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Found 10 papers, 3 papers with code
Regulation Games for Trustworthy Machine Learning
Existing work on trustworthy machine learning (ML) often concentrates on individual aspects of trust, such as fairness or privacy.
Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility
Deploying machine learning (ML) models often requires both fairness and privacy guarantees.
Proof-of-Learning is Currently More Broken Than You Think
They empirically argued the benefit of this approach by showing how spoofing--computing a proof for a stolen model--is as expensive as obtaining the proof honestly by training the model.
$p$-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations
We introduce $p$-DkNN, a novel inference procedure that takes a trained deep neural network and analyzes the similarity structures of its intermediate hidden representations to compute $p$-values associated with the end-to-end model prediction.
Tubes Among Us: Analog Attack on Automatic Speaker Identification
Recent years have seen a surge in the popularity of acoustics-enabled personal devices powered by machine learning.
SoK: Machine Learning Governance
The application of machine learning (ML) in computer systems introduces not only many benefits but also risks to society.
Dataset Inference: Ownership Resolution in Machine Learning
We thus introduce $dataset$ $inference$, the process of identifying whether a suspected model copy has private knowledge from the original model's dataset, as a defense against model stealing.
Proof-of-Learning: Definitions and Practice
In particular, our analyses and experiments show that an adversary seeking to illegitimately manufacture a proof-of-learning needs to perform *at least* as much work than is needed for gradient descent itself.
A Human-in-the-loop Framework to Construct Context-aware Mathematical Notions of Outcome Fairness
Our family of fairness notions corresponds to a new interpretation of economic models of Equality of Opportunity (EOP), and it includes most existing notions of fairness as special cases.
Disparate Vulnerability to Membership Inference Attacks
Differential privacy bounds disparate vulnerability but can significantly reduce the accuracy of the model.
