{"id":"W4412494397","doi":"10.1177/10711813251358254","title":"Inverse Counterfactual for AI-Assisted Decision Support: Enhancing Knowledge Elicitation for Capturing Aircraft Pilot Decisions","year":2025,"lang":"en","type":"article","venue":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thales (Canada); Université Laval","funders":"Mitacs","keywords":"Counterfactual thinking; Inverse; Computer science; Decision support system; Operations research; Artificial intelligence; Human–computer interaction; Engineering; Psychology; Mathematics; Social psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01069584,0.0009583155,0.00045532,0.001054022,0.0004294446,0.001712783,0.001196201,0.000970478,0.002049742],"category_scores_gemma":[0.08806062,0.0004082219,0.0004830539,0.0005748029,0.0008888634,0.002440794,0.002247722,0.0008053074,0.0003413613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000505163,"about_ca_system_score_gemma":0.001007763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007952038,"about_ca_topic_score_gemma":0.001059956,"domain_scores_codex":[0.9915174,0.005912306,0.0005634459,0.0008466524,0.001005313,0.0001548999],"domain_scores_gemma":[0.9029111,0.07759418,0.005583159,0.01059668,0.002653299,0.000661613],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005919465,0.002136511,0.03779845,0.001550864,0.0003134721,0.00072658,0.02444362,0.03215954,0.1804073,0.01300128,0.001718995,0.699824],"study_design_scores_gemma":[0.0008615518,0.004724937,0.04394582,0.000575547,0.0003803557,0.001761281,0.003425402,0.6980568,0.1784105,0.04673662,0.02053996,0.0005813453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5079104,0.0001656796,0.4860501,0.0003558602,0.00004944148,0.0008600898,0.0002575589,0.002071286,0.002279616],"genre_scores_gemma":[0.6355855,0.00005340551,0.363268,0.00008164231,0.00001341472,0.0005097688,0.0001811689,0.00007934846,0.0002276525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01069584,"threshold_uncertainty_score":0.05656564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03608528499541347,"score_gpt":0.2984748903865226,"score_spread":0.2623896053911092,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}