Black box medicine and transparency
The series of reports Black Box Medicine and Transparency examines the human interpretability of machine learning in healthcare and research:
1. Machine learning landscape considers the broad question of where machine learning is being (and will be) used in healthcare and research for health
2. Interpretable machine learning outlines how machine learning can be or may be rendered human interpretable
3. Ethics of transparency and explanation asks why machine learning should be made transparent or be explained, drawing upon the many lessons that the philosophical literature provides
4. Regulating transparency considers if (and to what extent) does the General Data Protection Regulation (GDPR) require machine learning in the context of healthcare and research to be transparent, human interpretable, or explainable
5. Interpretability by design framework distils the findings of the previous reports, providing a framework to think through human interpretability of machine learning in the context of healthcare and health research
6. Roundtables and interviews summarises the three roundtables and eleven interviews that provided the qualitative underpinning of preceding reports
Each report interlocks, building on the conclusions of preceding reports.
Meanwhile you can start with the executive summary. Does anybody care about it?
