no code implementations • 27 Sep 2023 • Victoria Smith, Ali Shahin Shamsabadi, Carolyn Ashurst, Adrian Weller
To help researchers and policymakers understand the state of knowledge around privacy attacks and mitigations, including where more work is needed, we present the first technical survey on LM privacy.
no code implementations • 22 Feb 2022 • Carolyn Ashurst, Ryan Carey, Silvia Chiappa, Tom Everitt
In addition to reproducing discriminatory relationships in the training data, machine learning systems can also introduce or amplify discriminatory effects.
no code implementations • 2 Nov 2021 • Carolyn Ashurst, Emmie Hine, Paul Sedille, Alexis Carlier
In 2020, the machine learning (ML) conference NeurIPS broke new ground by requiring that all papers include a broader impact statement.
1 code implementation • 28 Sep 2021 • Neel Alex, Eli Lifland, Lewis Tunstall, Abhishek Thakur, Pegah Maham, C. Jess Riedel, Emmie Hine, Carolyn Ashurst, Paul Sedille, Alexis Carlier, Michael Noetel, Andreas Stuhlmüller
Will models soon solve classification tasks that have so far been reserved for human research assistants?
Ranked #2 on Few-Shot Text Classification on RAFT
no code implementations • 30 May 2021 • Carina Prunkl, Carolyn Ashurst, Markus Anderljung, Helena Webb, Jan Leike, Allan Dafoe
In 2020, the Conference on Neural Information Processing Systems (NeurIPS) introduced a requirement for submitting authors to include a statement on the broader societal impacts of their research.