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Found 6 papers, 2 papers with code
FairProof : Confidential and Certifiable Fairness for Neural Networks
To this end, we propose FairProof - a system that uses Zero-Knowledge Proofs (a cryptographic primitive) to publicly verify the fairness of a model, while maintaining confidentiality.
Keeping Up with the Language Models: Robustness-Bias Interplay in NLI Data and Models
Bias auditing is further complicated by LM brittleness: when a presumably biased outcome is observed, is it due to model bias or model brittleness?
XAudit : A Theoretical Look at Auditing with Explanations
This work formalizes the role of explanations in auditing and investigates if and how model explanations can help audits.
Behavior of k-NN as an Instance-Based Explanation Method
We attribute this to training set subsampling for IFs.
On the design of convolutional neural networks for automatic detection of Alzheimer's disease
Early detection is a crucial goal in the study of Alzheimer's Disease (AD).
Cold Case: The Lost MNIST Digits
Although the popular MNIST dataset [LeCun et al., 1994] is derived from the NIST database [Grother and Hanaoka, 1995], the precise processing steps for this derivation have been lost to time.
