Inverse Counterfactual for AI-Assisted Decision Support: Enhancing Knowledge Elicitation for Capturing Aircraft Pilot Decisions
Bibliographic record
Abstract
Integrating AI into decision-support systems (DSS) for safety-critical domains like aviation requires aligning system behavior with pilot mental models to provide relevant information. Using the Cognitive Shadow—a DSS that models operator decisions and notifies discrepancies—we evaluated a novel knowledge-elicitation technique: the inverse counterfactual. After selecting their preferred option, users modified a single factor to make their second-best option preferable, creating paired cases across their decision boundary. In a simulated adverse-weather avoidance task, 44 participants completed 130 baseline trials and generated counterfactuals for 20 additional cases. Contrary to expectations, the current implementation of the technique did not enhance human-AI model similarity, as measured by the degree of agreement in a 20-case test phase. However, when counterfactuals involved minimal edits—remaining near the decision boundary—predictive accuracy improved and DSS recommendations were more often accepted. Larger edits degraded performance. These findings demonstrate the feasibility of counterfactual elicitation for improving model alignment with user mental models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".