Portrait of AJung Moon

AJung Moon

Core Academic Member
moonajung@mila.quebec
Associate professor, McGill University, Department of Electrical and Computer Engineering
Research Topics
AI Ethics
AI Safety
Fairness
Human-AI interaction
Human-Centered AI
Human-Computer Interaction (HCI)
Human-Robot Interaction
Robot Ethics
Robotics
Website
Google Scholar
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Biography

Ajung Moon is an experimental roboticist who investigates how robots and AI systems influence the way people move, behave and make decisions in order to help us design and deploy such autonomous intelligent systems more responsibly.

At McGill University, she is the director of the McGill Responsible Autonomy and Intelligent System Ethics (RAISE) lab. This is an interdisciplinary initiative that investigates the social and ethical implications of robots and AI systems, and explores what it means for engineers to be designing and deploying such systems responsibly for a better, technological future.

Current Students

Rahatul Amin Ananto
PhD - McGill University
Leah Davis
PhD - McGill University
Website
Github
Lucero Fuentes Ramos
Master's Research - McGill University
Website
David Marino
PhD - McGill University
Github
Google Scholar
Tamara Paris Paris
PhD - McGill University
Principal supervisor :
Jin Guo
Lixiao Zhu
PhD - McGill University

Publications

Measuring What Matters: Connecting AI Ethics Evaluations to System Attributes, Hazards, and Harms
Shalaleh Rismani
Renee Shelby
Leah Davis
Negar Rostamzadeh
AJung Moon
Over the past decade, an ecosystem of measures has emerged to evaluate the social and ethical implications of AI systems, largely shaped by … (see more)high-level ethics principles. These measures are developed and used in fragmented ways, without adequate attention to how they are situated in AI systems. In this paper, we examine how existing measures used in the computing literature map to AI system components, attributes, hazards, and harms. Our analysis draws on a scoping review resulting in nearly 800 measures corresponding to 11 AI ethics principles. We find that most measures focus on four principles – fairness, transparency, privacy, and trust – and primarily assess model or output system components. Few measures account for interactions across system elements, and only a narrow set of hazards is typically considered for each harm type. Many measures are disconnected from where harm is experienced and lack guidance for setting meaningful thresholds. These patterns reveal how current evaluation practices remain fragmented, measuring in pieces rather than capturing how harms emerge across systems. Framing measures with respect to system attributes, hazards, and harms can strengthen regulatory oversight, support actionable practices in industry, and ground future research in systems-level understanding.
2025-10-14
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (published)
doi.org
arxiv.org
Opening the Scope of Openness in AI
Tamara Paris
AJung Moon
Jin L.C. Guo
2025-06-22
Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (published)
doi.org
arxiv.org
Roboethics for Everyone – A Hands-On Teaching Module for K-12 and Beyond
Rahatul Amin Ananto
Shalaleh Rismani
Lixiao Zhu
Christopher Yee Wong
AJung Moon
In this work, we address the evolving landscape of roboethics, expanding beyond physical safety to encompass broader societal implications. … (see more)Recognizing the siloed nature of existing initiatives to teach and inform ethical implications of artificial intelligence (AI) and robotic systems, we present a roboethics teaching module designed for K-12 students and general audiences. The module focuses on the high-level analysis of the interplay between robot behaviour design choices and ethics, using everyday social dilemmas. We delivered the module in a workshop to high school students in Montreal, Canada. From this experience, we observed that the module successfully fostered critical thinking and ethical considerations in students, without requiring advanced technical knowledge. This teaching module holds promise to reach a wider range of populations. We urge the education community to explore similar approaches and engage in interdisciplinary training opportunities regarding the ethical implications of AI and robotics.
2024-12-19
Proceedings of the Canadian Engineering Education Association (CEEA) (published)
doi.org
From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems
Shalaleh Rismani
Roel Dobbe
AJung Moon
2024-10-28
ArXiv (preprint)
doi.org
arxiv.org
How different mental models of AI-based writing assistants impact writers’ interactions with them
Shalaleh Rismani
Su Lin Blodgett
A.R. Olteanu
Q. Vera Liao
AJung Moon
2024-05-10
Proceedings of the Third Workshop on Intelligent and Interactive Writing Assistants (published)
doi.org
Investigating Robot Influence on Human Behaviour By Leveraging Entrainment Effects
