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Natalie Dullerud

4 accepted papers

2023

Confidential-PROFITT: Confidential PROof of FaIr Training of Trees

ICLR 2023top-5%

Post hoc auditing of model fairness suffers from potential drawbacks: (1) auditing may be highly sensitive to the test samples chosen; (2) the model and/or its training data may need to be shared with an auditor thereby breaking confidentiality. We address these issues by instead providing a certifi…

Cited by 22SourcePDFScholar
2022

Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning

ICLR 2022poster

Deep metric learning (DML) enables learning with less supervision through its emphasis on the similarity structure of representations. There has been much work on improving generalization of DML in settings like zero-shot retrieval, but little is known about its implications for fairness. In this p…

Cited by 17SourcePDFScholar
2022

Washing The Unwashable : On The (Im)possibility of Fairwashing Detection

NeurIPS 2022accept

The use of black-box models (e.g., deep neural networks) in high-stakes decision-making systems, whose internal logic is complex, raises the need for providing explanations about their decisions. Model explanation techniques mitigate this problem by generating an interpretable and high-fidelity surr…

2021

CaPC Learning: Confidential and Private Collaborative Learning

ICLR 2021poster

Machine learning benefits from large training datasets, which may not always be possible to collect by any single entity, especially when using privacy-sensitive data. In many contexts, such as healthcare and finance, separate parties may wish to collaborate and learn from each other's data but are…