in-processing
What It Means
In-processing refers to bias mitigation techniques that work during the actual AI model training phase, rather than before or after. These methods modify how the algorithm learns by adjusting the training objectives or adding constraints that force the model to make fairer decisions while it's being built.
Why Chief AI Officers Care
This approach offers a middle-ground solution when you can't easily fix biased training data but need to ensure fair AI outcomes for regulatory compliance and brand protection. In-processing techniques can be more effective than post-training fixes because they bake fairness directly into the model's decision-making logic, reducing the risk of discriminatory outcomes that could trigger lawsuits or regulatory penalties.
Real-World Example
A bank training a loan approval AI model uses in-processing techniques to ensure the algorithm doesn't discriminate against certain demographic groups, even if the historical loan data shows past biased lending patterns. The training process is modified to penalize the model when it makes decisions that correlate too strongly with protected characteristics like race or gender, forcing it to find other legitimate factors for loan decisions.
Common Confusion
People often think in-processing is the same as simply removing sensitive variables from training data, but it's actually about fundamentally changing how the algorithm learns and optimizes its decisions. It's also confused with post-processing corrections, but in-processing builds fairness into the model's core logic rather than adjusting outputs after the fact.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI development, in-processing techniques are applied during model training to ensure compliance with anti-...
Finance: In finance, in-processing techniques are applied during model training for credit scoring, loan approval, and risk asses...
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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
"Techniques that modify the algorithms in order to mitigate bias during model training. Model training processes could incorporate changes to the objective (cost) function or impose a new optimization constraint."Source: SP1270
"Techniques that try to modify and change state-of-the-art learning algorithms to remove discrimination during the model training process."Source: Mehrabi,_Ninareh
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