Machine Learning Data Practices through a Data Curation Lens: An Evaluation Framework
Eshta Bhardwaj
Harshit Gujral
Siyi Wu
Ciara Zogheib
Tegan Maharaj
Christoph Becker
Studies of dataset development in machine learning call for greater attention to the data practices that make model development possible and… (see more) shape its outcomes. Many argue that the adoption of theory and practices from archives and data curation fields can support greater fairness, accountability, transparency, and more ethical machine learning. In response, this paper examines data practices in machine learning dataset development through the lens of data curation. We evaluate data practices in machine learning as data curation practices. To do so, we develop a framework for evaluating machine learning datasets using data curation concepts and principles through a rubric. Through a mixed-methods analysis of evaluation results for 25 ML datasets, we study the feasibility of data curation principles to be adopted for machine learning data work in practice and explore how data curation is currently performed. We find that researchers in machine learning, which often emphasizes model development, struggle to apply standard data curation principles. Our findings illustrate difficulties at the intersection of these fields, such as evaluating dimensions that have shared terms in both fields but non-shared meanings, a high degree of interpretative flexibility in adapting concepts without prescriptive restrictions, obstacles in limiting the depth of data curation expertise needed to apply the rubric, and challenges in scoping the extent of documentation dataset creators are responsible for. We propose ways to address these challenges and develop an overall framework for evaluation that outlines how data curation concepts and methods can inform machine learning data practices.
2024-06-05
The 2024 ACM Conference on Fairness, Accountability, and Transparency (published)
doi.org
arxiv.org
Meta's AI translation model embraces overlooked languages.
David Ifeoluwa Adelani
2024-06-05
Nature (published)
doi.org
Meta's AI translation model embraces overlooked languages.
David Ifeoluwa Adelani
2024-06-05
Nature (published)
doi.org
Noisy Data Visualization using Functional Data Analysis
Haozhe Chen
Andres Felipe Duque Correa
Guy Wolf
Kevin R. Moon
Data visualization via dimensionality reduction is an important tool in exploratory data analysis. However, when the data are noisy, many ex… (see more)isting methods fail to capture the underlying structure of the data. The method called Empirical Intrinsic Geometry (EIG) was previously proposed for performing dimensionality reduction on high dimensional dynamical processes while theoretically eliminating all noise. However, implementing EIG in practice requires the construction of high-dimensional histograms, which suffer from the curse of dimensionality. Here we propose a new data visualization method called Functional Information Geometry (FIG) for dynamical processes that adapts the EIG framework while using approaches from functional data analysis to mitigate the curse of dimensionality. We experimentally demonstrate that the resulting method outperforms a variant of EIG designed for visualization in terms of capturing the true structure, hyperparameter robustness, and computational speed. We then use our method to visualize EEG brain measurements of sleep activity.
2024-06-05
ArXiv (preprint)
doi.org
arxiv.org
A Robot Walks into a Bar: Can Language Models Serve as Creativity SupportTools for Comedy? An Evaluation of LLMs' Humour Alignment with Comedians
Piotr Mirowski
Juliette Love
Kory Wallace Mathewson
Shakir Mohamed
2024-06-05
The 2024 ACM Conference on Fairness, Accountability, and Transparency (published)
doi.org
arxiv.org
Shedding Light on Large Generative Networks: Estimating Epistemic Uncertainty in Diffusion Models
Lucas Berry
Axel Brando
David Meger
Generative diffusion models, notable for their large parameter count (exceeding 100 million) and operation within high-dimensional image spa… (see more)ces, pose significant challenges for traditional uncertainty estimation methods due to computational demands. In this work, we introduce an innovative framework, Diffusion Ensembles for Capturing Uncertainty (DECU), designed for estimating epistemic uncertainty for diffusion models. The DECU framework introduces a novel method that efficiently trains ensembles of conditional diffusion models by incorporating a static set of pre-trained parameters, drastically reducing the computational burden and the number of parameters that require training. Additionally, DECU employs Pairwise-Distance Estimators (PaiDEs) to accurately measure epistemic uncertainty by evaluating the mutual information between model outputs and weights in high-dimensional spaces. The effectiveness of this framework is demonstrated through experiments on the ImageNet dataset, highlighting its capability to capture epistemic uncertainty, specifically in under-sampled image classes.
