Technology
is neither good nor bad; nor is it neutral...technology’s interaction with the
social ecology is such that technical developments frequently have
environmental, social, and human consequences that go far beyond the immediate
purposes of the technical devices and practices themselves. ~ Melvin
Kranzberg
Following the discussions around open
data, the issue of what data inclusions constitute privacy violations,
especially when it comes to
big data, is perhaps
the biggest concern to open data advocates. To quote Kieron
O’Hara, what we seek is “transparent Government and not transparent citizens”. While the issue of data that should
not be included has been getting a lot of attention, the issue of data
exclusions that should be included has however been getting less
attention.
At the recently concluded Hivos’ Service Delivery Indicators Project Data for Education and Health meetings, attendees
were quite vocal about the political nature of data, and warned that to treat
it as a neutral thing, is to implicitly support the biases built into the
data. Where the data is being used for
policy making, especially resource allocation (!), then it becomes even more
important to pay attention to the motives, blind spots, capacities and indeed,
values of the persons designing the data collection exercise, and the
collecting and analyzing of this data. It’s not a stretch to say that
marginalization and exclusions offline tend to be mirrored during the data
collection process as can be seen in e.g. Kenya where data from North Eastern
Kenya is often left out, with “inaccessibility,
security, expense, capacity” often given as the reasons for the non-collection
of this data. As a result, where the counties wish to use data for policy, they
find themselves having to use proxy data, or else carry out primary research
with their limited resources to correct this gap.
These data gaps matter. When opening up the Kenya Open Data portal,
President Mwai Kibaki said
“The Government data website will be particularly useful to policy makers and
business persons who require timely and accurate information in formulating policies
and making business decisions.” Where there is no data, then policy is deduced
from ‘experience’ and extrapolation, which does a disservice to these areas as
all too often this information, is neither timely nor accurate. It’s become
common, with perhaps the exception of the Central Government, to have data
blank spots over some counties, especially those from North Eastern Kenya.
In addition to data collected by
state and non state actors, this also extends to crowd sourcing platforms developed
to collect information from citizens via citizen reports. A cursory look at the
platforms deployed for election, water, infrastructure monitoring etc. have
most of the reports from citizens clustered around the major cities and towns,
and as an interesting peculiarity of Kenyan data, areas that are not arid or
semi-arid. It’s interesting how one can almost get a perfect match between
rainfall patterns and socio-economic wellbeing in Kenya with the arid and semi
arid lands tending to do more poorly than their greener counterparts.
Other pertinent exclusions include
data on Persons with Disabilities, with the attendant policy implications.
Where data is not collected on say, accessible health and education
infrastructure, then this could negatively impact educational and health
outcomes of PWDs. To illustrate, if the data suggests that there are 20
facilities available to citizens in a certain county, if all 20 facilities are inaccessible to PWDs, then this number
should read “0”. Where this data is not available, then the assumption is that
the PWDs in the county are being served, while in truth they aren’t and this
invisibility of the persons is reflected in subsequent policy actions and
resources allocation.
It is perhaps not overstating to
suggest that these exclusions are seen where real world exclusions apply e.g.
People living in informal urban settlements tend to be missing from the urban
planning process, except as a problem that needs to be resolved. This was
flagged by the mappers of Map Kibera who wanted to give visibility (existence?)
to the people who live, work, worship etc. in Kibera, and was never visible in
any Government maps. Kibera was a blank spot on the Nairobi map until young
Kiberans created the first free and open digital map of their own community.
For policy makers, open data
proponents and civil society, the implications for this are obvious. We need,
when carrying out projects to examine what real world exclusions exist, map
these, and see if they’re mirrored in the data that we’d like to use for
policy. Only then can we say we’ve made a good faith effort in promoting
ethical and equitable data use for policy.
