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Poor model accuracy risk for AI
Last updated: Dec 12, 2024
Poor model accuracy risk for AI
Risks associated with input
Inference
Accuracy
Amplified by generative AI

Description

Poor model accuracy occurs when a model’s performance is insufficient to the task it was designed for. Low accuracy might occur if the model is not correctly engineered, or there are changes to the model’s expected inputs.

Why is poor model accuracy a concern for foundation models?

Inadequate model performance can adversely affect end users and downstream systems that are relying on correct output. In cases where model output is consequential, this might result in societal, reputational, or financial harm.

Parent topic: AI risk atlas

We provide examples covered by the press to help explain many of the foundation models' risks. Many of these events covered by the press are either still evolving or have been resolved, and referencing them can help the reader understand the potential risks and work towards mitigations. Highlighting these examples are for illustrative purposes only.

Generative AI search and answer
These answers are generated by a large language model in watsonx.ai based on content from the product documentation. Learn more