{"id":"W4288058277","doi":"10.1145/3514094.3534194","title":"Strategic Best Response Fairness in Fair Machine Learning","year":2022,"lang":"en","type":"article","venue":"","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Computer science; Artificial intelligence; Machine learning; Fair share; Context (archaeology); Algorithm; Economics","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004306529,0.00006571908,0.0001112953,0.00009289092,0.001397621,0.0001412995,0.0002635039,0.00006592446,0.002097511],"category_scores_gemma":[0.0006568749,0.00006902375,0.00004217915,0.0004672328,0.0001380399,0.0001936637,0.000105114,0.000685567,0.00002974096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000198486,"about_ca_system_score_gemma":0.0004734497,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03262605,"about_ca_topic_score_gemma":0.0285515,"domain_scores_codex":[0.9975892,0.001339047,0.0001394717,0.0001511333,0.0004752704,0.0003059132],"domain_scores_gemma":[0.9992986,0.0004228311,0.00004611908,0.00007944393,0.00005639121,0.00009666609],"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":[0.0003448048,0.0002804901,0.01813866,0.000006150382,0.00001124086,0.0001024541,0.08027314,0.001441172,0.0005297161,0.896957,0.0003415308,0.001573594],"study_design_scores_gemma":[0.0009113836,0.0006445536,0.005447512,0.00001574912,0.000007814924,0.000002030154,0.660959,0.0008316898,0.00003253428,0.1118552,0.2187772,0.0005153547],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6976157,0.00006373217,0.000007700301,0.01786822,0.00010546,0.0001632032,0.000004231954,0.00006976992,0.284102],"genre_scores_gemma":[0.9775875,0.00003832265,0.00003851104,0.0004906534,0.00005262327,0.0000223247,0.000003161448,0.000008550928,0.02175835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7851019,"threshold_uncertainty_score":0.9999024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08861203384532781,"score_gpt":0.379534109511438,"score_spread":0.2909220756661102,"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."}}