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arXiv Open Access 2023

Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa

Mercy Nyamewaa Asiedu Awa Dieng Abigail Oppong Maria Nagawa Sanmi Koyejo +1 lainnya
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Abstrak

With growing machine learning (ML) applications in healthcare, there have been calls for fairness in ML to understand and mitigate ethical concerns these systems may pose. Fairness has implications for global health in Africa, which already has inequitable power imbalances between the Global North and South. This paper seeks to explore fairness for global health, with Africa as a case study. We propose fairness attributes for consideration in the African context and delineate where they may come into play in different ML-enabled medical modalities. This work serves as a basis and call for action for furthering research into fairness in global health.

Topik & Kata Kunci

cs.LG cs.AI cs.CY

Penulis (6)

M

Mercy Nyamewaa Asiedu

A

Awa Dieng

A

Abigail Oppong

M

Maria Nagawa

S

Sanmi Koyejo

K

Katherine Heller

Format Sitasi

APA MLA BibTeX
Asiedu, M.N., Dieng, A., Oppong, A., Nagawa, M., Koyejo, S., Heller, K. (2023). Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa. https://arxiv.org/abs/2304.02190

Akses Cepat

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Informasi Jurnal
Tahun Terbit
2023
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