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

Augmented data sheets for speech datasets and ethical decision-making

FaccT, 2023
Orestis Papakyriakopoulos, Anna Seo Gyeong Choi*, William Thong, Dora Zhao, Jerone Andrews, Rebecca Bourke, Alice Xiang, Allison Koenecke*

Human-centric image datasets are critical to the development of computer vision technologies. However, recent investigations have foregrounded significant ethical issues related to privacy and bias, which have resulted in the complete retraction, or modification, of several …

Blog

June 29, 2023 | AI Ethics

New Dataset Labeling Breakthrough Strips Social Constructs in Image Recognition

New Dataset Labeling Breakthrough Strips Social Constructs in Image RecognitionThe outputs of AI as we know them today are created through deeply collaborative processes between humans and machines. The reality is that you cannot …

New Dataset Labeling Breakthrough Strips Social Constructs in Image RecognitionThe outputs of AI as we know them today are created…