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AI Risk

Fairness

This challenge appears when the learning model leads to a decision that is biased to some sensitive attributes... data itself could be biased, which results in unfair decisions. Therefore, this problem should be solved on the data level and as a preprocessing step

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AI Risk1. Discrimination & Toxicity1.3 > Unequal performance across groups1 - Pre-deployment

Record summary

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Techniques0Attack methods connected to this risk.
Mitigations0Defenses that may help with related attacks.
Domain1. Discrimination & ToxicityThe broad risk area this belongs to.

Risk profile

How this risk is described and categorized.

Domain1. Discrimination & Toxicity
Subdomain1.3 > Unequal performance across groups
Entity2 - AI
Intent2 - Unintentional
Timing1 - Pre-deployment
CategoryFairness
Subcategoryn/a

Related techniques

Attack methods connected to this risk.

No linked attack methods. No AI attack method is connected to this risk in the current data.

Suggested mitigations

Defenses that may help with related attacks.

No propagated mitigations. No defense is available through the connected attack methods.

Source

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