From the course: CompTIA SecAI+ (CY0-001) Cert Prep

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Auditing for bias and fairness

Auditing for bias and fairness

From the course: CompTIA SecAI+ (CY0-001) Cert Prep

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Auditing for bias and fairness

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Auditing for bias and fairness is an essential part of ensuring that AI systems treat all individuals and groups equitably. Bias occurs when an AI system produces unfair differences in outcomes among users. Fairness means that decisions are consistent, just, and free from discrimination. Because bias systems can cause harm and violate ethical or legal standards, Organizations most regularly audit their AI models to detect and correct unfair behavior. Bias typically originates from two sources, data bias and algorithmic bias. Data bias arises when the training data reflects social imbalances or stereotypes. Algorithmic bias, in contrast, results from the way models process data or assign weight to features. Even if the data set is balanced, an algorithm can still favor one group over another by giving greater importance to certain variables or by using decision thresholds that disadvantage specific populations. Effective bias audits start with clear fairness criteria. Statistical…

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