Untagger

An 'untagger' is a software or a human actor who performs the act of removing or deleting tags, labels, or metadata associated with digital content, typically images, videos, or text. This process often involves identifying and eliminating irrelevant, inaccurate, or unwanted tags to refine the data, improve its quality, and potentially make it more accessible or compliant with specific standards. The purpose of untagging can range from data cleansing and content moderation to ensuring privacy and preventing misuse of information. Skilled untaggers must possess good judgement to distinguish between appropriate and inappropriate tags.

Untagger meaning with examples

• The company hired a team of untaggers to review the image dataset used for their facial recognition software. They removed tags that were racially biased or violated privacy guidelines, which improved model accuracy. The process involved analyzing images and removing problematic labels, and removing offensive text from the image captions to create a more inclusive system.
• To protect user privacy, the social media platform employed untaggers to remove location tags from posts related to sensitive topics, such as political protests or domestic abuse. This helped protect users from unwanted targeting. The untaggers reviewed the content of the social media to assess where user locations were disclosed and removed those tags to reduce the possibility of harm to their users.
• After a data breach, the organization employed forensic untaggers to remove potentially identifying tags from the leaked documents, to try to make them anonymous, and to limit the damage from sensitive personal data being exposed. This was a time-critical operation to prevent further harm. Untaggers were key in making the data compliant with privacy regulations.
• A researcher, as an untagger, was responsible for cleaning a large text corpus before performing sentiment analysis. He eliminated irrelevant tags that were remnants from the text formatting and removed tags that didn't conform to standardized language rules to get a clean sample, which resulted in more reliable insights from the research.