Thank you to everyone for joining this presentation.
I’m Chinasa T. Okolo, a Computer Science PhD student at Cornell University.
I’ll be discussing findings from our team’s work
examining community health workers’ perceptions of AI in rural India.
Let’s begin!
As AI continues to spread widely into domains such as agriculture,
government, and healthcare, most conversations regarding its implications
focus on communities in the Global North, such as the US and Europe.
As nonprofits, tech companies, and governments rush to build and deploy AI systems,
failing to examine the knowledge, needs, and perceptions of the frontline workers who will
be expected to operate these systems, risks deploying AI in ways that lead to extra work
and inefficiencies, or that harm the very communities they intend to serve.
The shortage of qualified medical professionals in many low-income countries
has led to the establishment of programs in which community
health workers provide essential health services in hard-to-reach communities.
Prior work has shown that CHWs are essential in delivering healthcare
services and also that mobile technologies play an important role in their work.
These community health workers are likely to be the target users of AI systems that aim to improve
the health of marginalized communities via, for example,
AI-assisted disease prediction and diagnosis.
Thus, as advances in AI lead researchers and tech companies to develop AI applications
that target the health of marginalized communities, it is crucially important to study:
How do community health workers perceive AI and what are the anticipated benefits and challenges?
In our paper, we present a qualitative study examining the AI knowledge and perceptions
of community health workers in rural India.
We created an exploratory video provocation in which a CHW visits a mother and her sick
child. The CHW uses an AI-enabled app to scan the baby and diagnose them with pneumonia.
To encourage balanced and diverse responses, we created two versions of the video: a positive
scenario, in which the mother embraces the use of the AI application on her child, and a negative
scenario, in which the mother is suspicious and distrustful of the AI application. After
viewing one of these videos, CHWs participated in a 40-minute semi-structured interview.
Although our findings suggest that CHWs don’t have much knowledge of AI they do have opinions
regarding the perceived impact of AI on their workflows, communities, and their livelihoods.
CHW’s low levels of AI knowledge is concerning,
given that AI applications that target their work are already in active development and
early deployments of AI-enabled technologies have already reported failures that, at best,
created additional workflow inefficiencies and, at worst, harmed the communities they aimed to help.
CHWs agreed broadly that an AI app might be able to accomplish some of their tasks, but
it would never completely replace the breadth of activities they perform, including consultations,
immunizations, and culturally-sensitive discussions on breastfeeding and family planning.
Many CHWs felt that having the AI app perform tasks for them would make their work much more
efficient, saving both them and their patients valuable time when making decisions about if and
when to seek further medical help. Participants quickly identified that the improved efficiency
would be especially useful in rural or remote villages that might be located far away from
hospitals or health centers. Additionally, CHWs felt that this app could be used for training them
and solving problems far beyond its capabilities like COVID infections.
While all HCI4D research should have a deep understanding of local contexts,
AI technologies present new societal risks and complexities that must be
proactively studied before attempting deployment.
Concerns raised by our findings suggest CHWs may trust the AI’s expertise more than their own,
perceive AI to be infallible, and ascribe to it more capabilities than it possesses.
These utopian views could have far-reaching consequences
if, for example, CHWs act on incorrect predictions instead of challenging them.
We highlight opportunities for HCI
and AI researchers interested in deploying AI with communities in the Global South
to understand the challenges of developing AI systems that respect diverse communities’
cultural and value differences, while ensuring safe and equitable outcomes.
For more detail, please check out our paper at
{bit.ly/AI_CHW_mHealth} and feel free to reach out at chinasa@cs.cornell.edu.
Thank you for watching and we look forward to seeing you at the discussion!