Data deprivations, data gaps and digital divides: Lessons from the COVID-19 pandemic

Abstract

This paper draws lessons from the COVID-19 pandemic for the relationship between data-driven decision making and global development. The lessons are that (i) users should keep in mind the shifting value of data during a crisis, and the pitfalls its use can create; (ii) predictions carry costs in terms of inertia, overreaction and herding behaviour; (iii) data can be devalued by digital and data deluges; (iv) lack of interoperability and difficulty reusing data will limit value from data; (v) data deprivation, digital gaps and digital divides are not just a by-product of unequal global development, but will magnify the unequal impacts of a global crisis, and will be magnified in turn by global crises; (vi) having more data and even better data analytical techniques, such as artificial intelligence, does not guarantee that development outcomes will improve; (vii) decentralised data gathering and use can help to build trust – particularly important for coordination of behaviour.

Information Flow and Data Gaps in COVID-19 Recording and Reporting at National and Provincial Levels in Indonesia

“…The findings of our study also revealed the importance of implementing proper information technologies for the integration of health information systems across Indonesia. The need for interoperability to ensure coordinated response and decision-making at national and local levels was also emphasized by Luengo-Oroz et al [ 67 , 68 ]. By drawing on the lessons learned from Taiwanese, Singaporean, and Hong Kong success stories [ 69 , 70 ], similar technology and surveillance systems can be utilized for the COVID-19 pandemic management with sufficient strategic planning and vision.…”

The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

“…(7) An urgent recommendation for action is to close existing data gaps [211]. To address data absences as well as data gaps, the following recommendations can be made: widely accessible education; increased awareness of biases; better domain understanding of the field; and effective collaboration between developers, policy makers, and experts (e.g., business, data, law) [236,243]. (8) To specifically address data biases and discrimination, it is recommended that data be collected that can be disaggregated by gender [134] or geography [193].…”

Regulating artificial-intelligence applications to achieve the sustainable development goals

“…Gender inequality in data with male-dominated datasets and manpower should be clarified to avoid discrimination and bias in AI applications [ 22 ]. The same applies to datasets and deep-learning models biased against certain ethnicities [ 23 ].…”