MindSet: A Bias-Detection Interface Using a Visual Human-in-the-Loop Workflow
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Author / Producer
Date
2024
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
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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Publication status
published
External links
Book title
Artificial Intelligence. ECAI 2023 International Workshops
Journal / series
Volume
1948
Pages / Article No.
93 - 105
Publisher
Springer
Event
26th European Conference on Artificial Intelligence (ECAI 2023)
Edition / version
Methods
Software
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Date collected
Date created
Subject
User Interfaces; Dataset Bias; Bias in Machine Learning
Organisational unit
09822 - El-Assady, Mennatallah / El-Assady, Mennatallah