Fast, Accurate, and Healthier: Interactive Blurring Helps Moderators Reduce Exposure to Harmful Content

By Anubrata Das, Brandon Dang, Matthew Lease. 2020

48 citations recorded by the source corpus.

While most user content posted on social media is benign, other content, such as violent or adult imagery, must be detected and blocked. Unfortunately, such detection is difficult to automate, due to high accuracy requirements, costs of errors, and nuanced rules for acceptable content. Consequently, social media platforms today rely on a vast workforce of human moderators. However, mounting evidence suggests that exposure to disturbing content can cause lasting psychological and emotional damage to some moderators. To mitigate such harm, we investigate a set of blur-based moderation interfaces for reducing exposure to disturbing content whilst preserving moderator ability to quickly and accurately flag it. We report experiments with Mechanical Turk workers to measure moderator accuracy, speed, and emotional well-being across six alternative designs. Our key findings show interactive blurring designs can reduce emotional impact without sacrificing moderation accuracy and speed.

Subfields: Artificial Intelligence, Gender Studies, Social Psychology, Safety, Robustness & Interpretability

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In Artificial Intelligence

In Gender Studies

Samuel R. Bowman

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Citations in the corpus

Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned (2022, methodology): “• Pay for Time, not Quotas : [16] notes strict task quotas and job performance concerns can create additional stress, on top of the stress caused by viewing harmful content.”