90 citations · 188 across the 13 of their papers we have counts for
14 papers
Towards Reconstructing Geographically Diverse Architecture with 3D Foundation Models
Nicky Kriplani, Yiwen Zhang, Angelina Wang +1
Internet-scale photo collections of real-world landmarks have driven progress in 3D computer vision yet remain highly challenging for modern 3D foundation models (3DFMs) that estim…
Algorithmic Gender Prediction Is Illegitimate, But Gender Imputation Can Yield Valid Measurements
Evan Dong, Angelina Wang
Machine learning ethics researchers and critical HCI scholars have argued that algorithmically predicting gender is wrong. At the same time, other researchers rely on predicted gen…
NAMESAKES: Probing Identity Memorization in Text-to-Image Models
Morris Alper, Vasudha Varadarajan, Moran Yanuka +2
Text-to-image (T2I) models generate realistic likenesses of some individuals when prompted with their names, raising privacy concerns. However, distinguishing whether a generated f…
Taxonomizing and Measuring Representational Harms: A Look at Image Tagging
Jared Katzman, Angelina Wang, Morgan Scheuerman +4
In this paper, we examine computational approaches for measuring the "fairness" of image tagging systems, finding that they cluster into five distinct categories, each with its own…
Overwriting Pretrained Bias with Finetuning Data
Angelina Wang, Olga Russakovsky
Transfer learning is beneficial by allowing the expressive features of models pretrained on large-scale datasets to be finetuned for the target task of smaller, more domain-specifi…
Measuring Representational Harms in Image Captioning
Angelina Wang, Solon Barocas, Kristen Laird +1
Previous work has largely considered the fairness of image captioning systems through the underspecified lens of "bias." In contrast, we present a set of techniques for measuring f…
Gender Artifacts in Visual Datasets
Nicole Meister, Dora Zhao, Angelina Wang +3
Gender biases are known to exist within large-scale visual datasets and can be reflected or even amplified in downstream models. Many prior works have proposed methods for mitigati…
Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation
Angelina Wang, Vikram V. Ramaswamy, Olga Russakovsky
Research in machine learning fairness has historically considered a single binary demographic attribute; however, the reality is of course far more complicated. In this work, we gr…
Understanding and Evaluating Racial Biases in Image Captioning
Dora Zhao, Angelina Wang, Olga Russakovsky
Image captioning is an important task for benchmarking visual reasoning and for enabling accessibility for people with vision impairments. However, as in many machine learning sett…
The Limits of Global Inclusion in AI Development
Alan Chan, Chinasa T. Okolo, Zachary Terner +1
Those best-positioned to profit from the proliferation of artificial intelligence (AI) systems are those with the most economic power. Extant global inequality has motivated Wester…
Directional Bias Amplification
Angelina Wang, Olga Russakovsky
Mitigating bias in machine learning systems requires refining our understanding of bias propagation pathways: from societal structures to large-scale data to trained models to impa…
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets
Angelina Wang, Alexander Liu, Ryan Zhang +6
Machine learning models are known to perpetuate and even amplify the biases present in the data. However, these data biases frequently do not become apparent until after the models…
Learning Robotic Manipulation through Visual Planning and Acting
Angelina Wang, Thanard Kurutach, Kara Liu +2
Planning for robotic manipulation requires reasoning about the changes a robot can affect on objects. When such interactions can be modelled analytically, as in domains with rigid…
Safer Classification by Synthesis
William Wang, Angelina Wang, Aviv Tamar +2
The discriminative approach to classification using deep neural networks has become the de-facto standard in various fields. Complementing recent reservations about safety against…