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.

2024-12-20

Proceedings of the Canadian Engineering Education Association (CEEA) (published)

From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems

Shalaleh Rismani

Roel Dobbe

2024-10-29

ArXiv (preprint)

arxiv.org

How different mental models of AI-based writing assistants impact writers’ interactions with them

Shalaleh Rismani

Su Lin Blodgett

Alexandra Olteanu

Q. Vera Liao

2024-05-11

Proceedings of the Third Workshop on Intelligent and Interactive Writing Assistants (published)

Investigating Robot Influence on Human Behaviour By Leveraging Entrainment Effects

Lixiao Zhu

2024-03-11

Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (published)

Perspectives on Robotic Systems for the Visually Impaired

Christopher Yee Wong

Rahatul Amin Ananto

Tanaka Akiyama

Joseph Paul Nemargut

2024-03-11

Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (published)

Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review.

Vania Karami

Mark J. Yaffe

Genevieve Gore

2024-03-01

Archives of gerontology and geriatrics (Print) (published)

Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review.

Vania Karami

Mark J. Yaffe

Genevieve Gore

2024-03-01

Archives of gerontology and geriatrics (Print) (published)

Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review.

Vania Karami

Mark J. Yaffe

Genevieve Gore

2024-03-01

Archives of gerontology and geriatrics (Print) (published)

Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review.

Vania Karami

Mark J. Yaffe

Genevieve Gore

2024-03-01

Archives of gerontology and geriatrics (Print) (published)

No such thing as one-size-fits-all in AI ethics frameworks: a comparative case study

Vivian Qiang

Jimin Rhim

2024-01-01

AI Soc. (published)

Improving Generalization in Reinforcement Learning Training Regimes for Social Robot Navigation

Adam Sigal

Hsiu-Chin Lin

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.

2023-10-27

NeurIPS.cc/2023/Workshop/GenPlan (published)

openreview.net

Beyond the ML Model: Applying Safety Engineering Frameworks to Text-to-Image Development

Shalaleh Rismani

Renee Shelby

Andrew J Smart

Renelito Delos Santos

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.

2023-08-29

Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (published)