2024-06-05
ArXiv (preprint)
doi.org
arxiv.org
Temporal trends in disparities in COVID-19 seropositivity among Canadian blood donors
Yuan Yu
Matthew J Knight
Diana Gibson
Sheila F O’Brien
David Buckeridge
W Alton Russell
Abstract Background In Canada’s largest COVID-19 serological study, SARS-CoV-2 antibodies in blood donors have been monitored since 2020. … (see more)No study has analysed changes in the association between anti-N seropositivity (a marker of recent infection) and geographic and sociodemographic characteristics over the pandemic. Methods Using Bayesian multi-level models with spatial effects at the census division level, we analysed changes in correlates of SARS-CoV-2 anti-N seropositivity across three periods in which different variants predominated (pre-Delta, Delta and Omicron). We analysed disparities by geographic area, individual traits (age, sex, race) and neighbourhood factors (urbanicity, material deprivation and social deprivation). Data were from 420 319 blood donations across four regions (Ontario, British Columbia [BC], the Prairies and the Atlantic region) from December 2020 to November 2022. Results Seropositivity was higher for racialized minorities, males and individuals in more materially deprived neighbourhoods in the pre-Delta and Delta waves. These subgroup differences dissipated in the Omicron wave as large swaths of the population became infected. Across all waves, seropositivity was higher in younger individuals and those with lower neighbourhood social deprivation. Rural residents had high seropositivity in the Prairies, but not other regions. Compared to generalized linear models, multi-level models with spatial effects had better fit and lower error when predicting SARS-CoV-2 anti-N seropositivity by geographic region. Conclusions Correlates of recent COVID-19 infection have evolved over the pandemic. Many disparities lessened during the Omicron wave, but public health intervention may be warranted to address persistently higher burden among young people and those with less social deprivation.
2024-06-05
International Journal of Epidemiology (published)
doi.org
Towards Geographic Inclusion in the Evaluation of Text-to-Image Models
Melissa Hall
Samuel J. Bell
Candace Ross
Adina Williams
Michal Drozdzal
Adriana Romero Soriano
Rapid progress in text-to-image generative models coupled with their deployment for visual content creation has magnified the importance of … (see more)thoroughly evaluating their performance and identifying potential biases. In pursuit of models that generate images that are realistic, diverse, visually appealing, and consistent with the given prompt, researchers and practitioners often turn to automated metrics to facilitate scalable and cost-effective performance profiling. However, commonly-used metrics often fail to account for the full diversity of human preference; often even in-depth human evaluations face challenges with subjectivity, especially as interpretations of evaluation criteria vary across regions and cultures. In this work, we conduct a large, cross-cultural study to study how much annotators in Africa, Europe, and Southeast Asia vary in their perception of geographic representation, visual appeal, and consistency in real and generated images from state-of-the art public APIs. We collect over 65,000 image annotations and 20 survey responses. We contrast human annotations with common automated metrics, finding that human preferences vary notably across geographic location and that current metrics do not fully account for this diversity. For example, annotators in different locations often disagree on whether exaggerated, stereotypical depictions of a region are considered geographically representative. In addition, the utility of automatic evaluations is dependent on assumptions about their set-up, such as the alignment of feature extractors with human perception of object similarity or the definition of"appeal"captured in reference datasets used to ground evaluations. We recommend steps for improved automatic and human evaluations.
2024-06-05
The 2024 ACM Conference on Fairness, Accountability, and Transparency (published)
doi.org
arxiv.org
Visibility into AI Agents
Alan Chan
Carson Ezell
Max Kaufmann
Kevin Wei
Lewis Hammond
Herbie Bradley
Emma Bluemke
Nitarshan Rajkumar
David Scott Krueger
Noam Kolt
Lennart Heim
Markus Anderljung
2024-06-05
The 2024 ACM Conference on Fairness, Accountability, and Transparency (published)
doi.org
arxiv.org
Efficient Leverage Score Sampling for Tensor Train Decomposition
Vivek Bharadwaj
Beheshteh T. Rakhshan
Osman Asif Malik
Guillaume Rabusseau
2024-06-04
ArXiv (preprint)
doi.org
arxiv.org
Milnor-Myerson Games and The Principles of Artificial Principal-Agent Problems
Manfred Diaz
Joel Z Leibo
Liam Paull
In this paper, we introduce Milnor-Myerson games, a multiplayer interaction structure at the core of machine learning (ML), to shed light on… (see more) the fundamental principles and implications the artificial principal-agent problem has had in landmark ML results like AlphaGo and large language models (LLMs).
2024-06-04
rl-conference.cc/RLC/2024/Workshop/Finding_the_Frame (poster)
openreview.net
openreview.net
PETRA: Parallel End-to-end Training with Reversible Architectures
Stephane Rivaud
Louis Fournier
Thomas Pumir
Eugene Belilovsky
Michael Eickenberg
Edouard Oyallon
Reversible architectures have been shown to be capable of performing on par with their non-reversible architectures, being applied in deep l… (see more)earning for memory savings and generative modeling. In this work, we show how reversible architectures can solve challenges in parallelizing deep model training. We introduce PETRA, a novel alternative to backpropagation for parallelizing gradient computations. PETRA facilitates effective model parallelism by enabling stages (i.e., a set of layers) to compute independently on different devices, while only needing to communicate activations and gradients between each other. By decoupling the forward and backward passes and keeping a single updated version of the parameters, the need for weight stashing is also removed. We develop a custom autograd-like training framework for PETRA, and we demonstrate its effectiveness on CIFAR-10, ImageNet32, and ImageNet, achieving competitive accuracies comparable to backpropagation using ResNet-18, ResNet-34, and ResNet-50 models.
2024-06-04
ArXiv (preprint)
doi.org
arxiv.org