Sujan Dutta | Tharindu Cyril Weerasooriya

ARTICLE: Annotator Reliability Through In-Context Learning

Sat, 01 Mar 2025 00:00:00 +0000

When humans label data for AI (like deciding if a tweet is offensive), how do we know who’s doing good work versus who’s careless or biased? Traditional methods struggle because on subjective tasks, sincere people genuinely disagree. We built ARTICLE, which uses large language models to check if an annotator’s labels are internally consistent and align with what the AI predicts. This helps identify reliable annotators without silencing minority perspectives—crucial for building fair AI systems that don’t just reflect the majority view.

Rater Cohesion and Quality from a Vicarious Perspective

Fri, 01 Nov 2024 00:00:00 +0000

When labeling controversial content (like tweets about politics), people’s own beliefs strongly influence what they find offensive. We used “vicarious annotation”—asking people not just “Is this offensive to you?” but also “Would a Democrat/Republican/Independent find this offensive?” This revealed fascinating patterns: Republicans were the worst at predicting how others would react, and disagreement spiked on hot-button issues like gun control and abortion. Understanding these patterns helps us build content moderation AI that doesn’t just reflect one group’s values while silencing others.

Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive

Sat, 02 Dec 2023 00:00:00 +0000

We ran a massive experiment: 9 different AI content moderation systems analyzed 92 million YouTube comments about US politics. The results were shocking—different AI systems flagged wildly different content as offensive, with almost no consistency. When we asked humans to label the same content, political identity was a huge factor: Democrats and Republicans disagreed strongly on what’s offensive, especially on hot topics like abortion and gun rights. This proves that “offensiveness” isn’t a fact that AI can learn—it’s a subjective judgment shaped by values. Current content moderation practices that treat one group’s perspective as “truth” are fundamentally unfair.

Example from the Annotation

Vicarious Offense and Noise Audit of Offensive Speech Classifiers

Wed, 01 Feb 2023 00:00:00 +0000