2 citations · 4 across the 5 of their papers we have counts for
5 papers
Selective Explanations
Lucas Monteiro Paes, Dennis Wei, Flavio P. Calmon
Feature attribution methods explain black-box machine learning (ML) models by assigning importance scores to input features. These methods can be computationally expensive for larg…
Multi-Level Explanations for Generative Language Models
Lucas Monteiro Paes, Dennis Wei, Hyo Jin Do +8
Despite the increasing use of large language models (LLMs) for context-grounded tasks like summarization and question-answering, understanding what makes an LLM produce a certain r…
Algorithmic Arbitrariness in Content Moderation
Juan Felipe Gomez, Caio Vieira Machado, Lucas Monteiro Paes +1
Machine learning (ML) is widely used to moderate online content. Despite its scalability relative to human moderation, the use of ML introduces unique challenges to content moderat…
Multi-Group Fairness Evaluation via Conditional Value-at-Risk Testing
Lucas Monteiro Paes, Ananda Theertha Suresh, Alex Beutel +2
Machine learning (ML) models used in prediction and classification tasks may display performance disparities across population groups determined by sensitive attributes (e.g., race…
Word-Level Explanations for Analyzing Bias in Text-to-Image Models
Alexander Lin, Lucas Monteiro Paes, Sree Harsha Tanneru +2
Text-to-image models take a sentence (i.e., prompt) and generate images associated with this input prompt. These models have created award wining-art, videos, and even synthetic da…