Blind Spots and Biases: Exploring the Role of Annotator Cognitive Biases in NLP
This work tackles the problem of bias in AI systems for researchers and practitioners, but it is incremental as it focuses on reviewing existing approaches rather than proposing new solutions.
The paper reviews methodologies and investigations to understand how annotator cognitive biases contribute to bias in NLP systems, addressing the challenge that human involvement in AI can introduce new biases.
With the rapid proliferation of artificial intelligence, there is growing concern over its potential to exacerbate existing biases and societal disparities and introduce novel ones. This issue has prompted widespread attention from academia, policymakers, industry, and civil society. While evidence suggests that integrating human perspectives can mitigate bias-related issues in AI systems, it also introduces challenges associated with cognitive biases inherent in human decision-making. Our research focuses on reviewing existing methodologies and ongoing investigations aimed at understanding annotation attributes that contribute to bias.