# kaal:claim:4855607-033

**Claim.** The RLHF process is exposed to failure because participants may hold potentially adversarial and misaligned interests, so the vulnerability lies in the incentive structure of feedback provision rather than in the learning algorithm.

**Type.** failure  **Support.** argued

**Holds when.**

- RLHF systems where feedback providers have divergent stakes

**Source quote.**

> Currently, the RLHF process, which involves training AI models based on human preferences and feedback, can face challenges due to the potentially adversarial and misaligned interests of participants.

**From.** Wulf A. Kaal, *How AI Models are Optimized Through Web3 Governance* (2024), RLHF Optimization, page 51

**Cite as.** Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607

**Verify.** sha256 of source PDF `eb0b3e62374b45a8fa888c6bde9725e606bcb46cf4b5e74a6e851d9f25099113` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20How%20AI%20Models%20are%20Optimized%20Through%20Web3%20Governance.pdf

**Failure mode.** Adversarial feedback provider incentives  (family: ai-oversight-and-alignment-gap)

**Topics.** ai-and-agents, risk-and-incentives, governance-design

**Keywords.** rlhf, incentive-misalignment, adversarial-participants, feedback-quality, governance

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