47 citations · 66 across the 5 of their papers we have counts for
5 papers
Evaluation Gaps in Machine Learning Practice
Ben Hutchinson, Negar Rostamzadeh, Christina Greer +2
Forming a reliable judgement of a machine learning (ML) model's appropriateness for an application ecosystem is critical for its responsible use, and requires considering a broad r…
Visual Identification of Problematic Bias in Large Label Spaces
Alex Bäuerle, Aybuke Gul Turker, Ken Burke +4
While the need for well-trained, fair ML systems is increasing ever more, measuring fairness for modern models and datasets is becoming increasingly difficult as they grow at an un…
Thinking Beyond Distributions in Testing Machine Learned Models
Negar Rostamzadeh, Ben Hutchinson, Christina Greer +1
Testing practices within the machine learning (ML) community have centered around assessing a learned model's predictive performance measured against a test dataset, often drawn fr…
Measuring Model Biases in the Absence of Ground Truth
Osman Aka, Ken Burke, Alex Bäuerle +2
The measurement of bias in machine learning often focuses on model performance across identity subgroups (such as man and woman) with respect to groundtruth labels. However, these…
Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure
Ben Hutchinson, Andrew Smart, Alex Hanna +5
Rising concern for the societal implications of artificial intelligence systems has inspired demands for greater transparency and accountability. However the datasets which empower…