112 citations · 135 across the 12 of their papers we have counts for
4 papers · 1 filter
Fine-tuning language models to find agreement among humans with diverse preferences
Michiel A. Bakker, Martin J. Chadwick, Hannah R. Sheahan +8
Recent work in large language modeling (LLMs) has used fine-tuning to align outputs with the preferences of a prototypical user. This work assumes that human preferences are static…
Statistical discrimination in learning agents
Edgar A. Duéñez-Guzmán, Kevin R. McKee, Yiran Mao +9
Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics. One pr…
DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning
Michiel A. Bakker, Duy Patrick Tu, Humberto Riverón Valdés +4
We introduce a framework for dynamic adversarial discovery of information (DADI), motivated by a scenario where information (a feature set) is used by third parties with unknown ob…
Sherlock: A Deep Learning Approach to Semantic Data Type Detection
Madelon Hulsebos, Kevin Hu, Michiel Bakker +5
Correctly detecting the semantic type of data columns is crucial for data science tasks such as automated data cleaning, schema matching, and data discovery. Existing data preparat…