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preprocessing

What It Means

Preprocessing means cleaning and adjusting your training data before feeding it into an AI model to remove unfair biases. It's like editing a recipe's ingredients before cooking to ensure the final dish doesn't have unwanted flavors that could harm certain groups of people.

Why Chief AI Officers Care

This is your first line of defense against AI discrimination lawsuits and regulatory violations. If your training data contains historical biases (like past hiring discrimination), preprocessing can help prevent your AI from perpetuating those same unfair patterns, protecting your company from legal exposure and reputational damage.

Real-World Example

A bank's historical loan data shows that women were systematically denied loans in the past due to discriminatory practices. Before training a new AI lending model, the bank preprocesses this data by removing gender-correlated variables and rebalancing approval rates to ensure the AI doesn't learn to discriminate against female applicants.

Common Confusion

People often think preprocessing means just cleaning dirty data or removing errors, but in the AI fairness context it specifically means actively identifying and correcting systematic biases that could lead to discrimination. It's not about data quality - it's about data equity.

Industry-Specific Applications

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Healthcare: In healthcare AI, preprocessing involves cleaning medical datasets to remove biases related to demographics, socioeconom...

Finance: In finance, preprocessing involves cleaning datasets used for credit scoring, fraud detection, and risk assessment model...

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Technical Definitions

NISTNational Institute of Standards and Technology
"Transforming the data so that the underlying discrimination is mitigated. This method can be used if a modeling pipeline is allowed to modify the training data."
Source: SP1270

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