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.


Affiliated UBC Faculty & Postdocs

Name Role Research Interests
Ford, Cristie Faculty (G+PS eligible/member) Law and legal practice; 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; 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) Economics and business administration; Management information systems; Applied Machine Learning; Business Analytics; Computer Science and Statistics; Cybersecurity; Information Systems; Mobile Ecosystem; Social Media Analysis; Text Mining
Mesbah, Ali Faculty (G+PS eligible/member) Electrical engineering, computer engineering, and information engineering; Programming languages and software engineering
Rubin, Julia Faculty (G+PS eligible/member) Computer engineering; Programming languages and software engineering; Computer Systems; software engineering; Software quality, security, and robustness; program analysis; Adversarial robustness, explainability, and interpretability of ML-based systems; Mobile and cloud software