Summary of The Case For Globalizing Fairness: a Mixed Methods Study on Colonialism, Ai, and Health in Africa, by Mercy Asiedu et al.
The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africa
by Mercy Asiedu, Awa Dieng, Iskandar Haykel, Negar Rostamzadeh, Stephen Pfohl, Chirag Nagpal, Maria Nagawa, Abigail Oppong, Sanmi Koyejo, Katherine Heller
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the concept of fairness in machine learning (ML) applications for global health, with a focus on Africa as a case study. The authors conduct a scoping review to propose axes of disparities that need consideration when developing ML-based solutions for African healthcare. They then conduct qualitative research studies with 672 general population participants and 28 experts to gather evidence on these proposed axes. The analysis focuses on colonialism as the attribute of interest, examining its interplay with artificial intelligence (AI), health, and colonialism. The findings highlight specific axes of disparities, including colonial history, country of origin, and national income level. Practical recommendations are provided for developing fairness-aware ML solutions for African healthcare. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is being used more in healthcare, which has raised concerns about bias. The authors of this paper want to make sure that the decisions made by these systems are fair and don’t discriminate against certain groups. They looked at how colonial history affects health outcomes in Africa and how AI can be biased because of this. They also talked to experts and regular people about what they think about AI and colonialism. The results show that there are differences between what experts and regular people think, but both agree that colonial history is important to consider when developing AI for African healthcare. |
Keywords
» Artificial intelligence » Machine learning
