AI in Global Health: The View from the Front Lines

Ismail, Azra; Kumar, Neha

doi:10.1145/3411764.3445130

Cited by 38 publications

(13 citation statements)

References 146 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As Ismail and Kumar [32] contend, communities should ultimately be the ones to decide whether and how they would like to use AI. If the former condition is met and the community agrees that an AI solution may be relevant and useful, the latter requires the inclusive design of an AI system through a close and continued relationship between the AI researcher and those impacted.…”

Section: Community Participationmentioning

confidence: 99%

Envisioning Communities: A Participatory Approach Towards AI for Social Good

Bondi

Acosta-Navas

et al. 2021

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society

View full text Add to dashboard Cite

Research in artificial intelligence (AI) for social good presupposes some definition of social good, but potential definitions have been seldom suggested and never agreed upon. The normative question of what AI for social good research should be "for" is not thoughtfully elaborated, or is frequently addressed with a utilitarian outlook that prioritizes the needs of the majority over those who have been historically marginalized, brushing aside realities of injustice and inequity. We argue that AI for social good ought to be assessed by the communities that the AI system will impact, using as a guide the capabilities approach, a framework to measure the ability of different policies to improve human welfare equity. Furthermore, we lay out how AI research has the potential to catalyze social progress by expanding and equalizing capabilities. We show how the capabilities approach aligns with a participatory approach for the design and implementation of AI for social good research in a framework we introduce called PACT, in which community members affected should be brought in as partners and their input prioritized throughout the project. We conclude by providing an incomplete set of guiding questions for carrying out such participatory AI research in a way that elicits and respects a community's own definition of social good. CCS CONCEPTS• Human-centered computing → Participatory design; • Computing methodologies → Artificial intelligence; • General and reference → Cross-computing tools and techniques.

show abstract

Section: Community Participationmentioning

confidence: 99%

Envisioning Communities: A Participatory Approach Towards AI for Social Good

Bondi

Acosta-Navas

et al. 2021

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society

View full text Add to dashboard Cite

show abstract

“…The MLE selects data to use to train the model and to test the model. This data can come from a number of sources, both within the MLE's own team or organisation, or can be sourced as secondary data from peers, commercially, or from open public sources, including Government or city authorities or global actors including UN or The World Bank [14].…”

Section: The ML Engineermentioning

confidence: 99%

Identifying Roles, Requirements and Responsibilities in Trustworthy AI Systems

Barclay¹,

Abramson²

2021

Preprint

View full text Add to dashboard Cite

Artificial Intelligence (AI) systems are being deployed around the globe in critical fields such as healthcare and education. In some cases, expert practitioners in these domains are being tasked with introducing or using such systems, but have little or no insight into what data these complex systems are based on, or how they are put together. In this paper, we consider an AI system from the domain practitioner's perspective and identify key roles that are involved in system deployment. We consider the differing requirements and responsibilities of each role, and identify a tension between transparency and privacy that needs to be addressed so that domain practitioners are able to intelligently assess whether a particular AI system is appropriate for use in their domain.

show abstract

“…Ismail and Kumar outline the use of AI in frontline health throughout the Global South, analyzing the motivations behind such work, the stakeholders involved, and how well such applications engage with local communities. 16 The authors base this research on their extensive background conducting ethnographic fieldwork with community health workers in India, methods not commonly used by AI researchers. Their paper also provides actionable design considerations for AI systems being developed for social good that expand beyond healthcare, setting the stage for more impactful AI applications integrated in low-resource regions and with disadvantaged populations.…”

Section: Introductionmentioning

confidence: 99%

Optimizing human-centered AI for healthcare in the Global South

Okolo

2022

Patterns

View full text Add to dashboard Cite

Over the past 60 years, artificial intelligence (AI) has made significant progress, but most of its benefits have failed to make a significant impact within the Global South. Current practices that have led to biased systems will prevent AI from being actualized unless significant efforts are made to change them. As technical advances in AI and an interest in solving new problems lead researchers and tech companies to develop AI applications that target the health of marginalized communities, it is crucially important to study how AI can be used to empower those on the front lines in the Global South and how these tools can be optimally designed for marginalized communities. This perspective examines the landscape of AI for healthcare in the Global South and the evaluations of such systems and provides tangible recommendations for AI practitioners and human-centered researchers to incorporate in the development of AI systems for use with marginalized populations.Concept: Basic principles of a new data science output observed and reported ll

show abstract

AI in Global Health: The View from the Front Lines

Cited by 38 publications

References 146 publications

Envisioning Communities: A Participatory Approach Towards AI for Social Good

Envisioning Communities: A Participatory Approach Towards AI for Social Good

Identifying Roles, Requirements and Responsibilities in Trustworthy AI Systems

Optimizing human-centered AI for healthcare in the Global South

Contact Info

Product

Resources

About