The ethics of data self-reporting: important issues and best practices

Abstract

Background: This article examines the ethics of data self-reporting, in light of the extreme challenges thrown up by the COVID-19 pandemic. In many countries the public was asked to self-report personal social and health data often through the use of mobile apps, as various datasets were mobilised for the purpose of fighting COVID-19. Policy and implications: The article observes a number of dimensions that make the governance of self-reporting projects particularly thorny. The spectrum of self-reporting is extremely diverse. Projects can be scientific, commercial or other, can raise important privacy concerns, can be vulnerable to harm due to organised manipulation and poor governance. They change over time, with a tendency to function creep. The greatest scientific potential is through linkage between heterogeneous data sources; however, these practices are also the source of the highest risks for privacy and harms. Also, people take part for the most heterogeneous of purposes. Closely controlling their hopes, aims, and beliefs is usually beyond reach. This introduces various biases in the data. It can also introduce risks of self-harm because of the conclusions that people might draw. Lastly, participation in self-reporting is not equally distributed across society. When there are benefits associated in participation, it can exacerbate existing inequalities. Recommendations: The article illustrates three areas of emerging best practice in data governance: bottom-up models such as data trusts and data cooperatives; solidarity as a touchstone principle; and proactive research ethics processes and committees beyond public research institutions. These promising innovations deserve experimentation. Conclusions: Flexible and sustained ethical oversight is key. It is important to act proactively instead of reactively. Best practices must be adapted to the local setting and improved over time.