Centrala begrepp
Cognitive biases inherent in human decision-making can perpetuate and amplify existing social disparities in AI models. A multidisciplinary approach is necessary to design annotation systems that are inclusive, transparent, and fair.
Sammanfattning
The article discusses the critical issue of cognitive biases among human annotators and their impact on the development of fair and unbiased AI systems. It highlights how the cognitive biases of annotators, stemming from their lived experiences and social realities, can be propagated into the training data for AI models, leading to the perpetuation and amplification of existing societal biases.
The authors emphasize that while human annotators are invaluable in providing expert judgments and ground truth labels, particularly in domains like healthcare and finance, their cognitive biases can undermine the fairness and reliability of the resulting AI systems. Strategies like employing qualification tests, demographic filters, and sophisticated worker models may not be sufficient to overcome this challenge.
The article proposes a novel framework that focuses on enhancing transparency and robustness in the design of crowdsourcing tasks, drawing insights from the field of human-computer interaction (HCI). This approach aims to mitigate the impact of cognitive biases by incorporating principles of inclusive, transparent, and fair task design.
The authors highlight the need for a multidisciplinary collaboration, involving experts from diverse fields such as ethics, social sciences, law, healthcare, AI/ML, education, communication, and community representation. This collaborative framework is crucial for developing annotation systems that are free from biases, ambiguous instructions, and that account for the complexities of real-world data.
Furthermore, the article emphasizes the importance of maintaining a meticulous approach throughout the entire AI development lifecycle, from dataset selection to algorithm design and evaluation. Researchers are urged to consciously direct their efforts towards leveraging technological advancements to uplift and empower marginalized communities, rather than perpetuating existing biases.