Publications

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.
2023-08-29
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (published)
doi.org
arxiv.org
Policy composition in reinforcement learning via multi-objective policy optimization
Shruti Mishra
Ankit Anand
Jordan Hoffmann
Nicolas Heess
Martin A. Riedmiller
Abbas Abdolmaleki
Doina Precup
We enable reinforcement learning agents to learn successful behavior policies by utilizing relevant pre-existing teacher policies. The teach… (see more)er policies are introduced as objectives, in addition to the task objective, in a multi-objective policy optimization setting. Using the Multi-Objective Maximum a Posteriori Policy Optimization algorithm (Abdolmaleki et al. 2020), we show that teacher policies can help speed up learning, particularly in the absence of shaping rewards. In two domains with continuous observation and action spaces, our agents successfully compose teacher policies in sequence and in parallel, and are also able to further extend the policies of the teachers in order to solve the task. Depending on the specified combination of task and teacher(s), teacher(s) may naturally act to limit the final performance of an agent. The extent to which agents are required to adhere to teacher policies are determined by hyperparameters which determine both the effect of teachers on learning speed and the eventual performance of the agent on the task. In the humanoid domain (Tassa et al. 2018), we also equip agents with the ability to control the selection of teachers. With this ability, agents are able to meaningfully compose from the teacher policies to achieve a superior task reward on the walk task than in cases without access to the teacher policies. We show the resemblance of composed task policies with the corresponding teacher policies through videos.
2023-08-29
ArXiv (preprint)
doi.org
arxiv.org
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction
Renee Shelby
Shalaleh Rismani
Kathryn Henne
AJung Moon
Negar Rostamzadeh
Paul Nicholas
N'Mah Yilla-Akbari
Jess Gallegos
Andrew J Smart
Emilio Garcia
Gurleen Virk
2023-08-29
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (published)
doi.org
arxiv.org
What does it mean to be a responsible AI practitioner: An ontology of roles and skills
Shalaleh Rismani
AJung Moon
With the growing need to regulate AI systems across a wide variety of application domains, a new set of occupations has emerged in the indus… (see more)try. The so-called responsible Artificial Intelligence (AI) practitioners or AI ethicists are generally tasked with interpreting and operationalizing best practices for ethical and safe design of AI systems. Due to the nascent nature of these roles, however, it is unclear to future employers and aspiring AI ethicists what specific function these roles serve and what skills are necessary to serve the functions. Without clarity on these, we cannot train future AI ethicists with meaningful learning objectives. In this work, we examine what responsible AI practitioners do in the industry and what skills they employ on the job. We propose an ontology of existing roles alongside skills and competencies that serve each role. We created this ontology by examining the job postings for such roles over a two-year period (2020-2022) and conducting expert interviews with fourteen individuals who currently hold such a role in the industry. Our ontology contributes to business leaders looking to build responsible AI teams and provides educators with a set of competencies that an AI ethics curriculum can prioritize.
2023-08-29
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (published)
doi.org
arxiv.org
Beyond performance: the role of task demand, effort, and individual differences in ab initio pilots
Mohammad-Javad Darvishi-Bayazi
Andrew Law
Sergio Mejia Romero
Sion Jennings
Irina Rish
Jocelyn Faubert
2023-08-28
Scientific Reports (published)
doi.org
From Assistive Devices to Manufacturing Cobot Swarms
Monica Li
Bruno Belzile
Ali Imran
Lionel Birglen
Giovanni Beltrame
David St-Onge
This paper provides an overview of the latest trends in robotics research and development, with a particular focus on applications in manufa… (see more)cturing and industrial settings. We highlight recent advances in robot design, including cutting-edge collaborative robot mechanics and advanced safety features, as well as exciting developments in perception and human-swarm interaction. By examining recent contributions from Kinova, a leading robotics company, we illustrate the differences between industry and academia in their approaches to developing innovative robotic systems and technologies that enhance productivity and safety in the workplace. Ultimately, this paper demonstrates the tremendous potential of robotics to revolutionize manufacturing and industrial operations, and underscores the crucial role of companies like Kinova in driving this transformation forward.
2023-08-28
IEEE International Symposium on Robot and Human Interactive Communication (published)
doi.org
Speech Self-Supervised Representations Benchmarking: a Case for Larger Probing Heads
Salah Zaiem
Youcef Kemiche
Titouan Parcollet
Slim Essid
Mirco Ravanelli
2023-08-28
ArXiv (preprint)
doi.org
arxiv.org
Efficient Epistemic Uncertainty Estimation in Regression Ensemble Models Using Pairwise-Distance Estimators
Lucas Berry
David Meger
This work introduces an efficient novel approach for epistemic uncertainty estimation for ensemble models for regression tasks using pairwis… (see more)e-distance estimators (PaiDEs). Utilizing the pairwise-distance between model components, these estimators establish bounds on entropy. We leverage this capability to enhance the performance of Bayesian Active Learning by Disagreement (BALD). Notably, unlike sample-based Monte Carlo estimators, PaiDEs exhibit a remarkable capability to estimate epistemic uncertainty at speeds up to 100 times faster while covering a significantly larger number of inputs at once and demonstrating superior performance in higher dimensions. To validate our approach, we conducted a varied series of regression experiments on commonly used benchmarks: 1D sinusoidal data,
2023-08-25
ArXiv (preprint)
arxiv.org
arxiv.org
Party Prediction for Twitter
Kellin Pelrine
Anne Imouza
Zachary Yang
Jacob-Junqi Tian
Sacha Lévy
Gabrielle Desrosiers-Brisebois
Aarash Feizi
C'ecile Amadoro
André Blais
Jean-François Godbout
Reihaneh Rabbany
2023-08-25
ArXiv (preprint)
doi.org
arxiv.org
A comparison of reinforcement learning frameworks for software testing tasks
Paulina Stevia Nouwou Mindom
Amin Nikanjam
Foutse Khomh
2023-08-24
Empirical Software Engineering (published)
doi.org
arxiv.org
Multivariate Time-Series Anomaly Detection with Contaminated Data: Application to Physiological Signals
Thi Kieu Khanh Ho
Narges Armanfard
2023-08-24
ArXiv (preprint)
doi.org
arxiv.org
Reinforcement Learning Informed Evolutionary Search for Autonomous Systems Testing
Dmytro Humeniuk
Foutse Khomh
Giuliano Antoniol
Evolutionary search-based techniques are commonly used for testing autonomous robotic systems. However, these approaches often rely on compu… (see more)tationally expensive simulator-based models for test scenario evaluation. To improve the computational efficiency of the search-based testing, we propose augmenting the evolutionary search (ES) with a reinforcement learning (RL) agent trained using surrogate rewards derived from domain knowledge. In our approach, known as RIGAA (Reinforcement learning Informed Genetic Algorithm for Autonomous systems testing), we first train an RL agent to learn useful constraints of the problem and then use it to produce a certain part of the initial population of the search algorithm. By incorporating an RL agent into the search process, we aim to guide the algorithm towards promising regions of the search space from the start, enabling more efficient exploration of the solution space. We evaluate RIGAA on two case studies: maze generation for an autonomous ant robot and road topology generation for an autonomous vehicle lane keeping assist system. In both case studies, RIGAA converges faster to fitter solutions and produces a better test suite (in terms of average test scenario fitness and diversity). RIGAA also outperforms the state-of-the-art tools for vehicle lane keeping assist system testing, such as AmbieGen and Frenetic.
2023-08-24
ArXiv (preprint)
doi.org
arxiv.org