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Found 5 papers, 1 papers with code
Identifying and Mitigating Privacy Risks Stemming from Language Models: A Survey
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.
Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness
In addition to reproducing discriminatory relationships in the training data, machine learning systems can also introduce or amplify discriminatory effects.
AI Ethics Statements -- Analysis and lessons learnt from NeurIPS Broader Impact Statements
In 2020, the machine learning (ML) conference NeurIPS broke new ground by requiring that all papers include a broader impact statement.
RAFT: A Real-World Few-Shot Text Classification Benchmark
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
Institutionalising Ethics in AI through Broader Impact Requirements
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.
