no code implementations • 12 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.
no code implementations • 3 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.
no code implementations • 16 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.
1 code implementation • 5 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.
no code implementations • 17 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.
no code implementations • 27 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.