Portrait de Jin Guo

Jin Guo

Membre académique associé
guojin@mila.quebec
Professeur adjoint, McGill University, École d'informatique
Sujets de recherche
IA centrée sur l'humain
IA responsable
Interaction humain-IA
Interaction humain-machine (IHM)
Vie privée
Site web

Biographie

Jin L.C. Guo a obtenu son doctorat à l'Université de Notre Dame. Elle s'intéresse à l'utilisation des techniques d'intelligence artificielle pour résoudre des problèmes de génie logiciel. Ses recherches récentes portent sur la connaissance du domaine minier à partir des données de traçabilité logicielle et sur l'utilisation de ces connaissances pour faciliter les tâches automatisées de génie logiciel telles que la recherche de traces et les questions et réponses sur les projets. Avant son doctorat, elle a travaillé au laboratoire de recherche de Fuji Xerox dans les domaines du traitement de l'image et de la vision par ordinateur.

Étudiants actuels

Avinash Avinash
Doctorat - McGill
avinash.bhat@mila.quebec
Site web
Github
Google Scholar
Billy Exarhakos
Maîtrise recherche - McGill
billy.exarhakos@mila.quebec
Lyu Fuyuan
Doctorat - McGill
Superviseur⋅e principal⋅e :
Xue (Steve) Liu
fuyuan.lyu@mila.quebec
Site web
Github
Google Scholar
Jazlyn Jazlyn
Doctorat - McGill
jazlynhellman@gmail.com
Fanny Lasne
Stagiaire de recherche - McGill
Superviseur⋅e principal⋅e :
Jackie Cheung
fanny.lasne@mila.quebec
Github
LinkedIn
Tamara Paris Paris
Doctorat - McGill
Co-superviseur⋅e :
AJung Moon
tamaraprs21@gmail.com
Shalaleh Rismani
Postdoctorat - McGill
Superviseur⋅e principal⋅e :
Jackie Cheung
rismanis@mila.quebec
Site web
Google Scholar
Sara Tavakoli
Maîtrise recherche - McGill
sara.tavakoli@mila.quebec
Site web
Github
Veronica Xia
Maîtrise recherche - McGill
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… (voir plus) 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 (publié)
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… (voir plus)ural Language Processing Open Source Software (NLP-OSS 2023). 2023.
2022-12-31
Workshop for Natural Language Processing Open Source Software (publié)
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… (voir plus)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 (publié)
doi.org