Publications

Harms from Increasingly Agentic Algorithmic Systems
Alan Chan
Rebecca Salganik
Alva Markelius
Chris Pang
Nitarshan Rajkumar
Dmitrii Krasheninnikov
Lauro Langosco
Zhonghao He
Yawen Duan
Micah Carroll
Michelle Lin
Alex Mayhew
Katherine Collins
Maryam Molamohammadi
John Burden
Wanru Zhao
Shalaleh Rismani
Konstantinos Voudouris
Umang Bhatt
Adrian Weller … (voir 2 de plus)
David Krueger
Tegan Maharaj
Research in Fairness, Accountability, Transparency, and Ethics (FATE) has established many sources and forms of algorithmic harm, in domains… (voir plus) as diverse as health care, finance, policing, and recommendations. Much work remains to be done to mitigate the serious harms of these systems, particularly those disproportionately affecting marginalized communities. Despite these ongoing harms, new systems are being developed and deployed which threaten the perpetuation of the same harms and the creation of novel ones. In response, the FATE community has emphasized the importance of anticipating harms. Our work focuses on the anticipation of harms from increasingly agentic systems. Rather than providing a definition of agency as a binary property, we identify 4 key characteristics which, particularly in combination, tend to increase the agency of a given algorithmic system: underspecification, directness of impact, goal-directedness, and long-term planning. We also discuss important harms which arise from increasing agency -- notably, these include systemic and/or long-range impacts, often on marginalized stakeholders. We emphasize that recognizing agency of algorithmic systems does not absolve or shift the human responsibility for algorithmic harms. Rather, we use the term agency to highlight the increasingly evident fact that ML systems are not fully under human control. Our work explores increasingly agentic algorithmic systems in three parts. First, we explain the notion of an increase in agency for algorithmic systems in the context of diverse perspectives on agency across disciplines. Second, we argue for the need to anticipate harms from increasingly agentic systems. Third, we discuss important harms from increasingly agentic systems and ways forward for addressing them. We conclude by reflecting on implications of our work for anticipating algorithmic harms from emerging systems.
2023-06-11
ACM Conference on Fairness, Accountability, and Transparency (publié)
doi.org
arxiv.org
Conditions for indexability of restless bandits and an algorithm to compute whittle index – CORRIGENDUM
Nima Akbarzadeh
Aditya Mahajan
2023-06-08
Advances in Applied Probability (publié)
doi.org
Value function estimation using conditional diffusion models for control
Bogdan Mazoure
Walter Talbott
Miguel Ángel Bautista
R Devon Hjelm
Alexander T Toshev
Joshua M. Susskind
2023-06-08
ArXiv (prépublication)
doi.org
openreview.net
Invariant Causal Set Covering Machines
Thibaud Godon
Baptiste Bauvin
Pascal Germain
J. Corbeil
Alexandre Drouin
2023-06-06
ArXiv (prépublication)
doi.org
arxiv.org
Beyond Gaussian Noise: A Generalized Approach to Likelihood Analysis with non-Gaussian Noise
Ronan Legin
Alexandre Adam
Yashar Hezaveh
Laurence Perreault Levasseur
Likelihood analysis is typically limited to normally distributed noise due to the difficulty of determining the probability density function… (voir plus) of complex, high-dimensional, non-Gaussian, and anisotropic noise. This is a major limitation for precision measurements in many domains of science, including astrophysics, for example, for the analysis of the Cosmic Microwave Background, gravitational waves, gravitational lensing, and exoplanets. This work presents Score-based LIkelihood Characterization (SLIC), a framework that resolves this issue by building a data-driven noise model using a set of noise realizations from observations. We show that the approach produces unbiased and precise likelihoods even in the presence of highly non-Gaussian correlated and spatially varying noise. We use diffusion generative models to estimate the gradient of the probability density of noise with respect to data elements. In combination with the Jacobian of the physical model of the signal, we use Langevin sampling to produce independent samples from the unbiased likelihood. We demonstrate the effectiveness of the method using real data from the Hubble Space Telescope and James Webb Space Telescope.
2023-06-05
Astrophysical Journal Letters (publié)
doi.org
arxiv.org
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detection
Eduardo Dadalto Câmara Gomes
Pierre Colombo
Guillaume Staerman
Nathan Noiry
Pablo Piantanida
2023-06-05
ArXiv (prépublication)
doi.org
arxiv.org
Robust Scuba Diver Tracking and Recovery in Open Water Using YOLOv7, SORT, and Spiral Search
Faraz Lotfi
Khalil Virji
Gregory Dudek
Target tracking is a classic problem in computer vision, with numerous applications in robotics. However, tracking targets underwater presen… (voir plus)ts additional complications due to the six degrees of freedom nature of the problem and the challenging visual environment. In this paper, we address the problem of robotic underwater tracking of scuba divers by partitioning it into two parts: vision and control. We propose a new approach that exploits a highly-maneuverable underwater robot to perform experiments in open water, coupling sensing and control for improved performance. To evaluate the temporal stability of different tracking paradigms, we introduce a new metric, frame-to-frame vari-ance, which is better suited to assess the smoothness of detections from the vision side. We implement PID controllers for control and a spiral search algorithm for target recovery in case of a tracking failure. Our approach only uses observations in the image plane, eliminating the need for robot localization or camera calibration. Using a tracking-by-detection paradigm that combines YOLOv7 for target detection, a tuned filtering technique for temporal stability, and a spiral search algorithm for target recovery, we demonstrate promising performance for long-term tracking. We evaluate our proposed paradigm on the VDD-C dataset and deploy it on an underwater robot for several experiments in open water. Our outcomes show consistency with the ones in the initial studies, and the spiral search algorithm demonstrates promising performance for recapturing a target after a tracking failure. Our approach delivers promising performance for robust underwater tracking, achieving successful open-water tracking scenarios in the presence of strong water currents.
