HRT Transgender dataset uses YouTubers' data without consent
Occurred: July 2022
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A dataset supposedly meant to help protect against terrorism caused controversy for scraping the data of YouTube users without permission and exposing their data for years.
Karl Ricanek, a professor of computer science at the University of North Carolina at Wilmington, claimed his HRT Transgender dataset was developed to protect against the possibility of terrorists using Hormone Replacement Therapy (HRT) to avoid facial recognition and sneak across borders undetected, and that he had gained the permission of people whose data had been used.
However, a July 2022 peer-reviewed audit of the project's background and practices by researchers Os Keyes and Jeanie Austin took issue with a number of Ricanek's practices and claims, including:
That the real reason for the dataset was to strengthen national security. The researchers derided the claim as 'ludicrous'.
That Ricanek gained the consent of all those people whose videos he used. This appeared not to be the case.
That only the dataset images were distributed to third parties. But the videos were available via an unprotected Dropbox URL, including those that had been made private or deleted.
That Ricanek stopped giving access to the dataset in 2017. However, it was still accessible on Dropbox five years later.
That the researchers must have hacked Dropbox to access the files. In fact, they gained acess via a UNCW public records request.
The fracas was seen to highlight the need for the consent of users whose data is used in datasets, and for meaningful transparency in the governance of datasets.
System 🤖
Operator:
Developer: University of North Carolina, Wilmington (UNCW)
Country: USA
Sector: Research/academia; Technology
Purpose: Identify HRT users
Technology: Database/dataset; Facial recognition; Computer vision
Issue: Bias/discrimination - LGBTQ; Ethics/values; Privacy; Transparency
Research, advocacy 🧮
Scheuerman M.K., Pape M., Hanna A. (2021). Auto-essentialization: Gender in automated facial analysis as extended colonial project (pdf)
Kumar V., Ramachandra R., Namboodiri A.M., Busch C. (2016). Robust transgender face recognition: Approach based on appearance and therapy factors
Investigations, assessments, audits 🧐
Keyes, O., Austin, J. (2022). Feeling fixes: Mess and emotion in algorithmic audits
News, commentary, analysis 🗞️
Page info
Type: Incident
Published: June 2024