Confidential-PROFITT: Confidential PROof of FaIr Training of Trees
(ICLR) Proposes fair decision tree learning algorithms along with zero-knowledge proof protocols to obtain a proof of fairness on the audited server.
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FairProof: Confidential and Certifiable Fairness for Neural Networks arxiv.org
(Arxiv) Proposes an alternative paradigm to traditional auditing using crytographic tools like Zero-Knowledge Proofs; gives a system called FairProof for verifying fairness of small neural networks.
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Online Fairness Auditing through Iterative Refinement dl.acm.org
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Active Fairness Auditing proceedings.mlr.press
(ICML) Studies of query-based auditing algorithms that can estimate the demographic parity of ML models in a query-efficient manner.
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Auditing fairness under unawareness through counterfactual reasoning sciencedirect.com
(Information Processing & Management) Shows how to unveil whether a black-box model, complying with the regulations, is still biased or not.
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Proof of Learning arxiv.org
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NBDT: Neural-Backed Decision Trees openreview.net
ICLR'21, 2021. [All Versions]. [Code]. Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use. In response, previous work combines decision trees with deep learning, yielding models…
Audit Algorithms › Papers > 2023: “(ICLR) Proposes fair decision tree learning algorithms along with zero-knowledge proof protocols to obtain a proof of fairness on the audited server.”