Description
In the era of widespread AI deployment, model fairness has emerged as a critical consideration that extends beyond traditional performance metrics. As AI systems increasingly impact human lives, organizations must ensure their models make equitable decisions across different demographic groups and sensitive attributes.
The challenge of measuring model fairness lies in its multifaceted nature – there are numerous definitions of fairness, and they can sometimes be mathematically incompatible with each other. Here is a framework for measuring, analyzing, and improving model fairness in enterprise AI systems, helping organizations navigate the complex landscape of algorithmic fairness.
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