AI Explainability
Executive Summary
Key Takeaways
- ✓ Explainability means something different for a deterministic formula-based model, a machine learning model, and a generative AI model, and conflating these three standards creates unrealistic expectations for what any one of them can actually explain.
- ✓ A deterministic model's traceability standard, each output traces to a specific, disclosed formula, is the strongest form of explainability and is not the standard machine learning or generative AI should be measured against.
- ✓ Machine learning explainability typically means being able to state which input features most influenced a given prediction, a statistical attribution rather than a step-by-step calculation trace.
- ✓ Generative AI explainability is the weakest of the three standards; a language model's specific reasoning process for a given output is generally not fully traceable, which is why verification of the output itself, rather than its generation process, is the practical control.
- ✓ Practical documentation, feature importance summaries, training data descriptions, and explicit known limitations, supports each standard appropriately rather than overstating what any one AI approach can actually explain about itself.
Objective¶
This guide sets out what explainability means across the different AI technique categories used in finance, extending the reliability distinctions drawn throughout AI Financial Modelling & Artificial Intelligence in Finance.
Three Distinct Explainability Standards¶
Deterministic formula traceability. A deterministic, rule-based engine or a structured financial model's output traces to a specific, disclosed formula and location, the strongest explainability standard, addressed in full on AI Financial Model Audit.
Machine learning feature attribution. A machine learning model's explainability typically takes the form of stating which input features most influenced a given prediction, a statistical attribution of relative importance rather than a step-by-step calculation trace of exactly how the prediction was derived.
Generative AI output verification. A generative AI model's internal reasoning process for a given output is generally not fully traceable in the way the other two standards are, which is why the practical approach is verifying the output itself against independent sources, addressed in AI Hallucination Risk, rather than attempting to trace how the model arrived at it.
Why Conflating These Standards Is a Problem¶
Expecting a machine learning model to provide deterministic formula-level traceability, or expecting a generative AI output to be explainable in the same statistical-attribution sense as a machine learning prediction, sets an expectation neither technique is designed to meet. Each standard is genuinely useful for its own technique category; applying the wrong standard's expectations to the wrong technique produces either unrealistic demands or false reassurance.
Practical Documentation Supporting Each Standard¶
For machine learning models, feature importance summaries and training data descriptions support the attribution-based explainability standard appropriately. For generative AI, documentation of known limitations and the verification controls applied, addressed in AI Hallucination Risk, supports the output-verification standard that substitutes for full process traceability. For deterministic components, the disclosed rule library and traceable formula structure addressed on AI Financial Model Audit remains the appropriate documentation.
Common Construction Pitfalls¶
Demanding deterministic-level traceability from a machine learning model. This sets an expectation the technique is not designed to meet and can lead to either rejecting a genuinely useful machine learning application or accepting a misleading claim that it provides such traceability.
Treating feature importance as a full explanation of a prediction. Feature attribution states relative influence; it does not provide a step-by-step account of exactly how those features combined to produce the specific predicted value.
Assuming a generative AI model can explain its own reasoning reliably. A model's stated explanation for its own output is itself generated text, subject to the same hallucination risk as any other generative output, and should not be treated as an authoritative account of its actual internal process.
Recommended Practices¶
- Apply the explainability standard appropriate to each technique category, not a single standard uniformly.
- Document feature importance and training data for machine learning models supporting attribution-based explainability.
- Rely on output verification, not process explanation, as the practical control for generative AI.
- State explicitly which explainability standard applies to which part of an AI-assisted model or analysis.
Continue Reading¶
Related Pillars¶
Related Technical Guides¶
Related Glossary¶
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Frequently Asked Questions
Does explainability mean the same thing for every kind of model?
No. It means something different for a deterministic formula-based model, a machine learning model, and a generative AI model, and conflating these three distinct standards creates unrealistic expectations for what any one of them can actually explain about its own output.
What is the strongest form of explainability, and which model type provides it?
A deterministic model's traceability standard, where each output traces to a specific, disclosed formula and location in the model, addressed on AI Financial Model Audit, is the strongest form and is not the standard machine learning or generative AI outputs should be measured against.
What does explainability mean for a machine learning model?
Typically, being able to state which input features most influenced a given prediction, a statistical attribution of relative influence, rather than a step-by-step calculation trace of exactly how the output was derived.
Can a generative AI model fully explain its own reasoning for a given output?
Generally not in a fully traceable way, which is the weakest of the three explainability standards. This is precisely why verifying the output itself, rather than attempting to trace the model's internal generation process, is the practical control for generative AI.
What documentation supports explainability in practice?
Feature importance summaries for machine learning models, training data descriptions, and explicit statements of known limitations for each model type, applied appropriately to each standard rather than overstating what any one AI approach can actually explain about itself.
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