Portrait de Ian Arawjo

Ian Arawjo

Membre académique associé
Professeur adjoint, Université de Montréal, Département d'informatique et de recherche opérationnelle
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Biographie

Ian Arawjo est professeur adjoint au Département d'informatique et de recherche opérationnelle (DIRO) de l'Université de Montréal. Il détient un doctorat en sciences de l'information de l'Université Cornell, réalisé sous la supervision du professeur Tapan Parikh.

Sa thèse portait sur l'intersection de la programmation informatique et de la culture, explorant la programmation en tant que pratique sociale et culturelle. Il a acquis de l'expérience dans l'application d'une vaste gamme de méthodes liées aux interfaces homme-machine (IHM), allant du travail de terrain ethnographique à la recherche archivistique, en passant par le développement de systèmes novateurs (utilisés par des milliers de personnes) et la réalisation d'études de convivialité. Actuellement, il travaille sur des projets au carrefour de la programmation, de l'IA et de l'IHM, notamment sur la manière dont les nouvelles capacités de l'IA peuvent nous aider à réimaginer la pratique de la programmation.

Il travaille également sur l'évaluation de grands modèles de langage (LLM), à travers des projets en code source libre à forte visibilité tels que ChainForge. Les articles auxquels il a contribué comme premier auteur ont remporté des prix lors de grandes conférences portant sur l’IHM, notamment la Conference on Human Factors in Computing Systems (CHI), la Conference on Computer-Supported Cooperative Work and Social Computing (CSCW) et le Symposium on User Interface Software and Technology (UIST).

Étudiants actuels

Max Henry
Doctorat - UdeM
Site web
Github
LinkedIn
Google Scholar
Munyeong Kim
Doctorat - UdeM
jingyue zhang
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Damien Masson

