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Accepted Paper:
Algorithmic Transmutation: Applying Emotional Labour Theory to Data Annotation
Noah Khan
(University of Toronto)
Short abstract:
The present paper makes the case for the application of emotional labour theory to data annotation, exploring the psychological impacts on workers tasked with annotating potentially trauma-inducing content in Kenya.
Long abstract:
The present paper applies emotional labour theory to data annotation, exploring the psychological impacts on workers tasked with annotating potentially trauma-inducing content in Kenya. The case study highlights the emotional toll on workers, the emotionality of training datasets, and interfaces of emotional regulation, underscoring emotional labour's relevance in understanding data annotation tasks. This approach challenges current paradigms by advocating for broader labour definitions that include emotional dimensions, calling for improved support and compensation for data annotators to enhance both worker well-being and training dataset quality.