Muffin: A Framework Toward Multi-Dimension AI Fairness by Uniting Off-the-Shelf Models

Model fairness (a.k.a., bias) has become one of the most critical problems in a wide range of AI applications. An unfair model in autonomous driving may cause a traffic accident if corner cases (e.g., extreme weather) cannot be fairly regarded; or it will incur healthcare disparities if the AI model...

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Bibliographic Details
Published in:2023 60th ACM/IEEE Design Automation Conference (DAC) Vol. 2023; pp. 1 - 6
Main Authors: Sheng, Yi, Yang, Junhuan, Yang, Lei, Shi, Yiyu, Hu, Jingtong, Jiang, Weiwen
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 01.07.2023
Subjects:
Artificial intelligence
Correlation
Data models
Dermatology
Design automation
Medical services
model fusing
multi-dimensional fairness
parameters
reinforcement learning
Skin
multi-dimensional fairness
model fusing
parameters
reinforcement learning
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Summary:Model fairness (a.k.a., bias) has become one of the most critical problems in a wide range of AI applications. An unfair model in autonomous driving may cause a traffic accident if corner cases (e.g., extreme weather) cannot be fairly regarded; or it will incur healthcare disparities if the AI model misdiagnoses a certain group of people (e.g., brown and black skin). In recent years, there are emerging research works on addressing unfairness, and they mainly focus on a single unfair attribute, like skin tone; however, real-world data commonly have multiple attributes, among which unfairness can exist in more than one attribute, called "multi-dimensional fairness". In this paper, we first reveal a strong correlation between the different unfair attributes, i.e., optimizing fairness on one attribute will lead to the collapse of others. Then, we propose a novel Multi-Dimension Fairness framework, namely Muffin, which includes an automatic tool to unite off-the-shelf models to improve the fairness on multiple attributes simultaneously. Case studies on dermatology datasets with two unfair attributes show that the existing approach can achieve 21.05% fairness improvement on the first attribute while it makes the second attribute unfair by 1.85%. On the other hand, the proposed Muffin can unite multiple models to achieve simultaneously 26.32% and 20.37% fairness improvement on both attributes; meanwhile, it obtains 5.58% accuracy gain.
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DOI:10.1109/DAC56929.2023.10247765