Lixiao Zhu
AJung Moon
2024-03-10
Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (published)
doi.org
Perspectives on Robotic Systems for the Visually Impaired.
Christopher Yee Wong
Rahatul Amin Ananto
Tanaka Akiyama
Joseph Paul Nemargut
AJung Moon
Many roboticists hope to build robots and develop technologies that would one day help vulnerable populations to improve their quality of li… (see more)fe. As there are over 2.2 billion people with visual impairments in the world, this vulnerable population is a prime target for robotic assistants to help. In a discussion with a Certified Orientation and Mobility Specialist, someone who helps individuals with visual impairments navigate and perform daily tasks effectively, some interesting and counterintuitive questions were raised about technological developments, particularly robots. While these devices were meant to help the BVI population, many are, in reality, not practically beneficial. In this article, we highlight certain misconceptions about the BVI population and their needs. We emphasize the mismatch between robotics research and the needs of the individuals with visual impairments, especially from the lens of HRI researchers.
2024-03-10
Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (published)
doi.org
Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review.
Vania Karami
Mark J. Yaffe
Genevieve Gore
AJung Moon
S. A. Rahimi
2024-02-29
Archives of gerontology and geriatrics (Print) (published)
doi.org
No such thing as one-size-fits-all in AI ethics frameworks: a comparative case study
Vivian Qiang
Jimin Rhim
AJung Moon
2023-12-31
AI Soc. (published)
doi.org
Improving Generalization in Reinforcement Learning Training Regimes for Social Robot Navigation
Adam Sigal
Hsiu-Chin Lin
AJung Moon
In order for autonomous mobile robots to navigate in human spaces, they must abide by our social norms. Reinforcement learning (RL) has emer… (see more)ged as an effective method to train robot sequential decision-making policies that are able to respect these norms. However, a large portion of existing work in the field conducts both RL training and testing in simplistic environments. This limits the generalization potential of these models to unseen environments, and undermines the meaningfulness of their reported results. We propose a method to improve the generalization performance of RL social navigation methods using curriculum learning. By employing multiple environment types and by modeling pedestrians using multiple dynamics models, we are able to progressively diversify and escalate difficulty in training. Our results show that the use of curriculum learning in training can be used to achieve better generalization performance than previous training methods. We also show that results presented in many existing state-of-the art RL social navigation works do not evaluate their methods outside of their training environments, and thus do not reflect their policies' failure to adequately generalize to out-of-distribution scenarios. In response, we validate our training approach on larger and more crowded testing environments than those used in training, allowing for more meaningful measurements of model performance.
2023-10-26
NeurIPS.cc/2023/Workshop/GenPlan (published)
openreview.net
openreview.net
Driving into the Loop: Mapping Automation Bias and Liability Issues for Advanced Driver Assistance Systems
Katie Szilagyi
Jason Millar
AJung Moon
Shalaleh Rismani
2023-10-06
Digital Society (published)
doi.org
Beyond the ML Model: Applying Safety Engineering Frameworks to Text-to-Image Development
Shalaleh Rismani
Renee Shelby
Andrew J Smart
Renelito Delos Santos
AJung Moon
Negar Rostamzadeh
Identifying potential social and ethical risks in emerging machine learning (ML) models and their applications remains challenging. In this … (see more)work, we applied two well-established safety engineering frameworks (FMEA, STPA) to a case study involving text-to-image models at three stages of the ML product development pipeline: data processing, integration of a T2I model with other models, and use. Results of our analysis demonstrate the safety frameworks – both of which are not designed explicitly examine social and ethical risks – can uncover failure and hazards that pose social and ethical risks. We discovered a broad range of failures and hazards (i.e., functional, social, and ethical) by analyzing interactions (i.e., between different ML models in the product, between the ML product and user, and between development teams) and processes (i.e., preparation of training data or workflows for using an ML service/product). Our findings underscore the value and importance of examining beyond an ML model in examining social and ethical risks, especially when we have minimal information about an ML model.
2023-08-28
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (published)
doi.org
arxiv.org