Search Results for author: Abigail Oppong

Found 5 papers, 1 papers with code

The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africa

no code implementations5 Mar 2024 Mercy Asiedu, Awa Dieng, Iskandar Haykel, Negar Rostamzadeh, Stephen Pfohl, Chirag Nagpal, Maria Nagawa, Abigail Oppong, Sanmi Koyejo, Katherine Heller

Whereas experts generally expressed a shared view about the relevance of colonial history for the development and implementation of AI technologies in Africa, the majority of the general population participants surveyed did not think there was a direct link between AI and colonialism.

Attribute Fairness

Towards a Better Understanding of the Computer Vision Research Community in Africa

no code implementations11 May 2023 Abdul-Hakeem Omotayo, Mai Gamal, Eman Ehab, Gbetondji Dovonon, Zainab Akinjobi, Ismaila Lukman, Houcemeddine Turki, Mahmod Abdien, Idriss Tondji, Abigail Oppong, Yvan Pimi, Karim Gamal, Ro'ya-CV4Africa, Mennatullah Siam

Moreover, we study all computer vision publications beyond top-tier venues in different African regions to find that mainly Northern and Southern Africa are publishing in computer vision with 68. 5% and 15. 9% of publications, resp.

3D Reconstruction object-detection +2

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

no code implementations5 Apr 2023 Mercy Nyamewaa Asiedu, Awa Dieng, Abigail Oppong, Maria Nagawa, Sanmi Koyejo, Katherine Heller

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

AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages

1 code implementation7 Nov 2022 Bonaventure F. P. Dossou, Atnafu Lambebo Tonja, Oreen Yousuf, Salomey Osei, Abigail Oppong, Iyanuoluwa Shode, Oluwabusayo Olufunke Awoyomi, Chris Chinenye Emezue

In this paper, we present AfroLM, a multilingual language model pretrained from scratch on 23 African languages (the largest effort to date) using our novel self-active learning framework.

Active Learning Language Modelling +4

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