{"id":"W4404581799","doi":"10.1088/1748-9326/ad95a2","title":"Does artificial intelligence bias perceptions of environmental challenges?","year":2024,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Chatbot; Environmental justice; Perception; Computer science; Data science; Artificial intelligence; Psychology; Political science","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001608372,0.0002791023,0.0003834917,0.0006101413,0.0001845751,0.00009860178,0.0005471191,0.0001425122,0.00773936],"category_scores_gemma":[0.00005205738,0.0002624913,0.0002492361,0.0001476393,0.001076901,0.0004367058,0.0003683453,0.0005921967,0.007647343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008826103,"about_ca_system_score_gemma":0.000009902421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004734363,"about_ca_topic_score_gemma":0.00001119275,"domain_scores_codex":[0.9969806,0.0001177495,0.0008951598,0.001035755,0.0002440738,0.0007266403],"domain_scores_gemma":[0.9986855,0.000274231,0.0001413024,0.0006916643,0.000001034443,0.0002062567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001494068,0.002933805,0.09390568,0.0005017529,0.0009488108,0.0002768712,0.009891122,0.00236848,0.2786706,0.4476077,0.002592081,0.1601538],"study_design_scores_gemma":[0.0007260519,0.00108457,0.5195944,0.0003205682,0.00006152588,0.0000579874,0.01559857,0.01200381,0.0516329,0.2287398,0.1668817,0.003298047],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819648,0.005360231,0.001377254,0.006255194,0.0006014648,0.0003953903,0.0007150921,0.00005397921,0.003276539],"genre_scores_gemma":[0.9900446,0.008013975,0.0004425518,0.0001563106,0.000277075,0.00009332233,0.00005423935,0.00008159981,0.0008363536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4256887,"threshold_uncertainty_score":0.9999827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1024531147184768,"score_gpt":0.2850273751987018,"score_spread":0.182574260480225,"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."}}