心态
工作流程
计算机科学
接口(物质)
人在回路中
人机交互
万维网
情报检索
人工智能
操作系统
数据库
气泡
最大气泡压力法
作者
Senthuran Kalananthan,Alexander Kichutkin,Zhong-Xia Shang,András Strausz,Francisco Javier Sanguino Bautiste,Mennatallah El‐Assady
出处
期刊:Communications in computer and information science
日期:2024-01-01
卷期号:: 93-105
标识
DOI:10.1007/978-3-031-50485-3_8
摘要
Handling data artifacts is a critical and unsolved challenge in deep learning. Disregarding such asymmetries may lead to biased and socially unfair predictions, prohibiting applications in high-stake scenarios. In the case of visual data, its inherently unstructured nature makes automated bias detection especially difficult. Thus, a promising remedy is to rely on human feedback. Hu et al. [14] introduced a three-stage theoretical study framework to use a human-in-the-loop approach for bias detection in visual datasets and ran a small-sample study. While showing encouraging results, no implementation is available to enable researchers and practitioners to study their image datasets. In this work, we present a dataset-agnostic implementation based on a highly flexible web app interface. With this implementation, we aim to bring this theoretical framework into practice by following a user-centric approach. We also extend the framework so that the workflow can be adjusted to the researcher’s needs in terms of the granularity of detected anomalies.
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