2023-06-05
2023 20th Conference on Robots and Vision (CRV) (publié)
doi.org
The Stack: 3 TB of permissively licensed source code
Denis Kocetkov
Raymond Li
Loubna Ben allal
Jia LI
Chenghao Mou
Carlos Muñoz Ferrandis
Yacine Jernite
Margaret Mitchell
Sean Hughes
Thomas Wolf
Dzmitry Bahdanau
Leandro Von Werra
Harm de Vries
Large Language Models (LLMs) play an ever-increasing role in the field of Artificial Intelligence (AI)--not only for natural language proces… (voir plus)sing but also for code understanding and generation. To stimulate open and responsible research on LLMs for code, we introduce The Stack, a 3.1 TB dataset consisting of permissively licensed source code in 30 programming languages. We describe how we collect the full dataset, construct a permissively licensed subset, present a data governance plan, discuss limitations, and show promising results on text2code benchmarks by training 350M-parameter decoders on different Python subsets. We find that (1) near-deduplicating the data significantly boosts performance across all experiments, and (2) it is possible to match previously reported HumanEval and MBPP performance using only permissively licensed data. We make the dataset available at https://hf.co/BigCode, provide a tool called"Am I in The Stack"(https://hf.co/spaces/bigcode/in-the-stack) for developers to search The Stack for copies of their code, and provide a process for code to be removed from the dataset by following the instructions at https://www.bigcode-project.org/docs/about/the-stack/.
2023-06-05
TMLR (accepté)
doi.org
openreview.net
AHA!: Facilitating AI Impact Assessment by Generating Examples of Harms
Zana Buçinca
Chau Minh Pham
Maurice Jakesch
Marco Túlio Ribeiro
A.R. Olteanu
Saleema Amershi
While demands for change and accountability for harmful AI consequences mount, foreseeing the downstream effects of deploying AI systems rem… (voir plus)ains a challenging task. We developed AHA! (Anticipating Harms of AI), a generative framework to assist AI practitioners and decision-makers in anticipating potential harms and unintended consequences of AI systems prior to development or deployment. Given an AI deployment scenario, AHA! generates descriptions of possible harms for different stakeholders. To do so, AHA! systematically considers the interplay between common problematic AI behaviors as well as their potential impacts on different stakeholders, and narrates these conditions through vignettes. These vignettes are then filled in with descriptions of possible harms by prompting crowd workers and large language models. By examining 4113 harms surfaced by AHA! for five different AI deployment scenarios, we found that AHA! generates meaningful examples of harms, with different problematic AI behaviors resulting in different types of harms. Prompting both crowds and a large language model with the vignettes resulted in more diverse examples of harms than those generated by either the crowd or the model alone. To gauge AHA!'s potential practical utility, we also conducted semi-structured interviews with responsible AI professionals (N=9). Participants found AHA!'s systematic approach to surfacing harms important for ethical reflection and discovered meaningful stakeholders and harms they believed they would not have thought of otherwise. Participants, however, differed in their opinions about whether AHA! should be used upfront or as a secondary-check and noted that AHA! may shift harm anticipation from an ideation problem to a potentially demanding review problem. Drawing on our results, we discuss design implications of building tools to help practitioners envision possible harms.
2023-06-04
ArXiv (prépublication)
doi.org
arxiv.org
GUILGET: GUI Layout GEneration with Transformer
Andrey Sobolevsky
Guillaume-Alexandre Bilodeau
Jinghui Cheng
Jin L.C. Guo
2023-06-04
Proceedings of the Canadian Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
High-Throughput Edge Inference for BERT Models via Neural Architecture Search and Pipeline.
Hung-Yang Chang
Seyyed Hasan Mozafari
James J. Clark
Brett H. Meyer
Warren J. Gross
There has been growing interest in improving the BERT inference throughput on resource-constrained edge devices for a satisfactory user expe… (voir plus)rience. One methodology is to employ heterogeneous computing, which utilizes multiple processing elements to accelerate inference. Another methodology is to deploy Neural Architecture Search (NAS) to find optimal solutions in accuracy-throughput design space. In this paper, for the first time, we incorporate NAS with pipelining for BERT models. We show that performing NAS with pipelining achieves on average 53% higher throughput, compared to NAS with a homogeneous system. Additionally, we propose a NAS algorithm that incorporates hardware performance feedback to accelerate the NAS process. Our proposed NAS algorithm speeds up the search process by ~4x, and 5.5x on the design space of the BERT and CNNs, respectively. Also, by exploring the accuracy-throughput design space of BERT models, we demonstrate that performing pipelining then NAS (Pipeline-then-NAS) can lead to solutions with up to 9x higher inference throughput, compared to running homogeneous inference on the BERT-base model, with only a 1.3% decrease in accuracy.
2023-06-04
Proceedings of the Great Lakes Symposium on VLSI 2023 (publié)
doi.org
PAC-Bayesian Learning of Aggregated Binary Activated Neural Networks with Probabilities over Representations
Louis Fortier-Dubois
Gaël Letarte
Benjamin Leblanc
Franccois Laviolette
Pascal Germain
2023-06-04
Proceedings of the Canadian Conference on Artificial Intelligence (publié)
doi.org
arxiv.org