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Disparate Performance

AI Risk

The LLM’s performances can differ significantly across different groups of users. For example, the question-answering capability showed significant performance differences across different racial and social status groups. The fact-checking abilities can differ for different tasks and languages

Overview

A source-backed snapshot of this AI risk.

Techniques1Attack methods connected to this risk.
Mitigations4Defenses that may help with related attacks.
Records2Source records unified into this concept.

Risk profile

How the MIT AI Risk Repository categorizes this risk.

Domain1. Discrimination & Toxicity
Subdomain1.3 > Unequal performance across groups
Entity2 - AI
Intent2 - Unintentional
Timing3 - Other
CategoryFairness; Impacts: The Technical Base System
SubcategoryDisparate Performance

Merged risk records

Source records unified into this canonical risk concept.

2 recordsView all →

MITRISK-Liu2024-30.03.04 - Disparate Performance

The LLM’s performances can differ significantly across different groups of users. For example, the question-answering capability showed significant performance differences across different racial and social status groups. The fact-checking abilities can differ for different tasks and languages

Domain1. Discrimination & ToxicitySubdomain1.3 > Unequal performance across groupsSourceTrustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' AlignmentYear2024

MITRISK-Solaiman2023-13.01.03 - Disparate Performance

"In the context of evaluating the impact of generative AI systems, disparate performance refers to AI systems that perform differently for different subpopulations, leading to unequal outcomes for those groups."

Domain1. Discrimination & ToxicitySubdomain1.3 > Unequal performance across groupsSourceEvaluating the Social Impact of Generative AI Systems in Systems and SocietyYear2023

Mitigations

Defenses that may help with related attacks.

LifecycleML Model Engineering + 2 moreCategoryTechnical - ML
ML Model EngineeringML Model Evaluation+1 more
LifecycleML Model Engineering + 2 moreCategoryTechnical - ML
ML Model EngineeringML Model Evaluation+1 more

Source evidence

Research source for this risk, when available.