What's Fair is Fair: Detecting and Mitigating Encoded Bias in Multimodal Models of Museum Visitor Attention-Reference-Cited by-同舟云学术

What's Fair is Fair: Detecting and Mitigating Encoded Bias in Multimodal Models of Museum Visitor Attention

Published:2021-10-18 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2021 International Conference on Multimodal Interaction
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Author:

Acosta Halim¹,Henderson Nathan¹,Rowe Jonathan¹,Min Wookhee¹,Minogue James²,Lester James¹

Affiliation:

1. Computer Science, North Carolina State University, USA

2. North Carolina State University, USA

Funder

NSF (National Science Foundation)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3462244.3479943

Reference44 articles.

1. Alekh Agarwal Alina Beygelzimer Miroslav Dudík John Langford Hannah Wallach. 2018. A reductions approach to fair classification. Retrieved from https://arxiv.org/abs/1803.02453. Alekh Agarwal Alina Beygelzimer Miroslav Dudík John Langford Hannah Wallach. 2018. A reductions approach to fair classification. Retrieved from https://arxiv.org/abs/1803.02453.

2. Multimodal Machine Learning: A Survey and Taxonomy

3. OpenFace: An open source facial behavior analysis toolkit

4. Accuracy vs. Availability Heuristic in Multimodal Affect Detection in the Wild

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