AI that explains itself—by following the science
AI that explains itself—by following the science
Black-box AI can be powerful, but it often can’t tell us why it made a decision. Concept Bottleneck Models (CBMs) try to fix that by predicting human-understandable concepts first, then the final answer. The catch: standard CBMs ignore domain-specific cause-and-effect and usually need lots of labeled concept data.
Enter the Process-Guided Concept Bottleneck Model (PG-CBM). It builds the “bottleneck” out of concepts that reflect real scientific processes and constrains learning to follow those causal links—even when concept labels are sparse.
- Case study: estimating forest above‑ground biomass from satellite data
- Results: lower error and bias vs. multiple baselines, with interpretable intermediate outputs
- Benefits: detects spurious shortcuts, fuses heterogeneous data sources, and offers scientific insight
Why it matters: PG-CBM makes AI more transparent and trustworthy for scientific tasks, where understanding mechanisms is as important as getting the right answer.
Paper: Process-Guided Concept Bottleneck Model by Reza M. Asiyabi, SEOSAW Partnership, Steven Hancock, and Casey Ryan — https://arxiv.org/abs/2601.10562v1
Paper: https://arxiv.org/abs/2601.10562v1
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ExplainableAI Causality EarthObservation RemoteSensing Forestry MachineLearning AITransparency TrustworthyAI
