{"id":"W2809628680","doi":"10.1038/d41586-018-05469-3","title":"Bias detectives: the researchers striving to make algorithms fair","year":2018,"lang":"en","type":"article","venue":"Nature","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":207,"is_retracted":false,"has_abstract":false,"ca_institutions":"World Federation of Science Journalists","funders":"","keywords":"Injustice; Social injustice; Computer science; Artificial intelligence; Psychology; Data science; Sociology; Social psychology; Law; Political science","routes":{"ca_aff":true,"ca_fund":false,"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":["metaresearch","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002847349,0.00007501451,0.00008360883,0.00007617712,0.001629751,0.0003367372,0.0005491977,0.0009598308,0.0000952254],"category_scores_gemma":[0.009060868,0.00005221544,0.00005348026,0.0009559738,0.0005847864,0.0001518307,0.0001009602,0.002394487,0.00007777592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001287398,"about_ca_system_score_gemma":0.0003559164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001376045,"about_ca_topic_score_gemma":0.01301323,"domain_scores_codex":[0.9982407,0.0002963527,0.0000801642,0.0001832367,0.0007737292,0.0004258098],"domain_scores_gemma":[0.9983844,0.0004903629,0.00003418712,0.0001892624,0.0006944381,0.0002073649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000549627,0.00007461509,0.003277898,0.00001153398,0.0001120456,0.00001694977,0.5036562,0.000004057743,0.0009337091,0.1543348,0.05756265,0.2799606],"study_design_scores_gemma":[0.0001579704,0.0001917245,0.01184959,0.00005677547,0.000009108281,3.84995e-7,0.08079531,0.00002304872,0.0009967134,0.05194981,0.853739,0.0002305883],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4152735,0.000694375,0.0001711953,0.3116482,0.001504377,0.000713828,0.00002015057,0.0001812475,0.2697932],"genre_scores_gemma":[0.9880365,0.0000478198,0.0005272621,0.005065545,0.001755174,0.000005702434,3.713853e-7,0.00001096097,0.004550648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7961763,"threshold_uncertainty_score":0.999907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1400523431765896,"score_gpt":0.4653754314622225,"score_spread":0.3253230882856329,"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."}}