Search Results for author:
Found 6 papers, 1 papers with code
Are Emily and Greg Still More Employable than Lakisha and Jamal? Investigating Algorithmic Hiring Bias in the Era of ChatGPT
We replicate this experiment on state-of-art LLMs (GPT-3. 5, Bard, Claude and Llama) to evaluate bias (or lack thereof) on gender, race, maternity status, pregnancy status, and political affiliation.
Application of BadNets in Spam Filters
Our results show that the backdoor attacks can be effectively used to identify vulnerabilities in spam filters and suggest the need for ongoing monitoring and improvement in this area.
Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access
Recent work has sought to train fair models without sensitive attributes on training data.
Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale
We further show that MinDiff optimization is very sensitive to choice of batch size in the under-parameterized regime.
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection
This paper proposes a new defense against neural network backdooring attacks that are maliciously trained to mispredict in the presence of attacker-chosen triggers.
NNoculation: Catching BadNets in the Wild
This paper proposes a novel two-stage defense (NNoculation) against backdoored neural networks (BadNets) that, repairs a BadNet both pre-deployment and online in response to backdoored test inputs encountered in the field.
