the hyperparameters are appropriate for the model’s goal and application.
  • Determine if model validation metrics such as false positives, precision, and recall are adequately established in accordance with business requirements.
  • Use more computationally costly approaches to assess model correctness and stability.
  • Confirm that a full sensitivity analysis has been completed, allowing the impact of each feature to be assessed. More sophisticated approaches for global interpretability exist, such as partial dependency plots (PDP), which visualize the average partial connection between the projected response and one or more characteristics to uncover patterns.
  • Once the sensitivity has been collected, assess the plausibility of the situation and its impact. Scenario analysis is required to guarantee that the model is resistant to any severe events or sounds.
  • Compare and contrast the benchmarking or challenger models with the final model.
  1. Interpretability and conceptual consistency

AI/ML approaches are still not widely accepted by regulators or practitioners when compared to classical model validation methods. This is owing to its black-box character, which makes it impossible to determine explainability and fitting in the modeling or commercial environment at hand.

SHAP, LIME, or Explainable Boosting Machines (EBM), which are model-agnostic and also offer the interaction term for the models, are commonly used to quantify the transparency, explainability, and feature significance of a black-box model.

Validators must confirm that this sort of study has been completed and that the analysis’ result is relevant to the business challenge.

  1. Model Security and Model Implementation

Once the model has been constructed, the next step is to put it into production, either on a server or in the cloud, such as Azure or GCP. Validators must carefully analyze the model implementation plan’s readiness and design in this phase. Validators must also assess whether the applications, including libraries, modules, and settings, are suitable for deployment, taking into account the potential consequences of future releases.

Apart from implementation, this validation of the ai models framework is likely to include a viewpoint on model security, such as an adversarial attack, model theft, and so on. To this end, risk tiering should be taken into account when determining the solution’s transparency.

  1. Version Control and Model Documentation – the documentation should be self-explanatory and comprehensive enough for validators to reproduce the model.

With suitable model documentation rules, documentation should detail the development data extraction and pre-processing, model theory and design, development strategy, and model performance, including challenger models.

  1. Auditing and management – Validators should examine the monitoring strategy to ensure that scope, objectives, stakeholders, and roles and duties are all addressed. In addition, the frequency and duration of scheduled revisits or recalibrations should be assessed. Management bodies with effective supervision will guarantee that management is aware of all model hazards.

As companies enhance their use of the new AI/ML regime, the MRM framework must become more solid and extensive than ever before. When validating ai models, validators must assess the models on all of the major dimensions.