Fall 2020

Anubrata Das

Anubrata Das

Ph.D. Student
UT Austin
anubrata@utexas.edu

Time

Friday, 10/02/20 at 12:00 PM

Title

Worker Well-being and Content Moderation

Watch the Video

Abstract

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.

Bio

Anubrata Das is a third-year Ph.D. student at the School of Information, University of Texas at Austin. His advisor is Dr. Matt Lease. His broad research interests are explainable AI and human computation.

https://anubrata.github.io/