{"id":"W3047187553","doi":"10.36645/mtlr.27.2.how","title":"How Can I Tell if My Algorithm Was Reasonable?","year":2021,"lang":"en","type":"article","venue":"Michigan Technology Law Review","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"University of Haifa","keywords":"Tort; Damages; Computer science; Strengths and weaknesses; Compensation (psychology); Artificial intelligence; Order (exchange); Liability; Risk analysis (engineering); Algorithm; Law and economics; Law; Business; Psychology; Economics; Political science; Social psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.0009784873,0.0002030435,0.0005857638,0.00005506014,0.0009125657,0.0001986676,0.0006099614,0.0006082117,0.0001676674],"category_scores_gemma":[0.00129889,0.0001983364,0.0001739792,0.001091182,0.001226525,0.0002101114,0.0001700393,0.0007534162,0.00008038525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007740191,"about_ca_system_score_gemma":0.000557892,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001354775,"about_ca_topic_score_gemma":0.01881141,"domain_scores_codex":[0.9980691,0.0002215352,0.000267465,0.0004296932,0.0003684486,0.000643758],"domain_scores_gemma":[0.9984775,0.00009194529,0.0001600247,0.0005551586,0.0005295348,0.0001858882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[6.390665e-7,0.00006747522,0.00005503622,0.0002755631,0.00006725408,0.0001285388,0.00084803,1.452144e-8,0.0001864542,0.9198972,0.005284334,0.07318944],"study_design_scores_gemma":[0.00009754288,0.00002961741,0.000006885343,0.0008079768,0.00007477894,0.00001704103,0.001577004,5.164532e-7,0.001289977,0.05094698,0.9449123,0.0002394291],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.004366715,0.1952809,0.00007358711,0.7016423,0.0005711748,0.0006681829,0.00005909233,0.0007207209,0.09661727],"genre_scores_gemma":[0.08059346,0.8222041,0.01422475,0.04302677,0.0007878799,0.0001181979,0.0001020948,0.0001075176,0.03883521],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.9396279,"threshold_uncertainty_score":0.9990927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02406130042368366,"score_gpt":0.3214266298542768,"score_spread":0.2973653294305932,"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."}}