{"id":"W4308963385","doi":"10.1007/s43681-022-00234-9","title":"AI and housing discrimination: the case of mortgage applications","year":2022,"lang":"en","type":"article","venue":"AI and Ethics","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Denial; Context (archaeology); White (mutation); Actuarial science; Racism; Mortgage underwriting; Ethnic group; Shared appreciation mortgage; Business; Computer science; Psychology; Mortgage insurance; Political science; Law; History","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":[],"consensus_categories":[],"category_scores_codex":[0.001036381,0.00006105802,0.0001328365,0.00006122609,0.0006655022,0.00005429485,0.00007285862,0.0000520986,0.00006816163],"category_scores_gemma":[0.00004301156,0.00006262688,0.00002698231,0.00009876371,0.0001274692,0.00009459948,0.0001269478,0.0004009134,0.000003261529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002294381,"about_ca_system_score_gemma":0.000020947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003858859,"about_ca_topic_score_gemma":0.0001778252,"domain_scores_codex":[0.9994546,0.00002533712,0.0002375267,0.0001678098,0.00001538036,0.00009933415],"domain_scores_gemma":[0.9995077,0.0001445704,0.0001231027,0.0001779554,0.00001698441,0.00002965534],"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":[0.0000107218,0.00005717661,0.0158166,0.0001141372,0.00003280595,0.00001756435,0.009210781,0.0002929147,0.000005866076,0.9429662,0.0008717942,0.03060348],"study_design_scores_gemma":[0.0009546262,0.0001607564,0.007561507,0.00001719017,0.00006894078,0.0006390754,0.009332181,0.06066221,0.00003611419,0.5246906,0.3952067,0.0006701238],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8785722,0.003289308,0.02138033,0.05307218,0.0004603017,0.0005447244,0.000325194,0.00005090595,0.04230485],"genre_scores_gemma":[0.9969434,0.0006502737,0.0001279185,0.002058295,0.00004414015,0.00003354541,0.000007770951,0.00001083163,0.0001238987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4182756,"threshold_uncertainty_score":0.5118572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06006881903489823,"score_gpt":0.2807860317110874,"score_spread":0.2207172126761892,"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."}}