{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01154018,0.001193086,0.001344794,0.001857346,0.002758173,0.008338259,0.00258031,0.007314534,0.02384125],"category_scores_gemma":[0.1049886,0.0005556287,0.001457942,0.001213818,0.008097275,0.01335319,0.002177168,0.006624511,0.01155853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003370379,"about_ca_system_score_gemma":0.004088474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00376258,"about_ca_topic_score_gemma":0.002803561,"domain_scores_codex":[0.9899117,0.004775231,0.0007078408,0.001890954,0.001995168,0.0007191187],"domain_scores_gemma":[0.9680765,0.02407319,0.001583385,0.002126486,0.003313828,0.0008266946],"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":[0.0005203593,0.0001263003,0.005514223,0.0005565035,0.0001547127,0.0008227169,0.001824613,0.007711736,0.0006559286,0.6764702,0.1498773,0.1557655],"study_design_scores_gemma":[0.0001136623,0.0001227642,0.000832302,0.0008263055,0.00009105363,0.001281221,0.001366741,0.02723105,0.001405379,0.7723055,0.1942869,0.0001371672],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.02004097,0.008505258,0.3899415,0.4106089,0.007088389,0.0005299503,0.002197578,0.001997277,0.1590902],"genre_scores_gemma":[0.5752572,0.008234901,0.2865522,0.05432235,0.004674457,0.000990242,0.002919638,0.001149684,0.06589937],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.02384125,"threshold_uncertainty_score":0.07975698,"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."}}