Explainability for identification of vulnerable groups in machine learning models

Vulnerable groups differ from protected groups in important ways: While protected groups (like those defined by race or gender) are considered vulnerable in all contexts, vulnerable groups may only be vulnerable in specific situations. This means AI systems need context-aware protections rather than one-size-fits-all fairness rules.

Current AI fairness tools have a blind spot: Existing machine learning fairness methods focus on preventing discrimination against protected groups but don't adequately address whether models are unfairly targeting people who are vulnerable in particular contexts—a significant gap in responsible AI practice.

Explainability is key to identifying hidden vulnerabilities: The paper argues that explainable AI (XAI) methods—which answer "why did the model make this decision?"—are better equipped than traditional fairness approaches to uncover whether models are inadvertently identifying and potentially exploiting vulnerable individuals or groups.

We need new tools to detect vulnerability-based harms: Neither current fairness nor explainability methods alone can determine if a prediction model is identifying vulnerability. The authors propose that new analytical approaches are needed to specifically test for this type of harm before deploying AI systems.

Context matters for protection: Because vulnerability is context-dependent, responsible AI deployment requires understanding not just what a model predicts, but why it makes those predictions and when those predictions might harm people in vulnerable situations.