Big Data – Governance.

Governance is central in this third article in the series on Big Data. Good governance can support transparency and strengthen accountability structures necessary for a fair and ethical approach to big data projects.

Definition

In this context, Governance [hereinafter referred to as Board] focuses on strengthening the support base between public administration decisions and business data policy. This involves cooperation with the environment to ensure that the projects are people and environment oriented and do not lose themselves in an overly technical approach.

Risks

Many Data-driven projects lack support from the community and run the risk of an overly technical discourse that displaces local participation. A technological approach is often seen as politically neutral, but is perceived by the community as an exclusive approach. In such a way that it still carries an, often undesirable, political charge. Without proper coordination with the public environment, the project is likely to exceed prevailing norms and values. And where it is still a gray area, good governance is all the more necessary, so that interpretation can take place on the basis of reciprocity and respect.

Balance.

The condition for an ethically responsible and administratively balanced project is that this community is driven. It is precisely allowing local communities to participate in a timely manner when it comes to involvement in decision-making is essential. Good governance ensures reciprocity and mutual help to shape data projects. This concerns data management such as access, ownership and privacy. Good governance must ensure a respectful data management policy that enables people to determine whether, how and for what purposes and for how long their data can be used.

Sources:

Big Data, Communities and Ethical Resilience: A Framework for Action By 2013 Bellagio/PopTech Fellows Kate Crawford, Gustavo Faleiros, Amy Luers, Patrick Meier, Claudia Perlich and Jer Thorp. Draft Date: Oct. 24, 2013
Monroy-Hernandez, A., E. Kiciman, D. Boyd, and S. Counts. 2012. Tweeting the Drug War: Empowerment, Intimidation, and Regulation in Social Media. HCIC. [online] URL: http://research.microsoft.com/apps/pubs/default.aspx?id=168809
Robertson, J. 2013. How big data Could Help Identify the Next Felon – Or Blame the Wrong Guy. [online] URL: http://www.bloomberg.com/news/2013-08-14/how-big-data-could-help-identify-the-next-felon-or-blame-the-wrongguy.html
For example, see Solove, D. 2011 Nothing to Hide: The False Tradeoff Between Privacy and Security. New Haven: Yale University Press.
For example, see the Fair Information Practice Principles: http://www.ftc.gov/reports/privacy3/fairinfo.shtm
Crawford, K., and J. Schultz. 2013. big data and Due Process: Towards a Framework to Redress Predictive Privacy Harms. Boston College Law Review. 55(1). [online] URL: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2325784