Automating dynamic consent decisions for the processing of social media data in health research

TL;DR

This paper explores the use of machine learning to automate and personalize consent decisions for social media data in health research, aiming to balance ethical considerations with data utility.

Contribution

It presents an exploratory user study demonstrating the feasibility of predicting consent decisions with reasonable accuracy using machine learning models.

Findings

Predictive models can determine consent flow with reasonable accuracy.

Automated consent prediction can reduce data leaks and ethical concerns.

Study highlights real-world implications of deploying such models.

Abstract

Social media have become a rich source of data, particularly in health research. Yet, the use of such data raises significant ethical questions about the need for the informed consent of those being studied. Consent mechanisms, if even obtained, are typically broad and inflexible, or place a significant burden on the participant. Machine learning algorithms show much promise for facilitating a 'middle ground' approach: using trained models to predict and automate granular consent decisions. Such techniques, however, raise a myriad of follow-on ethical and technical considerations. In this paper, we present an exploratory user study (n = 67) in which we find that we can predict the appropriate flow of health-related social media data with reasonable accuracy, while minimising undesired data leaks. We then attempt to deconstruct the findings of this study, identifying and discussing a number of real-world implications if such a technique were put into practice.