The Case for Greener Biomedical Data Science and How to Get There

Area of Science:

• Biomedical Data Science
• Environmental Sustainability
• Computational Ethics

Background:

• Biomedical data science offers significant potential for advancing human health.
• The computational infrastructure required is energy- and resource-intensive, posing environmental challenges.
• Current efforts to mitigate these harms are in early stages.

Purpose of the Study:

• To comprehensively review the environmental harms of biomedical data science.
• To explore technical, ethical, sociological, and regulatory approaches to mitigation.
• To propose strategies for accelerating the institutionalization of sustainable practices.

Main Methods:

• Literature review of existing efforts in environmental mitigation within biomedical data science.
• Analysis of historical institutionalization of animal research ethics for lessons learned.
• Identification of persistent challenges and future considerations.

Main Results:

• A growing consensus exists on the need to address the environmental impact.
• Progress in institutionalizing mitigation strategies is slow.
• Key lessons from animal research ethics can inform current efforts.

Conclusions:

• Accelerating the adoption of sustainable practices requires learning from historical precedents.
• Addressing challenges like the rebound paradox and global equity is crucial.
• Ensuring a responsible and sustainable future for biomedical data science is paramount.