Systemic Oversight - AFIRRM framework
[Status: Completed]
This project introduces “systemic oversight” with the AFIRRM framework (Adaptivity, Flexibility, Inclusiveness, Responsiveness, Reflexivity, Monitoring) to tackle the ethical challenges in big data health research, such as privacy and discrimination. It aims to establish a common ethical orientation for diverse stakeholders, ensuring more effective and responsible innovation.
Project Team:
Alessandro Blasimme
Effy Vayena
Description
Big data health research is underlined by a plethora of ethical challenges and concerns, such as information privacy and discrimination. Current trends, such as enhancing the granularity of informed consent and specifying legal provisions, are laudable attempts to address these ethical concerns. But such solutions alone cannot have the desired impact unless oversight activities by different stakeholders acquire a common substantive orientation. To remedy this situation in data-driven health research, we propose the concept of systemic oversight. Our approach, based on six defining features (Adaptivity, Flexibility, Inclusiveness, Responsiveness, Reflexivity, and Monitoring - AFIRRM), aims to create common ground for oversight across the landscape of biomedical big data research.
Vayena, E., & Blasimme, A. (2018). Health Research with Big Data: Time for Systemic Oversight. The Journal of Law, Medicine & Ethics, 46(1), 119–129. external page https://doi.org/10.1177/1073110518766026
The AFIRRM framework
This framework provides a systemic oversight approach for big data health research, fostering adaptability, flexibility, inclusiveness, responsiveness, reflexivity, and continuous monitoring to navigate complex ethical challenges.
