Year: 2025
Type: conference-paper
Abstract: Trustworthy AI is crucial for web applications, since it ensures user data privacy, enhances security, and fosters user confidence. As web applications increasingly rely on AI for personalization and decision-making, maintaining transparency and accountability becomes essential to prevent bias, misinformation, and unethical practices. By building trust, developers can create safer and more reliable experiences, ultimately promoting user engagement and satisfaction. However, when dataset sizes grow bigger with the rapid web data collection, it is laborious and expensive to obtain perfect data (e.g., clean, safe, and balanced data). As a result, the volume of imperfect data becomes enormous, e.g., web-scale image and speech data with noisy labels, images with specific noise, and long-tail-distributed data. However, standard learning methods assumes that the supervised information is fully correct and intact. Therefore, imperfect data harms the performance of most of the standard learning algorithms, and sometimes even makes existing algorithms break down. In this tutorial, we focus on the algorithmic design of trustworthy AI when facing three types of imperfect data: noisy data, adversarial data, and long-tailed data in the real-world web applications.
Authors: Jiangchao Yao, Feng Liu, Bo Han
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: Universitas Brawijaya
Awards: G5000189262, G8244644518
OpenAlex ID: W4410636888