AI Bias Assessment & Mitigation FAQs

Question 1

How does it help with bias mitigation?

Accepted Answer

The skill provides code patterns for reweighting data (pre-processing), constrained optimization (in-processing), and group-specific threshold adjustments (post-processing).

Question 2

What fairness metrics does this skill support?

Accepted Answer

It supports standard group fairness metrics including Demographic Parity, Equalized Odds, Equal Opportunity, and Predictive Parity, as well as individual and causal fairness concepts.

Question 3

Can this detect bias in Large Language Models (LLMs)?

Accepted Answer

Yes, it includes specialized testers that generate demographic variations of prompts to measure response consistency and identify potential bias indicators in LLM outputs.

Question 4

Can it guarantee a perfectly fair model?

Accepted Answer

No; as noted in the 'Impossibility Theorem,' it is mathematically impossible to satisfy all fairness metrics simultaneously when base rates differ. This skill helps document and navigate these trade-offs.

Question 5

What are proxy variables in AI bias?

Accepted Answer

Proxy variables are non-protected features (like zip codes) that correlate strongly with protected attributes (like race), which can lead to indirect discrimination if not detected and handled.

AI Bias Assessment & Mitigation

主要功能

使用场景

AI Bias Assessment & Mitigation

主要功能

使用场景