Publications

Antagonistic AI
Alice Cai
Ian Arawjo
Elena L. Glassman
The vast majority of discourse around AI development assumes that subservient,"moral"models aligned with"human values"are universally benefi… (voir plus)cial -- in short, that good AI is sycophantic AI. We explore the shadow of the sycophantic paradigm, a design space we term antagonistic AI: AI systems that are disagreeable, rude, interrupting, confrontational, challenging, etc. -- embedding opposite behaviors or values. Far from being"bad"or"immoral,"we consider whether antagonistic AI systems may sometimes have benefits to users, such as forcing users to confront their assumptions, build resilience, or develop healthier relational boundaries. Drawing from formative explorations and a speculative design workshop where participants designed fictional AI technologies that employ antagonism, we lay out a design space for antagonistic AI, articulating potential benefits, design techniques, and methods of embedding antagonistic elements into user experience. Finally, we discuss the many ethical challenges of this space and identify three dimensions for the responsible design of antagonistic AI -- consent, context, and framing.
2024-02-11
ArXiv (prépublication)
doi.org
arxiv.org
Antagonistic AI
Alice Cai
Ian Arawjo
Elena L. Glassman
The vast majority of discourse around AI development assumes that subservient,"moral"models aligned with"human values"are universally benefi… (voir plus)cial -- in short, that good AI is sycophantic AI. We explore the shadow of the sycophantic paradigm, a design space we term antagonistic AI: AI systems that are disagreeable, rude, interrupting, confrontational, challenging, etc. -- embedding opposite behaviors or values. Far from being"bad"or"immoral,"we consider whether antagonistic AI systems may sometimes have benefits to users, such as forcing users to confront their assumptions, build resilience, or develop healthier relational boundaries. Drawing from formative explorations and a speculative design workshop where participants designed fictional AI technologies that employ antagonism, we lay out a design space for antagonistic AI, articulating potential benefits, design techniques, and methods of embedding antagonistic elements into user experience. Finally, we discuss the many ethical challenges of this space and identify three dimensions for the responsible design of antagonistic AI -- consent, context, and framing.
2024-02-11
ArXiv (prépublication)
doi.org
arxiv.org
Antagonistic AI
Alice Cai
Ian Arawjo
Elena L. Glassman
The vast majority of discourse around AI development assumes that subservient,"moral"models aligned with"human values"are universally benefi… (voir plus)cial -- in short, that good AI is sycophantic AI. We explore the shadow of the sycophantic paradigm, a design space we term antagonistic AI: AI systems that are disagreeable, rude, interrupting, confrontational, challenging, etc. -- embedding opposite behaviors or values. Far from being"bad"or"immoral,"we consider whether antagonistic AI systems may sometimes have benefits to users, such as forcing users to confront their assumptions, build resilience, or develop healthier relational boundaries. Drawing from formative explorations and a speculative design workshop where participants designed fictional AI technologies that employ antagonism, we lay out a design space for antagonistic AI, articulating potential benefits, design techniques, and methods of embedding antagonistic elements into user experience. Finally, we discuss the many ethical challenges of this space and identify three dimensions for the responsible design of antagonistic AI -- consent, context, and framing.
2024-02-11
ArXiv (prépublication)
doi.org
arxiv.org
Antagonistic AI
Alice Cai
Ian Arawjo
Elena L. Glassman
2024-02-11
ArXiv (prépublication)
doi.org
arxiv.org
Antagonistic AI
Alice Cai
Ian Arawjo
Elena L. Glassman
The vast majority of discourse around AI development assumes that subservient,"moral"models aligned with"human values"are universally benefi… (voir plus)cial -- in short, that good AI is sycophantic AI. We explore the shadow of the sycophantic paradigm, a design space we term antagonistic AI: AI systems that are disagreeable, rude, interrupting, confrontational, challenging, etc. -- embedding opposite behaviors or values. Far from being"bad"or"immoral,"we consider whether antagonistic AI systems may sometimes have benefits to users, such as forcing users to confront their assumptions, build resilience, or develop healthier relational boundaries. Drawing from formative explorations and a speculative design workshop where participants designed fictional AI technologies that employ antagonism, we lay out a design space for antagonistic AI, articulating potential benefits, design techniques, and methods of embedding antagonistic elements into user experience. Finally, we discuss the many ethical challenges of this space and identify three dimensions for the responsible design of antagonistic AI -- consent, context, and framing.
2024-02-11
ArXiv (prépublication)
doi.org
arxiv.org
ChainBuddy: An AI-assisted Agent System for Helping Users Set up LLM Pipelines
jingyue zhang
Ian Arawjo
2023-12-31
UIST (Adjunct Volume) (publié)
doi.org
Notational Programming for Notebook Environments: A Case Study with Quantum Circuits
Ian Arawjo
Anthony DeArmas
Michael Roberts
Shrutarshi Basu
Tapan Parikh
We articulate a vision for computer programming that includes pen-based computing, a paradigm we term notational programming. Notational pro… (voir plus)gramming blurs contexts: certain typewritten variables can be referenced in handwritten notation and vice-versa. To illustrate this paradigm, we developed an extension, Notate, to computational notebooks which allows users to open drawing canvases within lines of code. As a case study, we explore quantum programming and designed a notation, Qaw, that extends quantum circuit notation with abstraction features, such as variable-sized wire bundles and recursion. Results from a usability study with novices suggest that users find our core interaction of implicit cross-context references intuitive, but suggests further improvements to debugging infrastructure, interface design, and recognition rates. Throughout, we discuss questions raised by the notational paradigm, including a shift from ‘recognition’ of notations to ‘reconfiguration’ of practices and values around programming, and from ‘sketching’ to writing and drawing, or what we call ‘notating.’
2022-10-27
ACM Symposium on User Interface Software and Technology (publié)
doi.org
To Write Code: The Cultural Fabrication of Programming Notation and Practice
Ian Arawjo
2020-04-20
International Conference on Human Factors in Computing Systems (publié)
doi.org