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Found 9 papers, 1 papers with code
Fairness Analysis with Shapley-Owen Effects
We argue that relative importance and its equitable attribution in terms of Shapley-Owen effects is an appropriate one, and, if we accept a small number of reasonable imperatives for equitable attribution, the only way to measure fairness.
Conditional Fairness for Generative AIs
The deployment of generative AI (GenAI) models raises significant fairness concerns, addressed in this paper through novel characterization and enforcement techniques specific to GenAI.
Safety Performance of Neural Networks in the Presence of Covariate Shift
The reshaped test set reflects the distribution of neuron activation values as observed during operation, and may therefore be used for re-evaluating safety performance in the presence of covariate shift.
Towards Rigorous Design of OoD Detectors
Out-of-distribution (OoD) detection techniques are instrumental for safety-related neural networks.
Evidential Transactions with Cyberlogic
The key ideas underlying Cyberlogic are extremely simple, as (1) public keys correspond to authorizations, (2) transactions are specified as distributed logic programs, and (3) verifiable evidence is collected by means of distributed proof search.
Towards Dependability Metrics for Neural Networks
Artificial neural networks (NN) are instrumental in realizing highly-automated driving functionality.
Verification of Binarized Neural Networks via Inter-Neuron Factoring
We study the problem of formal verification of Binarized Neural Networks (BNN), which have recently been proposed as a energy-efficient alternative to traditional learning networks.
Neural Networks for Safety-Critical Applications - Challenges, Experiments and Perspectives
We propose a methodology for designing dependable Artificial Neural Networks (ANN) by extending the concepts of understandability, correctness, and validity that are crucial ingredients in existing certification standards.
Maximum Resilience of Artificial Neural Networks
The deployment of Artificial Neural Networks (ANNs) in safety-critical applications poses a number of new verification and certification challenges.
