Post-hoc auditing framework opens black box of neural auction mechanisms
Original title: Opening the Black Box of Neural Auction Mechanisms
CITransNet is unpacked using sparse autoencoders to verify features that align with Myerson's optimal auction theory.
Neural auction mechanisms lack internal transparency raising auditability concerns. The paper proposes a post-hoc white-box auditing framework for CITransNet. Sparse autoencoders decompose hidden representations while preserving downstream behavior. Verified features support diagnosis of misreporting incentives and optimal auction alignment.
- Sparse autoencoders decompose hidden representations
- Features anchored to native economic targets
- Validation via activation prediction and causal interventions
- Framework applies across varied auction settings
From the original article
1 min read at arxiv.orgNeural auction design has emerged as a powerful paradigm within automated mechanism design for modern Web platforms. Because these learned mechanisms directly dictate critical economic outcomes, their lack of internal transparency raises significant auditability and trust concerns.
By Li Kong · arxiv.org
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