Portrait of Jin Guo

Jin Guo

Associate Academic Member
guojin@mila.quebec
Assistant Professor, McGill University, School of Computer Science
Research Topics
Human-AI interaction
Human-Centered AI
Human-Computer Interaction (HCI)
Privacy
Responsible AI
Website

Biography

Jin L.C. Guo is an assistant professor at the School of Computer Science, McGill University.

She is interested in using AI techniques to solve software engineering problems. Her recent research focuses on mining domain knowledge from software traceability data and using such knowledge to facilitate automated SE tasks, such as trace retrieval and project Q&A.

Guo completed her PhD at the University of Notre Dame. Prior to that, she worked on image processing and computer vision in Fuji Xerox’s research lab.

Current Students

Avinash Avinash
PhD - McGill University
avinash.bhat@mila.quebec
Website
Github
Google Scholar
Billy Exarhakos
Master's Research - McGill University
billy.exarhakos@mila.quebec
Lyu Fuyuan
PhD - McGill University
Principal supervisor :
Xue (Steve) Liu
fuyuan.lyu@mila.quebec
Website
Github
Google Scholar
Jazlyn Jazlyn
PhD - McGill University
jazlynhellman@gmail.com
Fanny Lasne
Research Intern - McGill University
Principal supervisor :
Jackie Cheung
fanny.lasne@mila.quebec
Github
LinkedIn
Tamara Paris Paris
PhD - McGill University
Co-supervisor :
AJung Moon
tamaraprs21@gmail.com
Shalaleh Rismani
Postdoctorate - McGill University
Principal supervisor :
Jackie Cheung
rismanis@mila.quebec
Website
Google Scholar
Sara Tavakoli
Master's Research - McGill University
sara.tavakoli@mila.quebec
Website
Github
Veronica Xia
Master's Research - McGill University
veronica.xia@mila.quebec

Publications

Don't Trust the Process: When Verifiability Undermines AI Accountability
Tamara Paris
AJung Moon
Jin Guo
How do we know if Artificial Intelligence (AI) systems are as performant and responsibly designed as the AI companies claim them to be? In a… (see more) race-driven innovation climate where responsive development requires time and resources, AI developers and providers may be tempted to misrepresent system performance or overstate their commitment to responsible AI principles. Such circumvention is further enabled by limited access to system components and information by external stakeholders, a restriction commonly justified on the grounds of trade secret protection, privacy and security considerations, among others. In response, a growing community of scholars has been developing cryptographic and statistical solutions that aim to enable robust verification of specific claims under constrained access. However, the construction of these solutions rely on a set of shared, yet unexamined, assumptions required to abstract complex real-world governance challenges into computational representations. In this article, we examine the validity of these assumptions. After detailing the conceptual foundation and analytical lens we used to interrogate these abstraction processes, we show that existing technical approaches to developing verifiable AI commit systematic fallacies that compromise the validity of these approaches. While the existing technical verification processes aim to solve critical AI governance problems, we argue that these fallacies create loopholes that can be exploited by dishonest developers and providers, and therefore lead to misplaced trust in these processes. Finally, we discuss how the field of verifiability could be reoriented towards a more nuanced and interdisciplinary approach to develop rigorous verification processes, both technical and non-technical, that support effective AI governance.
2026-06-24
ACM Conference on Fairness, Accountability, and Transparency (published)
doi.org
The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation
Dung Nguyen Manh
Nam Le Hai
Anh T. V. Dau
Anh Minh Nguyen
Khanh Nghiem
Jin Guo
Nghi D. Q. Bui
We present The Vault, an open-source dataset of high quality code-text pairs in multiple programming languages for training large language m… (see more)odels to understand and generate code. We propose methods for thoroughly extracting samples that use both rules and deep learning to ensure that they contain high-quality pairs of code and text, resulting in a dataset of 43 million high-quality code-text pairs. We thoroughly evaluated this dataset and discovered that when used to train common code language models (such as CodeT5, CodeBERT, and CodeGen), it outperforms the same models train on other datasets such as CodeSearchNet. These evaluations included common coding tasks such as code generation, code summarization, and code search. The Vault can be used by researchers and practitioners to train a wide range of big language models that understand code. Alternatively, researchers can use our data cleaning methods and scripts to improve their own datasets. We anticipate that using The Vault to train large language models will improve their ability to understand and generate code, propelling AI research and software development forward. We are releasing our source code and a framework to make it easier for others to replicate our results.
2022-12-31
Findings of the Association for Computational Linguistics: EMNLP 2023 (published)
doi.org
The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation
Dung Nguyen Manh
Nam Le Hai
Anh T. V. Dau
Anh Minh Nguyen
Khanh Nghiem
Jin Guo
Nghi D. Q. Bui
Dung Nguyen Manh, Nam Le Hai, Anh T. V. Dau, Anh Minh Nguyen, Khanh Nghiem, Jin Guo, Nghi D. Q. Bui. Proceedings of the 3rd Workshop for Nat… (see more)ural Language Processing Open Source Software (NLP-OSS 2023). 2023.
2022-12-31
Workshop for Natural Language Processing Open Source Software (published)
doi.org