# kaal:claim:5541658-021

**Claim.** The overreliance concern is not uniform: some studies find that annotators do not over rely on LLM output when labelling court opinions for legally relevant factors, and other studies find an aversion to AI generated legal content consistent with algorithmic aversion in other domains.

**Type.** empirical  **Support.** evidenced

**Holds when.**

- annotation tasks labelling court opinions for legally relevant factors
- studies of lawyers' preferences between human and LLM authored documents

**Source quote.**

> On the other hand, some studies have found that students were unlikely to over rely on LLM output when labelling court opinions as to legally relevant factors.

**From.** Wulf A. Kaal, Morgan A. Gray, *The Evolving Role of Artificial Intelligence in Law* (2025), B. Overreliance and Errors, page 26

**Cite as.** Wulf A. Kaal, Morgan A. Gray, The Evolving Role of Artificial Intelligence in Law (2025). SSRN: https://ssrn.com/abstract=5541658

**Verify.** sha256 of source PDF `e543a2d698fcd522d4d02e034cc9ee1344d0015d2c824b40b9e05ab7c0728c60` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20and%20Gray%20-%202025%20-%20The%20Evolving%20Role%20of%20Artificial%20Intelligence%20in%20Law.pdf

**Topics.** empirical-evidence

**Keywords.** overreliance, algorithmic-aversion, empirical-studies, annotation

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