Stefaan G. Verhulst and Andrew Zahuranec at Data & Policy blog: “There has been a rapid increase in the number of data-driven projects and tools released to contain the spread of COVID-19. Over the last three months, governments, tech companies, civic groups, and international agencies have launched hundreds of initiatives. These efforts range from simple visualizations of public health data to complex analyses of travel patterns.
When designed responsibly, data-driven initiatives could provide the public and their leaders the ability to be more effective in addressing the virus. The Atlantic andNew York Times have both published work that relies on innovative data use. These and other examples, detailed in our #Data4COVID19 repository, can fill vital gaps in our understanding and allow us to better respond and recover to the crisis.
But data is not without risk. Collecting, processing, analyzing and using any type of data, no matter how good intention of its users, can lead to harmful ends. Vulnerable groups can be excluded. Analysis can be biased. Data use can reveal sensitive information about people and locations. In addressing all these hazards, organizations need to be intentional in how they work throughout the data lifecycle.
Decision Provenance: Documenting decisions and decision makers across the Data Life Cycle
Unfortunately the individuals and teams responsible for making these design decisions at each critical point of the data lifecycle are rarely identified or recognized by all those interacting with these data systems.
The lack of visibility into the origins of these decisions can impact professional accountability negatively as well as limit the ability of actors to identify the optimal intervention points for mitigating data risks and to avoid missed use of potentially impactful data. Tracking decision provenance is essential.
As Jatinder Singh, Jennifer Cobbe, and Chris Norval of the University of Cambridge explain, decision provenance refers to tracking and recording decisions about the collection, processing, sharing, analyzing, and use of data. It involves instituting mechanisms to force individuals to explain how and why they acted. It is about using documentation to provide transparency and oversight in the decision-making process for everyone inside and outside an organization.
Toward that end, The GovLab at NYU Tandon developed the Decision Provenance Mapping. We designed this tool for designated data stewards tasked with coordinating the responsible use of data across organizational priorities and departments….(More)”