Hardware design and the fairness of a neural network

Author:

Guo YuanboORCID,Yan ZheyuORCID,Yu XiaotingORCID,Kong Qingpeng,Xie Joy,Luo KevinORCID,Zeng Dewen,Wu YawenORCID,Jia ZhengeORCID,Shi YiyuORCID

Funder

National Science Foundation

ACCESS - AI Chip Center for Emerging Smart Sytems, sponsored by InnoHK funding, Hong Kong SAR

Publisher

Springer Science and Business Media LLC

Reference48 articles.

1. Zhao, Z.-Q., Zheng, P., Xu, S.-T. & Wu, X. Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learn. Syst. 30, 3212–3232 (2019).

2. Buolamwini, J. & Gebru, T. Gender shades: intersectional accuracy disparities in commercial gender classification. In Proc. 1st Conference on Fairness, Accountability and Transparency (eds Friedler, S. A. & Wilson, C.) 77–91 (PMLR, 2018).

3. Kamulegeya, L. et al. Using artificial intelligence on dermatology conditions in Uganda: a case for diversity in training data sets for machine learning. Afr. Health Sci. 23, 753–763 (2023).

4. Ferryman, K. & Pitcan, M. Fairness in Precision Medicine (Data & Society, 2022).

5. Gurevich, E., El Hassan, B. & El Morr, C. Equity within AI systems: what can health leaders expect? Healthc. Manage. Forum 36, 119–124 (2023).

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