A 3-part podcast on Data for Good where Gry Hasselbalch discusses new data landscapes, power dynamics in data, inequities and concrete solutions to redress (some of) with UNESCO’s John Crowley and Iulia Sevciuc.
Part 1: Power in and of data
The data systems we witness forming follow, unsurprisingly, the existing power dynamics. They drive current inequities and inequalities even further. They also give rise to new groups – within countries and on the global scale – of haves and have nots. This part delves into:
- Power – how we live in economies of data and should talk power dynamics, equity, ethics, commons, (re)distribution and so much more;
- Inequities – what skews are being in-built into the new data systems and why they need addressing sooner rather than later;
- Privacy divide – why privacy became a luxury good at the core of yet another divide within and, more starkly, between countries; and
- Data reporting – how pressure builds up for data equity, much like for eco-friendliness before, pushing businesses and governments towards accountability and reporting.
Part 2: Data and governance
The COVID-19 crisis did not create but it did expose limitations in capacities and regulations of the new data systems. This part goes deep into:
- Data culture – why governments need to follow suit of the private sector and build standing capacities from within not only to regulate but to also bank on new data in the very act of governing;
- Regulations – how governments need to master the balancing act of being permissive enough for data to thrive yet provide guarantee against misuse; and
- Skills – how the rest need to become data scientists and how the data scientists need to become much more.
Part 3: Data and policy
The key concern at the UNESCO Inclusive Policy Lab is connecting knowledge and data to policy on the ground. Part 3 talks about these issues in relation to data itself (i.e., data as an area of knowledge and of policy action):
- Knowledge gaps – what we know and what we lack in knowledge on data; and
- Policy use – what deserves increased attention in debates and decision-making on data.