scrban

Scrban is a theoretical concept in data governance and online moderation that describes a coordinated approach to data cleaning and enforcement of participation restrictions on a digital platform. In this framework, scrubbing refers to redacting or removing sensitive information and non-compliant content, while ban denotes the application of access restrictions to users or accounts that violate policies. The term is used primarily in academic, policy, and design discussions about automated moderation workflows that are still auditable and subject to human review.

Origin and usage: Scrban is a portmanteau of scrub and ban. It is used to analyze how data handling and user sanctions can be tied together to improve policy compliance, transparency, and accountability in moderation systems. The concept is not tied to a single implementation and is discussed as a design pattern rather than a specific product.

Architecture and workflow: In a scrban workflow, content or user signals pass through a scrubbing module that applies redaction, removal, or alteration of sensitive material, followed by an enforcement layer that assigns sanctions such as warnings, temporary suspensions, or permanent bans. An audit log, risk scoring, and an appeals mechanism are typically included to provide accountability and enable human review.

Reception and limitations: Proponents argue that scrban can standardize moderation, reduce policy drift, and improve user trust. Critics caution against overbroad scrubbing, potential biases in automated decisions, privacy concerns, and the risk of chilling effects if bans are applied too aggressively.

Related topics include data governance, content moderation, automated moderation, and data scrubbing.