By Andreas Rasche, Mette Morsing, & Erik Wetter
The pandemic and resulting public-private responses and collaborations and consequent debates have highlighted the importance of, and need for, understanding and managing legitimacy when designing data partnerships.
The COVID-19 pandemic has grown to become one of the largest global health crises in modern history. At the time of writing, there are almost 200 million confirmed cases, over 4 million known deaths, and half the world´s population has been impacted by quarantines and lockdowns. While vaccination campaigns are underway, the pandemic is far from over, and the policy responses increasingly focus and have come to rely on the public use of private company data – e.g. from mobile operators and tech companies – in their strategies to combat the virus.
To better understand population behaviors, Google started sharing global community mobility data, as did Apple. Open data sharing has been increasing, as evident from these more than 230 open data sources curated by the NYU GovLab. Additionally, numerous governments have launched and leveraged industry tools such as mobile phone and app data for COVID-19 surveillance activities. In Europe, the European Commission initially requested all mobile operators to provide access to their customer data in support of the crisis response.
But as the public is becoming increasingly aware of the intrusive nature of location data, critical debate relating to legitimacy of these data partnerships is now being raised by a broad spectrum of institutions. Concerns have been raised from NGOs such as Human Rights Watch and Privacy International, international organizations such as the UN and the OECD, policymakers such as US Congress and the European Parliament, and even national security think tanks such as the RAND Corporation.
As we have shown in our recent paper, cross-sectoral data partnerships with private sector data have been immensely useful in public crisis management including previous disease outbreaks such as Ebola. However, the various forms of these data partnerships – for instance if participation is open and inclusive or closed and exclusive, and whether results are shared openly or selectively – are crucial to determine the perceived legitimacy of the partnerships and their use of data.
As we formulate in our paper, the specific drivers of this legitimacy can be divided into input and output legitimacy of data partnerships. Input legitimacy specifically refers to the governance of a data partnership, where aspects of participation, transparency, and deliberative quality play major roles. A data partnership with inclusive and broad participation both in membership and transparent decision-making, such as the Global Partnership for Sustainable Development Data (GPSDD) can be classified as having high input legitimacy.
Output legitimacy, on the other hand, focuses on the results of a data partnership, where one can look at the outputs and outcomes respectively. The Facebook High-Resolution Settlement Layer (HRSL) was initiated by an exclusive partnership with the Center for International Earth Science Information Network (CIESIN) at Columbia University, and consequently does not have very high input legitimacy in terms of inclusive or transparent participation; however the resulting datasets are publicly available through Columbia and other established open data repositories, which increases the output legitimacy of this initiative.
The pervasive and increasing collection of high resolution personal data by industry and government is as unlikely to go away as the COVID-19 virus and its´ dangerous virus relatives. This means that, going forward, deliberate and transparent design of data partnerships will become even more important to support both the legitimacy of these novel cross-sector collaborations as well as the public health effects they seek to create. Hopefully our article can provide some insights for how to better plan for this.
References:
Rasche, A., Morsing, M., & Wetter, E. 2021. Assessing the Legitimacy of “Open” and “Closed” Data Partnerships for Sustainable Development. Business & Society, 60(3): 547–581.