Authors

* External authors

Venue

Date

Share

A View From Somewhere: Human-Centric Face Representations

Jerone T. A. Andrews

Przemyslaw Joniak*

Alice Xiang

* External authors

ICLR 2023

2023

Abstract

Few datasets contain self-identified sensitive attributes, inferring attributes risks introducing additional biases, and collecting attributes can carry legal risks. Besides, categorical labels can fail to reflect the continuous nature of human phenotypic diversity, making it difficult to compare the similarity between same-labeled faces. To address these issues, we present A View From Somewhere (AVFS)—a dataset of 638,180 human judgments of face similarity. We demonstrate the utility of AVFS for learning a continuous, low-dimensional embedding space aligned with human perception. Our embedding space, induced under a novel conditional framework, not only enables the accurate prediction of face similarity, but also provides a human-interpretable decomposition of the dimensions used in the human-decision making process, and the importance distinct annotators place on each dimension. We additionally show the practicality of the dimensions for collecting continuous attributes, performing classification, and comparing dataset attribute disparities.

Related Publications

A Taxonomy of Challenges to Curating Fair Datasets

NeurIPS, 2024
Dora Zhao*, Morgan Klaus Scheuerman, Pooja Chitre*, Jerone Andrews, Georgia Panagiotidou*, Shawn Walker*, Kathleen H. Pine*, Alice Xiang

Despite extensive efforts to create fairer machine learning (ML) datasets, there remains a limited understanding of the practical aspects of dataset curation. Drawing from interviews with 30 ML dataset curators, we present a comprehensive taxonomy of the challenges and trade…

Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes

EMNLP, 2024
Yusuke Hirota, Jerone Andrews, Dora Zhao*, Orestis Papakyriakopoulos*, Apostolos Modas, Yuta Nakashima*, Alice Xiang

We tackle societal bias in image-text datasets by removing spurious correlations between protected groups and image attributes. Traditional methods only target labeled attributes, ignoring biases from unlabeled ones. Using text-guided inpainting models, our approach ensures …

Efficient Bias Mitigation Without Privileged Information

ECCV, 2024
Mateo Espinosa Zarlenga*, Swami Sankaranarayanan, Jerone Andrews, Zohreh Shams, Mateja Jamnik*, Alice Xiang

Deep neural networks trained via empirical risk minimisation often exhibit significant performance disparities across groups, particularly when group and task labels are spuriously correlated (e.g., “grassy background” and “cows”). Existing bias mitigation methods that aim t…

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.