The street light effect dates back to a fable by Turkish philosopher, Mulla nasruddin
Mulla nasruddin
The story goes that Mulla had lost his keys
and spent hours looking for them under a street light,
but all that time the key was lying in the dark.
This observational bias can be found in many
fields where we will look for solutions where they are easiest to find
not where they are likely to be found.
This is also the effect that we uncover in our paper
analysing AI efforts in Global Health,
with a focus on applications in the Global South.
Many of these are motivated by the shortage of expert
human and technical resources in underserved settings.
Efforts to advance in Global Health have only increased using the pandemic.
AI systems have been used to determine who gets
vaccinated, for cough-based diagnosis, and for disease forecasting.
We need to ask questions of the systems being deployed.
Who will be using the system?
Where is the data coming from and how is it being collected?
and how does AI fit into the broader ecology and work practices?
We focus on the view from the front lines in our own work.
AI would require humans to collect data,
use the application, or implement its recommendations.
In many countries in the Global South, these are Frontline health workers.
They provide care directly to their communities
and serve as an individual's first point of contact with the Healthcare system.
We reviewed 347 academic and grey literature
to identify applications that have been proposed,
predominantly in the Global South.
We then consider these in light of ethnographic data
that we collected over three years with Frontline health workers
in an underserved Muslim majority religion of Delhi.
This is a region that has been determined to be
at high risk of disease by the state government.
We found that over seventy per cent of applications
were either around diagnosis or disease surveillance.
Other applications included risk assessment, chatbots,
behavior prediction, data quality control, and data digitization.
Development was largely led by global South researchers,
researchers,
but rarely in engagement with other local non-tech actors.
This led to several gaps and assumptions about local contexts.
Our ethnographic data showed the need to consider
the broader political and organizational contexts that the AI is situated in,
and the many invisible frontline health workflows.
For instance, we found that health workers are frequently called upon for quick response
like we saw during the pandemic.
But the disease surveillance applications we reviewed
rarely considered the impact on their workflows.
Health workers also spend a lot of time planning workflows and
focusing on more underserved households
and there is a risk of distorting priorities with new tools for diagnosis and planning.
They also tend to the social cultural dimensions of health, especially in patriarchal households
and these are rarely captured in data.
They were also already involved in a lot of data
collection and additional data needs
could overburden them.
So where do we go from
Many of the official Jersey that uncovered in our analysis are not
There is a lot that we can learn from past ICT interventions
Both the rare successes and the many failures.
First there is a need to foreground user agency.
We must expect targeted users to not use the systems at all,
use them in unexpected ways, or work around the systems altogether.
Many ICT systems are also motivated by the promise of scale to help leapfrog development.
We need to critically question the unit and nature of scale.
We also recommend a move towards supporting self-determination by communities on the development and use of AI.
One approach is for researchers to work closely with the community over the long term
through methodological approaches such as action research
and offering technical support after the project ends.
We also need to think about sustainability of these applications in terms of resource extraction.
We must consider other technical and non-technical solutions before settling on an AI solution.
We also need to foster solidarity and exchange knowledge across disciplines and geographies.
Many of the papers we reviewed asked narrow questions.
They asked: how well can we predict a disease with the given dataset?
rather than: why are patients unable to access treatment in the first place?
Finally we recommend focusing on common struggles that communities face when translating systems across contacts or collaborating with others.
This means supporting the global struggle of frontline health workers for better pay and recognition.