Homogenization or correlated failures in model derivatives

Sub-category

Risk Domain

7AI System Safety, Failures & Limitations

7.3Lack of capability or robustness

AI systems that fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences, especially in critical applications or areas that require moral reasoning.

"Homogenization refers to common methodologies and models used across down- stream GPAI systems, which may lead to uniform failures and amplification of biases [176, 30]. This risk arises when numerous downstream AI systems are built upon a few large-scale foundation models."(p. 51)

Entity— Who or what caused the harm

Human

Due to a decision or action made by humans

AI system

Due to a decision or action made by an AI system

Other

Due to some other reason or is ambiguous

Intent— Whether the harm was intentional or accidental

Intentional

Due to an expected outcome from pursuing a goal

Unintentional

Due to an unexpected outcome from pursuing a goal

Other

Without clearly specifying the intentionality

Timing— Whether the risk is pre- or post-deployment

Pre-deployment

Occurring before the AI is deployed

Post-deployment

Occurring after the AI model has been trained and deployed

Other

Without a clearly specified time of occurrence

Supporting Evidence (1)

"This may be caused by centralization of AI advancements within a few compa- nies, as well as flaws from algorithmic monoculture, where dataset sources and collection methods are similar across numerous AI models. Homogenization in models may lead to consistent and arbitrary rejection, mis- treatment, scrutiny, or misclassification of specific users of groups, as well as the spread of implicit perspectives (e.g. bias towards a particular political group) across multiple application domains."(p. 51)

Part of Impacts of AI (Bias)