Automated Retroactive Minimal Moderation (ARMM) refers to a system or process that utilizes automated technology to moderate and regulate content after it has been posted or shared online. This method aims to minimize the need for human intervention in content moderation while ensuring that the content aligns with community guidelines and policies.
ARMM typically involves the use of artificial intelligence (AI) algorithms and machine learning models to analyze and classify online content. These algorithms are designed to detect and filter out potentially offensive, harmful, or inappropriate posts, comments, images, or videos. The moderation process occurs retrospectively, meaning that content is reviewed and moderated after it has already been published or shared.
The term "minimal moderation" in ARMM implies that human interaction is minimal, as the system relies largely on automated processes. However, it does not indicate that there is no human involvement in the moderation process. In cases where ARMM finds content that requires further review or action, it may flag it for human moderators to review and make final decisions.
The main goals of ARMM are to streamline content moderation processes, increase efficiency, and ensure consistency in enforcing community guidelines. By using automated technology to retroactively moderate content, platforms and websites can handle large amounts of user-generated content, saving human moderators' time and effort.
ARMM technology continues to evolve and improve to better address the challenges associated with content moderation in the digital realm, contributing to a safer online environment.
