Learning with Conflicts of Interest
Financial, social, and political factors often prevent the interests of the owners of ML systems and services and their users from being perfectly aligned. ML systems often produce biased information that can influence users to make decisions that are not in their best interest. Current solution approaches require ML systems to implement protocols to mitigate their biases. However, ML system owners usually do not have any incentive to implement these protocols and often argue that it limits their freedom of expression or business. We believe that a successful solution to this problem must recognize the conflict of interest between the ML systems and their users, and use this information t...
| Authors | Nischal Aryal, Arash Termehchy, Ali Vakilian et al. |
|---|---|
| Primary category | cs.LG |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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