1 citations · 2 across the 8 of their papers we have counts for
4 papers · 1 filter
Causal Pre-training Under the Fairness Lens: An Empirical Study of TabPFN
Qinyi Liu, Mohammad Khalil, Naman Goel
Foundation models for tabular data, such as the Tabular Prior-data Fitted Network (TabPFN), are pre-trained on a massive number of synthetic datasets generated by structural causal…
Whose Preferences? Differences in Fairness Preferences and Their Impact on the Fairness of AI Utilizing Human Feedback
Emilia Agis Lerner, Florian E. Dorner, Elliott Ash +1
There is a growing body of work on learning from human feedback to align various aspects of machine learning systems with human values and preferences. We consider the setting of f…
WCLD: Curated Large Dataset of Criminal Cases from Wisconsin Circuit Courts
Elliott Ash, Naman Goel, Nianyun Li +2
Machine learning based decision-support tools in criminal justice systems are subjects of intense discussions and academic research. There are important open questions about the ut…
The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective
Naman Goel, Alfonso Amayuelas, Amit Deshpande +1
Training datasets for machine learning often have some form of missingness. For example, to learn a model for deciding whom to give a loan, the available training data includes ind…