# kaal:claim:5095633-019

**Claim.** Biases held by human annotators or embedded in automated annotation systems are propagated into the models trained on their output, producing AI that performs inequitably across demographic groups.

**Type.** mechanism  **Support.** argued

**Holds when.**

- applies even to providers that emphasize high-quality data such as Scale AI and Appen

**Source quote.**

> there's a theoretical risk that biases inherent in data annotators or automated systems might be propagated into AI models. This can lead to AI that does not perform equitably across different demographic groups or scenarios.

**From.** Wulf A. Kaal, *Artificial Intelligence The Final Frontier* (2025), 4.1. Theoretical Shortcomings, page 12

**Cite as.** Wulf A. Kaal, Artificial Intelligence The Final Frontier (2025). SSRN: https://ssrn.com/abstract=5095633

**Verify.** sha256 of source PDF `cbb484711f89bcefc9fc6a5730a1ed0a3f764d7999ad9b6f7d8ea05634c26c63` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202025%20-%20Artificial%20Intelligence%20The%20Final%20Frontier.pdf

**Failure mode.** annotator bias propagation  (family: ai-model-and-training-failure)

**Topics.** institutional-design

**Keywords.** annotator-bias, bias-propagation, data-annotation, fairness

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