Mitigating Label Bias via Decoupled Confident Learning

Autor:	Li, Yunyi, De-Arteaga, Maria, Saar-Tsechansky, Maytal
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	Growing concerns regarding algorithmic fairness have led to a surge in methodologies to mitigate algorithmic bias. However, such methodologies largely assume that observed labels in training data are correct. This is problematic because bias in labels is pervasive across important domains, including healthcare, hiring, and content moderation. In particular, human-generated labels are prone to encoding societal biases. While the presence of labeling bias has been discussed conceptually, there is a lack of methodologies to address this problem. We propose a pruning method -- Decoupled Confident Learning (DeCoLe) -- specifically designed to mitigate label bias. After illustrating its performance on a synthetic dataset, we apply DeCoLe in the context of hate speech detection, where label bias has been recognized as an important challenge, and show that it successfully identifies biased labels and outperforms competing approaches. Comment: AI & HCI Workshop at the 40th International Conference on Machine Learning (ICML), Honolulu, Hawaii, USA. 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.08945 Zobrazit plný text záznamu View this record from Arxiv