A View From Somewhere: Human-Centric Face RepresentationsDownload PDF

Published: 01 Feb 2023, Last Modified: 12 Mar 2024ICLR 2023 posterReaders: Everyone
Keywords: similarity, faces, annotator bias, computer vision, cognitive, mental representations, diversity
Abstract: Few datasets contain self-identified demographic information, inferring demographic information risks introducing additional biases, and collecting and storing data on sensitive attributes can carry legal risks. Besides, categorical demographic labels do not necessarily capture all the relevant dimensions of human diversity. We propose to implicitly learn a set of continuous face-varying dimensions, without ever asking an annotator to explicitly categorize a person. We uncover the dimensions by learning on A View From Somewhere (AVFS) dataset of 638,180 human judgments of face similarity. We demonstrate the utility of our learned embedding space for predicting face similarity judgments, collecting continuous face attribute values, attribute classification, and comparative dataset diversity auditing. Moreover, using a novel conditional framework, we show that an annotator's demographics influences the \emph{importance} they place on different attributes when judging similarity, underscoring the \emph{need} for diverse annotator groups to avoid biases. Data and code are available at \url{https://github.com/SonyAI/a_view_from_somewhere}.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
Submission Guidelines: Yes
Please Choose The Closest Area That Your Submission Falls Into: Social Aspects of Machine Learning (eg, AI safety, fairness, privacy, interpretability, human-AI interaction, ethics)
TL;DR: Implicit discovery of face-varying dimensions and annotator bias by learning on a novel face similarity dataset
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 5 code implementations](https://www.catalyzex.com/paper/arxiv:2303.17176/code)
22 Replies