Musings from The Power List: Gerrit Renner

Gerrit Renner explains why an analytical measurement does not end at the detector – and why trustworthy AI will depend on transparent, traceable data

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Gerrit Renner’s research develops data-processing and chemometric approaches that preserve uncertainty, quality, and provenance throughout analytical workflows.
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Renner treats data processing as part of analytical measurement, with transparent processing steps and quantitative uncertainty and quality measures.
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He argues that machine-actionable analytical data should carry metadata, provenance, uncertainty, and semantic meaning.
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Renner says processing decisions can affect results, yet their rationale and uncertainty are often difficult to reconstruct.
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He believes FAIR data should be built into analytical workflows from acquisition onward, rather than addressed only at the end of a project.

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