a real opportunity for us to repur-
pose that data and serve these under-
served communities.”
The diverse set of projects pre-
sented at the Artificial Intelligence
for Development symposium under-
scored his point. Much of the research
was preliminary, but the initial re-
sults were promising. Shawndra Hill,
an assistant professor in Operations
and Information Management at the
Wharton School of the University of
Pennsylvania, who has also taught at
Addis Ababa University (AAU), spoke
of efforts to improve Ethiopia’s road
safety. Ethiopia has the world’s high-
est rate of traffic fatalities, according
to the World Health Organization,
with a reported 114 deaths per 10,000
vehicles per year. By comparison, the
U.K. has one death per 10,000 vehicles
per year.

“The Ethiopian Traffic Enforcement Agency collects data on every accident that’s reported,” Hill explains. “Where did the accident happen, what did the intersection look like, what’s the road quality, was it raining, and so on.” Working with AAU lecturer Tibebe Beshah, Hill investigated the role of road-related factors in accident severity. The researchers tested classification models to predict the severity of more than 18,000 car accidents and used a projective adaptive resonance theory algorithm to identify the data’s significant patterns. One research finding: Severe physical injuries were more likely to occur on straight, flat roads than on all other types of roads in the same area.

“The methods don’t change,” says Hill. “You could do the same analysis with data from the United States.” In a country that has the highest rate of traffic fatalities in the world, however—and those accidents being among the nation’s leading causes of death— the potential socioeconomic impact is huge. In the future, Hill and her fellow researchers hope to develop new predictive models that combine road data with driver information, and develop a decision support tool for the Ethiopian Traffic Office.

At the Artificial Intelligence for De-
velopment symposium, Eagle and Hor-
vitz presented research in which they
deduced the impact of seismic activ-
ity in the Lac Kivu region of the Demo-
cratic Republic of the Congo from three
years of mobile phone data in neigh-
boring Rwanda. “By watching anoma-
lous call behavior, we could infer the
epicenter of the earthquake,” Horvitz
explains. The researchers could then
make inferences about which areas in
the Lac Kivu region were likely to have
suffered the greatest damage and be
of higher priority for emergency assis-
tance workers. Eagle has used the same
data to better understand the dynam-
ics of urban slums and model the ef-
fects of social networks on infectious
disease outbreaks. And University of
California, Berkeley postdoctoral re-
search fellow Emma Brunskill spoke of
using traveling salesman techniques to
help community health workers in the
developing world—some of whom can
be responsible for up to 4,000 people—
improve the efficiency and timing of
their visits to patients in rural areas.
The data analysis was exploratory, but
Brunskill says she is encouraged by
the potential of existing techniques.
Another area she finds promising is
education. Schools in developing na-
tions often rely on a single computer
per classroom. In experimental trials in
Bangalore, India, Brunskill and a team
of researchers built on foundational
studies in multi-input interfaces to test
the efficacy of an adaptive multi-user
learning game. Initial trials suggested

“our idea was
that we have so
much data, and the
majority of it is being
generated by people
in the developed
world,” says nathan
eagle. “there’s a
real opportunity
for us to repurpose
that data and serve
these underserved
communities.”

that customizing each student’s experience could increase her or his engagement by reducing the likelihood that a single student dominated the game.

Constraints, Costs, Challenges

While AI-D research methods may be
the same as they are in mainstream
Western science, other factors in de-
veloping nations are quite different.
First and foremost are the technol-
ogy constraints. Access to electricity,
computers, and the Internet is limit-
ed in many areas. Language presents
another barrier, as does cost. “The
design considerations are much dif-
ferent,” says Lakshmi Subramanian,
an assistant professor at the Courant
Institute of Mathematical Sciences
at New York University. Subrama-
nian’s research includes the use of
document classification and focused
crawling methods to build offline
educational portals, and computer
vision techniques to detect diabetic
retinopathy, the world’s leading cause
of adult blindness. Yet, according to
Subramanian, constraints are what
make the problems interesting. “If
you can only use SMS, what can you
do? Turns out, you can do a lot, thanks
to semantic compression and other
tools,” he says. “In fact, we’ve built an
SMS search engine in Kenya.”
Gaining access to useful data can
also be a challenge. “There’s no cul-
ture of data like there is in the West,”
says Hill. “Even businesses in Ethiopia
aren’t collecting data like we are.” As a
result, one of Horvitz and Eagle’s pri-
orities is to create a central data reposi-
tory to support new research projects.
They began by compiling a list of useful
resources at the AI-D symposium Web
site, http://www.ai-d.org, from orga-
nizations like the World Bank, World
Trade Organization, and UNICEF. They
are also working with regional organi-
zations, such as telephone companies,
to share additional data.

“We’re trying to set up a Switzerland for data sets,” says Horvitz.

Beyond that, Horvitz and Eagle hope to get more computer scientists involved. Not surprisingly, in such a young field, there are differences of opinion about research, strategies, and direction. “There is a tension inherent in this area, as in the broader computing for development com-