LabelAId: Just-in-time AI Interventions for Improving Human Labeling Quality and Domain Knowledge in Crowdsourcing Systems

Chu Li, Zhihan Zhang, Michael Saugstad, Esteban Safranchik, Minchu Kulkarni, Xiaoyu Huang, Shwetak Patel, Vikram Iyer, Tim Althoff, Jon Froehlich

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We introduce LabelAId, an ML-based inference system to provide just-in-time feedback during crowdsourced labeling to improve data quality and user expertise. LabelAId consists of (1) a novel ML-based pipeline for detecting labeling mistakes, which is efficiently trained to infer label correctness based on user behavior and domain knowledge; (2) a real-time ML model and UI that tracks worker behavior and intervenes when an inferred mistake is occurring.

Abstract

Crowdsourcing platforms have transformed distributed problem-solving, yet quality control remains a persistent challenge. Traditional quality control measures, such as prescreening workers and refining instructions, often focus solely on optimizing economic output. This paper explores just-in-time AI interventions to enhance both labeling quality and domain-specific knowledge among crowdworkers. We introduce LabelAId, an advanced inference model combining Programmatic Weak Supervision (PWS) with FT-Transformers to infer label correctness based on user behavior and domain knowledge. Our technical evaluation shows that our LabelAId pipeline consistently outperforms state-of-the-art ML baselines, improving mistake inference accuracy by 36.7% with 50 downstream samples. We then implemented LabelAId into Project Sidewalk, an open-source crowdsourcing platform for urban accessibility. A between-subjects study with 34 participants demonstrates that LabelAId significantly enhances label precision without compromising efficiency while also increasing labeler confidence. We discuss LabelAId’s success factors, limitations, and its generalizability to other crowdsourced science domains.