Testing Stylistic Interventions to Reduce Emotional Impact of Content Moderation Workers

By Sowmya Karunakaran, Rashmi Ramakrishan. 2019

34 citations recorded by the source corpus.

With the rise in user generated content, there is a greater need for content reviews. While machines and technology play a critical role in content moderation, the need for manual reviews still remains. It is known that such manual reviews could be emotionally challenging. We test the effects of simple interventions like grayscaling and blurring to reduce the emotional impact of such reviews. We demonstrate this by bringing in interventions in a live content review setup thus allowing us to maximize external validity. We use a pre-test post-test experiment design and measure review quality, average handling time and emotional affect using the PANAS scale. We find that simple grayscale transformations can provide an easy to implement and use solution that can significantly change the emotional impact of content reviews. We observe, however, that a full blur intervention can be challenging to reviewers.

Subfields: Artificial Intelligence, Information Systems, AI Ethics, Fairness & Society

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Luke S. Zettlemoyer
2. Percy Liang
3. Yejin Choi
4. Mohit Bansal
5. Ethan Perez
6. Samuel R. Bowman
7. Jianfeng Gao
8. Quoc V. Le

In Information Systems

1. Saurav Kadavath
2. Matthew Lease
3. Dawn Drain
4. Dario Amodei
5. Ethan Perez
6. Samuel R. Bowman
7. Brandon Dang

In AI Ethics, Fairness & Society

1. Anubrata Das
2. Matthew Lease
3. Brandon Dang
4. Amanda Askell
5. Samuel R. Bowman

Citations in the corpus

• Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned (2022, methodology): “These include versions of the Positive and Negative Affect Schedule (PANAS) [56] used in [15, 16, 31] and the Scale of Positive and Negative Experience (SPANE) [17] used in [15, 16].”
• Fast, Accurate, and Healthier: Interactive Blurring Helps Moderators Reduce Exposure to Harmful Content (2020, background): “Most recently, Karunakaran and Ramakrishan (2019) presented the first evaluation of grayscale and blurring image transformations, using actual Google review queues and moderators.”
• The Psychological Well-Being of Content Moderators: The Emotional Labor of Commercial Moderation and Avenues for Improving Support (2021, background): “In contrast with Karunakaran and Ramakrishan [80]’s findings regarding static blurring, Das et al.”