AI in the "Real World": Examining the Impact of AI Deployment in Low-Resource Contexts

TL;DR

This paper analyzes the deployment of AI in low-resource regions of the Global South, highlighting challenges, factors affecting success, and advocating for inclusive design that prioritizes marginalized communities as primary stakeholders.

Contribution

It provides a case study on AI deployment in low-resource contexts, emphasizing inclusive design and stakeholder engagement to improve impact and sustainability.

Findings

Deployment challenges in low-resource settings identified

Factors influencing unanticipated AI deployment outcomes analyzed

Recommendations for inclusive AI design and stakeholder involvement proposed

Abstract

As AI becomes integrated throughout the world, its potential for impact within low-resource regions around the Global South have grown. AI research labs from tech giants like Microsoft, Google, and IBM have a significant presence in countries such as India, Ghana, and South Africa. The work done by these labs is often motivated by the potential impact it could have on local populations, but the deployment of these tools has not always gone smoothly. This paper presents a case study examining the deployment of AI by large industry labs situated in low-resource contexts, highlights factors impacting unanticipated deployments, and reflects on the state of AI deployment within the Global South, providing suggestions that embrace inclusive design methodologies within AI development that prioritize the needs of marginalized communities and elevate their status not just as beneficiaries of AI systems but as primary stakeholders.