Scientific and computational outputs are increasingly embedded in decision making across healthcare, biotechnology, diagnostics, and AI-assisted systems.
Between probabilistic computation and final decisions lies a largely ungoverned step:
interpretation.
This work defines that gap, explains how it introduces risk, and outlines why it must be treated as a distinct, governable layer.
KEY IDEAS
Interpretation translates outputs into conclusions.
Those conclusions drive decisions.
This step is rarely defined, measured, or governed.
Interpretation compresses uncertainty into structured decisions.
That compression can expand perceived confidence beyond what the evidence supports.
WHY IT MATTERS
Claims can extend beyond evidence
Confidence can exceed support
Language can introduce unintended certainty
Decisions can move faster than validation
Most governance frameworks do not address this layer.
This work forms the conceptual foundation of DAIXIS™.
DAIXIS™ is designed to make scientific interpretation visible, measurable, and defensible inside organizations.
It governs how conclusions are formed, not just the data they are based on.
If your team is making decisions based on scientific or AI-generated outputs, this layer already exists.
The question is whether it is being managed.
Learn More: www.dumstorf.ai or www.daixis.ai
