Active learning with biased non-response to label requests

• URL: http://arxiv.org/abs/2312.08150v2
• Date: Mon, 11 Mar 2024 09:43:47 GMT
• Title: Active learning with biased non-response to label requests
• Authors: Thomas Robinson, Niek Tax, Richard Mudd, and Ido Guy
• Abstract summary: Non-response to label requests can impact active learning's effectiveness in real-world contexts. We conceptualise this degradation by considering the type of non-response present in the data. We propose a cost-based correction to the sampling strategy to mitigate the impact of biased non-response.
• Score: 5.940553820027303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Active learning can improve the efficiency of training prediction models by identifying the most informative new labels to acquire. However, non-response to label requests can impact active learning's effectiveness in real-world contexts. We conceptualise this degradation by considering the type of non-response present in the data, demonstrating that biased non-response is particularly detrimental to model performance. We argue that biased non-response is likely in contexts where the labelling process, by nature, relies on user interactions. To mitigate the impact of biased non-response, we propose a cost-based correction to the sampling strategy--the Upper Confidence Bound of the Expected Utility (UCB-EU)--that can, plausibly, be applied to any active learning algorithm. Through experiments, we demonstrate that our method successfully reduces the harm from labelling non-response in many settings. However, we also characterise settings where the non-response bias in the annotations remains detrimental under UCB-EU for specific sampling methods and data generating processes. Finally, we evaluate our method on a real-world dataset from an e-commerce platform. We show that UCB-EU yields substantial performance improvements to conversion models that are trained on clicked impressions. Most generally, this research serves to both better conceptualise the interplay between types of non-response and model improvements via active learning, and to provide a practical, easy-to-implement correction that mitigates model degradation.

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