Trustworthiness of Machine-Learning-Based Systems (TrustML)
TrustML facilitates the development of trustworthy machine-learning-based systems: systems that are reliable, secure, explainable, and ethical. The cluster brings together a remarkable set of experts from computer science and engineering, law, business and ethics, and relevant application domains such as finance, manufacturing, education, and medicine. It (a) examines trust-related challenges in these critical domains, (b) helps develop and adopt guidelines for new AI policies, and (c) investigates solutions for building trustworthy systems that professionals and the general public can safely adopt.
Campus
Vancouver
Affiliated UBC Faculty & Postdocs
| Name | Role | Research Interests |
|---|---|---|
| Ford, Cristie | Faculty (G+PS eligible/member) | Law; Regulation; Social, Economical and Political Impacts of Innovations; Laws, Standards and Regulation Impacts; Administrative Law; Ideological, Political, Economical and Social Environments of Social Transformations; Administrative Law; Financial innovation and fintech; financial regulation; Legal innovation and law tech; regulation & governance theory; securities regulation; the legal profession; Innovation and the law; Law, Regulation, Social, Economical and Political Impacts of Innovations, Laws, Standards and Regulation Impacts, Administrative Law, Ideological, Political, Economical and Social Environments of Social Transformations, Administrative law, Financial innovation and fintech, Financial regulation, Legal innovation and law tech, Regulation & governance theory, Securities regulation, The legal profession, Innovation and the law |
| Lee, Gene | Faculty (G+PS eligible/member) | AI in Business; Business Analytics; Information Systems; Social Media Analysis; Mobile Ecosystem; AI in Business, Business Analytics, Information Systems, Social Media Analysis, Mobile Ecosystem |
| Mesbah, Ali | Faculty (G+PS eligible/member) | Computer and Software Systems; software engineering |
| Rubin, Julia | Faculty (G+PS eligible/member) | Computer Systems; software engineering; Software quality, security, and robustness; program analysis; Adversarial robustness, explainability, and interpretability of ML-based systems; Mobile and cloud software; Computer systems, Software engineering, Software quality, security, and robustness, Program analysis, Adversarial robustness, explainability, and interpretability of ML-based systems, Mobile and cloud software |