{"id":"W4415722927","doi":"10.1108/jices-01-2025-0002","title":"Fairness in social machines: a systematic review","year":2025,"lang":"en","type":"article","venue":"Journal of Information Communication and Ethics in Society","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Normative; Categorization; Harm; Similarity (geometry); Context (archaeology); Frame (networking); Phenomenon","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":[0.01589432,0.00006390061,0.0003264691,0.0001120533,0.000575683,0.0001722936,0.0003815244,0.000296504,0.000008284977],"category_scores_gemma":[0.005835056,0.00005741519,0.0001132825,0.0005883509,0.0002640035,0.001415049,0.000067497,0.001474472,0.000001147355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002203879,"about_ca_system_score_gemma":0.0006270606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003132119,"about_ca_topic_score_gemma":0.0006675196,"domain_scores_codex":[0.9975843,0.0009167609,0.0009493417,0.00003049008,0.0004052563,0.0001138102],"domain_scores_gemma":[0.9972068,0.001160292,0.0006222859,0.000123927,0.0008500186,0.00003667394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.000007254733,0.0000459342,0.0006152723,0.03539034,0.0000325695,1.789912e-7,0.3859479,0.00001050321,7.019834e-7,0.5732549,0.004037637,0.0006567832],"study_design_scores_gemma":[0.002643066,0.00005027476,0.007379081,0.1865571,0.000206279,0.000005483093,0.4118104,0.001341826,0.00000312607,0.359404,0.03010695,0.0004924816],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0111551,0.04551096,0.003178136,0.7316977,0.0005040838,0.001969517,0.000008705792,0.00005163127,0.2059242],"genre_scores_gemma":[0.7449127,0.2249441,0.001167871,0.02878019,0.00003628019,0.00001496228,0.000005376214,0.000003591059,0.0001349055],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.7337577,"threshold_uncertainty_score":0.6985528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05288696643618308,"score_gpt":0.4402067766339929,"score_spread":0.3873198101978098,"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."}}