430 citations · 784 across the 14 of their papers we have counts for
6 papers · 1 filter
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Alexander D'Amour, Katherine Heller, Dan Moldovan +37
ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures. An ML pipeline i…
Measuring and Reducing Gendered Correlations in Pre-trained Models
Kellie Webster, Xuezhi Wang, Ian Tenney +6
Pre-trained models have revolutionized natural language understanding. However, researchers have found they can encode artifacts undesired in many applications, such as professions…
Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias
Ana Valeria Gonzalez, Maria Barrett, Rasmus Hvingelby +2
The one-sided focus on English in previous studies of gender bias in NLP misses out on opportunities in other languages: English challenge datasets such as GAP and WinoGender highl…
Scalable Cross Lingual Pivots to Model Pronoun Gender for Translation
Kellie Webster, Emily Pitler
Machine translation systems with inadequate document understanding can make errors when translating dropped or neutral pronouns into languages with gendered pronouns (e.g., English…
Social Biases in NLP Models as Barriers for Persons with Disabilities
Ben Hutchinson, Vinodkumar Prabhakaran, Emily Denton +3
Building equitable and inclusive NLP technologies demands consideration of whether and how social attitudes are represented in ML models. In particular, representations encoded in…
Automatically Identifying Gender Issues in Machine Translation using Perturbations
Hila Gonen, Kellie Webster
The successful application of neural methods to machine translation has realized huge quality advances for the community. With these improvements, many have noted outstanding chall…