{"id":"W4283801158","doi":"10.1609/aaai.v36i7.20696","title":"Learning Losses for Strategic Classification","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Corruption and Economic Development","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Vector Institute","keywords":"Computer science; Artificial intelligence; Machine learning; Graph; Sample (material); Perspective (graphical); Similarity (geometry); Function (biology); Graph theory; Theoretical computer science; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001010549,0.00008513127,0.0001170416,0.00007215362,0.00116714,0.0001237598,0.0005742474,0.00003344358,0.001076678],"category_scores_gemma":[0.0002951561,0.00007853324,0.00007555834,0.0002745364,0.0001968394,0.0001107362,0.00009925516,0.0001947152,0.00004101685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000169212,"about_ca_system_score_gemma":0.0002588742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009613884,"about_ca_topic_score_gemma":0.00005817868,"domain_scores_codex":[0.9989246,0.00002600031,0.0003109066,0.0002335007,0.0002819049,0.0002231255],"domain_scores_gemma":[0.9992411,0.00008689037,0.0002860688,0.00006360964,0.0002715764,0.0000508105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006094552,0.00006403566,0.0003261162,0.000009585275,0.000005649726,3.07737e-8,0.005528477,0.0001660877,0.004153306,0.9309962,0.00007254365,0.05861706],"study_design_scores_gemma":[0.0001005174,0.0006115729,0.0009339371,0.00008729069,0.00003385204,0.000002226406,0.4118111,0.03518943,0.02035033,0.4643447,0.06591349,0.0006215626],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8192253,0.000009082804,0.0003252769,0.009543824,0.0008685263,0.0007672296,0.000008288224,0.00008291098,0.1691695],"genre_scores_gemma":[0.9948558,0.00003251056,0.0001694811,0.0001188415,0.00007125433,0.0001413267,0.000001464857,0.00000724084,0.004602032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4666514,"threshold_uncertainty_score":0.9998364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2442444935030087,"score_gpt":0.3599918962834953,"score_spread":0.1157474027804866,"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."}}