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Red-Teaming for Generative AI: Silver Bullet or Security Theater?
In response to rising concerns surrounding the safety, security, and trustworthiness of Generative AI (GenAI) models, practitioners and regulators alike have pointed to AI red-teaming as a key component of their strategies for identifying and mitigating these risks.
The AI Incident Database as an Educational Tool to Raise Awareness of AI Harms: A Classroom Exploration of Efficacy, Limitations, & Future Improvements
Prior work has established the importance of integrating AI ethics topics into computer and data sciences curricula.
A Suite of Fairness Datasets for Tabular Classification
There have been many papers with algorithms for improving fairness of machine-learning classifiers for tabular data.
Moral Machine or Tyranny of the Majority?
With Artificial Intelligence systems increasingly applied in consequential domains, researchers have begun to ask how these systems ought to act in ethically charged situations where even humans lack consensus.
Navigating Ensemble Configurations for Algorithmic Fairness
Bias mitigators can improve algorithmic fairness in machine learning models, but their effect on fairness is often not stable across data splits.
An Empirical Study of Modular Bias Mitigators and Ensembles
A popular approach to train more stable models is ensemble learning.
