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When remediating one artifact results in another: control, confounders, and correction
1Munich Center for Mathematical Philosophy, LMU Munich, Munich, Germany. david.colaco@lmu.de.
History and Philosophy of the Life Sciences
|January 11, 2024
Summary
Artifact remediation in scientific data can create new issues. Understanding these changes to experimental setups is key to controlling both original and new artifacts, especially with AI tools.
Area of Science:
- Neuroscience
- Data Science
- Philosophy of Science
Background:
- Scientific research often encounters data artifacts that require remediation.
- Remediating one artifact can inadvertently introduce new ones, complicating data integrity.
- Existing philosophical frameworks for artifacts may not fully address remediation complexities.
Purpose of the Study:
- To explore why artifact remediation can lead to new artifacts.
- To examine the implications of remediation as a change to the experimental arrangement.
- To propose how researchers can better account for and control artifacts during remediation.
Main Methods:
- Case study analysis in functional neuroimaging data.
- Philosophical argumentation regarding experimental arrangements and causal modularity.
- Parallel with transparency issues in complex computational systems and AI.
Main Results:
- Remediation alters the experimental arrangement, potentially affecting factors beyond the targeted artifact.
- The concept of causal modularity is insufficient for understanding remediation's scope.
- Determining remediation consequences allows for control of multiple artifacts.
Conclusions:
- Artifact remediation is an intervention that changes the experimental setup.
- Current philosophical accounts need refinement to include artifact correction.
- Future artifact control, especially with AI, requires understanding these complex interactions.
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