post-processing
What It Means
Post-processing is like adding a final quality control step after your AI model makes its predictions. Instead of changing how the model was trained, you apply corrections or adjustments to its outputs using a separate dataset to fix problems like bias or improve accuracy. Think of it as a filter that modifies what the AI says before those results reach your customers or business users.
Why Chief AI Officers Care
This is often your only option for fixing bias or performance issues in third-party AI systems or legacy models where you can't retrain from scratch. It's particularly critical for meeting regulatory compliance requirements around fair lending, hiring, or other regulated decisions where you need to demonstrate bias mitigation. Post-processing also lets you quickly adjust AI outputs to meet changing business requirements without expensive model rebuilding.
Real-World Example
A bank uses a third-party credit scoring model but discovers it unfairly denies loans to certain demographic groups. Since they can't retrain the vendor's model, they implement post-processing that automatically adjusts credit scores for affected groups based on analysis of past loan performance data, ensuring fair lending compliance while keeping the original model intact.
Common Confusion
People often think post-processing means any data cleanup or formatting of AI outputs, but it specifically refers to algorithmic corrections applied to model predictions to address bias or performance issues. It's also confused with data preprocessing, which happens before training rather than after predictions are made.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, post-processing ensures clinical predictions meet safety and regulatory standards by applying validati...
Finance: In finance, post-processing is critical for ensuring AI model outputs comply with regulatory requirements like fair lend...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"Typically performed with the help of a holdout dataset (data not used in the training of the model). Here, the learned model is treated as a black box and its predictions are altered by a function during the post-processing phase. The function is deduced from the performance of the black box model on the holdout dataset. "Source: SP1270
"Performed after training by accessing a holdout set that was not involved during the training of the model. If the algorithm can only treat the learned model as a black box without any ability to modify the training data or learning algorithm, then only post-processing can be used in which the labels assigned by the black-box model initially get reassigned based on a function during the post-processing phase."Source: Mehrabi,_Ninareh
"Steps performed after a machine learning model has been run to adjust its output. This can include adjusting a model's outputs or using a holdout dataset — data not used in the training of the model — to create a function run on the model's predictions to improve fairness or meet business requirements."Source: IAPP_Governance_Terms
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