Jerone
Andrews
Profile
Jerone is a research scientist and contributes to the Sony AI Gastronomy and AI Ethics flagship projects. His research interests and expertise span computer vision and deep learning, in particular self-supervised anomaly detection, transfer learning, and adversarial machine learning. Prior to joining Sony, Jerone received an MSci in mathematics from King’s College London, which he followed with an EPSRC-funded MRes and Ph.D. in computer science at University College London (UCL). Subsequently, Jerone was awarded a Royal Academy of Engineering Research Fellowship, a British Science Association Media Fellowship with BBC Future, and a Marie Skłodowska-Curie RISE grant. While at UCL, Jerone also spent time as a Visiting Researcher at the National Institute of Informatics (Tokyo) and Telefónica Research (Barcelona).
Message
“As an avid cook, I was thrilled to become a part of the Gastronomy team, where I hope to contribute to the creation of AI tools that help reveal memorable and aesthetically pleasing visual content. I admire and share as a priority Sony AI’s commitment to examining the scientific and social impacts of AI algorithms. In my AI ethics role, I am currently developing auditing tools that can be used to assess the visual diversity of datasets. Through this work, I hope to ensure that datasets accurately represent the visual diversity that exists within and across all demographic groups.”
Publications
As computer vision systems become more widely deployed, there is increasing concern from both the research community and the public that these systems are not only reproducing but amplifying harmful social biases. The phenomenon of bias amplification, which is the focus of t…
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 novel dataset of 638,180 human judgments of face similarity (FAX). We demonstrate the ut…
Biases in human-centric computer vision models are often attributed to a lack of sufficient data diversity, with many demographics insufficiently represented. However, auditing datasets for diversity can be difficult, due to an absence of ground-truth labels of relevant feat…
As computer vision systems become more widely deployed, there is increasing concern from both the research community and the public that these systems are not only reproducing but amplifying harmful social biases. The phenomenon of bias amplification, which is the focus of t…
Biases in human-centric computer vision models are often attributed to a lack of sufficient data diversity, with many demographics insufficiently represented. However, auditing datasets for diversity can be difficult, due to an absence of ground-truth labels of relevant feat…
Blog
December 16, 2021 | Sony AI
Celebrating Black Voices in AI: Sony AI Team Member Jerone Andrews Discusses Mitigating Biases in Visual Datasets at UCL AI Centre Black History Month Event
This past October, in recognition of Black History Month, I was delighted to be invited to participate in a Black History Month panel, Celebrating Black History Month in AI, organized by the UCL AI Centre. The event was shaped to …
This past October, in recognition of Black History Month, I was delighted to be invited to participate in a Black History Month pa…
November 29, 2021 | Sony AI
Meet the Team #2: Lingjuan, Jerone and Roberto
What do privacy, pattern recognition, and percussion all have in common? They are concepts and creative endeavors that have inspired Sony AI team members Lingjuan, Jerone and Roberto. Read on to learn more about these three Sony…
What do privacy, pattern recognition, and percussion all have in common? They are concepts and creative endeavors that have insp…
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