FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity.
By Matthew Arnold, Rachel K. E. Bellamy, Michael Hind, Stephanie Houde, Sameep Mehta, Aleksandra Mojsilovic, Ravi Nair, Karthikeyan Natesan Ramamurthy, Darrell Reimer, Alexandra Olteanu and 3 more. 2018
7 citations recorded by the source corpus.
Subfields: Safety, Robustness & Interpretability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Safety, Robustness & Interpretability
- Karthikeyan Natesan Ramamurthy
- L. Varshney
- Kush R. Varshney
- Richard Socher
- Caiming Xiong
- Rachel K. E. Bellamy
- Nitish Shirish Keskar
Citations in the corpus
- CTRL: A Conditional Transformer Language Model for Controllable Generation (2019, background): “o the included questions, to pose further questions, and suggest solutions by emailing ctrl-monitoring@salesforce.com. Finally, the README asks users to develop appropriate documentation (PAI, 2019; Arnold et al., 2018; Mitchell et al., 2019) when building on CTRL and to tell the research team how t”
- FactSheets: Increasing trust in AI services through supplier's declarations of conformity (2019, methodology): “Subsequent to the first posting of this article [13], Mitchell et al. proposed model cards to convey information that characterizes the evaluation of a machine learning model in a variety of conditions and disclose the context in which models are intended to be used, details of the performance…”