162 citations · 442 across the 7 of their papers we have counts for
4 papers · 1 filter
Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements
Leandro von Werra, Lewis Tunstall, Abhishek Thakur +16
Evaluation is a key part of machine learning (ML), yet there is a lack of support and tooling to enable its informed and systematic practice. We introduce Evaluate and Evaluation o…
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…
Model Cards for Model Reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar +6
Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the in…
Mitigating Unwanted Biases with Adversarial Learning
Brian Hu Zhang, Blake Lemoine, Margaret Mitchell
Machine learning is a tool for building models that accurately represent input training data. When undesired biases concerning demographic groups are in the training data, well-tra…