# kaal:claim:4855607-038

**Claim.** Gathering a wide range of human feedback makes the Reward Model reflect a comprehensive spectrum of human preferences and values, and it is this inclusivity that mitigates bias and captures a richer understanding of what counts as a desirable outcome.

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

**Holds when.**

- reward models trained on feedback drawn from a broad participant base

**Source quote.**

> By leveraging this model, RLHF can gather a wide range of human feedback, ensuring the Reward Model (RM) reflects a comprehensive spectrum of human preferences and values. This inclusivity helps mitigate biases and captures a richer understanding of what is considered a desirable outcome.

**From.** Wulf A. Kaal, *How AI Models are Optimized Through Web3 Governance* (2024), RLHF Optimization, page 52

**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

**Topics.** institutional-design

**Keywords.** rlhf, reward-model, preference-diversity, bias-mitigation, inclusivity

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