Search Results for author: Aad van Moorsel

Found 6 papers, 1 papers with code

Verifiable Fairness: Privacy-preserving Computation of Fairness for Machine Learning Systems

no code implementations12 Sep 2023 Ehsan Toreini, Maryam Mehrnezhad, Aad van Moorsel

In this paper, we propose Fairness as a Service (FaaS), a secure, verifiable and privacy-preserving protocol to computes and verify the fairness of any machine learning (ML) model.

Fairness Privacy Preserving

GTV: Generating Tabular Data via Vertical Federated Learning

no code implementations3 Feb 2023 Zilong Zhao, Han Wu, Aad van Moorsel, Lydia Y. Chen

Conditional vector for tabular GANs is a valuable tool to control specific features of generated data.

Privacy Preserving Vertical Federated Learning

A Dataset of Coordinated Cryptocurrency-Related Social Media Campaigns

no code implementations16 Jan 2023 Karolis Zilius, Tasos Spiliotopoulos, Aad van Moorsel

The rise in adoption of cryptoassets has brought many new and inexperienced investors in the cryptocurrency space.

LDRNet: Enabling Real-time Document Localization on Mobile Devices

1 code implementation5 Jun 2022 Han Wu, Holland Qian, Huaming Wu, Aad van Moorsel

Aiming at improving the responsiveness of the IDV process, we propose a new document localization model for mobile devices, LDRNet, to Localize the identity Document in Real-time.

Document Classification

Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context

no code implementations17 Jul 2020 Ehsan Toreini, Mhairi Aitken, Kovila P. L. Coopamootoo, Karen Elliott, Vladimiro Gonzalez Zelaya, Paolo Missier, Magdalene Ng, Aad van Moorsel

As a consequence, we survey in this paper the main technologies with respect to all four of the FEAS properties, for data-centric as well as model-centric stages of the machine learning system life cycle.

BIG-bench Machine Learning Fairness

The relationship between trust in AI and trustworthy machine learning technologies

no code implementations27 Nov 2019 Ehsan Toreini, Mhairi Aitken, Kovila Coopamootoo, Karen Elliott, Carlos Gonzalez Zelaya, Aad van Moorsel

To build AI-based systems that users and the public can justifiably trust one needs to understand how machine learning technologies impact trust put in these services.

BIG-bench Machine Learning Fairness

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