{"id":"W4390151798","doi":"10.48550/arxiv.2312.16191","title":"SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; École de Technologie Supérieure","funders":"","keywords":"Interpretability; Computer science; Audit; Pairwise comparison; Key (lock); Artificial intelligence; Data science; Machine learning; Computer security; Business","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02456078,0.0009728659,0.001344825,0.001631534,0.002704519,0.00842302,0.00334089,0.003148347,0.006560612],"category_scores_gemma":[0.07906351,0.0009320537,0.00162629,0.001612156,0.02035953,0.01789553,0.01271615,0.007313626,0.001153107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002531925,"about_ca_system_score_gemma":0.003600057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009521047,"about_ca_topic_score_gemma":0.0006133985,"domain_scores_codex":[0.9704896,0.01824941,0.001321708,0.003504809,0.005100205,0.00133428],"domain_scores_gemma":[0.9002329,0.05279643,0.004699476,0.0369628,0.003718526,0.001589827],"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":[0.00007737501,0.00002656281,0.0006595515,0.0001126894,0.00003497577,0.00008053236,0.001175417,0.004552902,0.0008555404,0.972661,0.00130996,0.01845335],"study_design_scores_gemma":[0.00001775307,0.00003525724,0.0001783501,0.00006849411,0.00002487313,0.00008842925,0.0001635331,0.01971585,0.001657257,0.9696618,0.008364663,0.00002370735],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.02872789,0.0008703589,0.9342707,0.016876,0.0002767065,0.0001193359,0.0001465978,0.001124365,0.01758801],"genre_scores_gemma":[0.8350862,0.0009111537,0.1538227,0.002971123,0.0003484849,0.0003291839,0.0001722158,0.0005865261,0.005772351],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.02456078,"threshold_uncertainty_score":0.1298914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.153186853381888,"score_gpt":0.2253173262170066,"score_spread":0.07213047283511853,"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."}}