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Evidence record 4480 · automatically gathered

Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning

Collaborative machine learning involves training high-quality models using datasets from a number of sources. To incentivize sources to share data, existing data valuation methods fairly reward each source based on its data submitted as is. However, as these methods do not verify nor incentivize data truthfulness, the sources can manipulate their data (e.g., by submitting duplicated or noisy data) to artificially increase their valuations and rewards or prevent others from benefiting. This paper

Record details

Published: 12 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Finance, VC & PE
Retrieved: 14 July 2026

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How to cite this record

ethics.ai (12 May 2026), “Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning,” evidence record 4480, https://ethics.ai/record/4480 (originally published by arXiv).

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