{"id":"W4226246254","doi":"10.1609/aaai.v36i11.21438","title":"Interpretable Low-Resource Legal Decision Making","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Government of Canada","keywords":"Interpretability; Deep learning; Artificial intelligence; Computer science; Machine learning; Task (project management); Resource (disambiguation); Confusion; Trademark; Data science; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003432694,0.0006783307,0.0009307736,0.000934385,0.0008892026,0.004176566,0.002216436,0.002092972,0.01189877],"category_scores_gemma":[0.02555101,0.0005917738,0.000772225,0.0007034739,0.002206042,0.005631619,0.00334492,0.0037372,0.001682181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002374893,"about_ca_system_score_gemma":0.003395413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004499006,"about_ca_topic_score_gemma":0.007881079,"domain_scores_codex":[0.9972039,0.001061154,0.0001689438,0.0005478447,0.000778915,0.000239277],"domain_scores_gemma":[0.988813,0.0074813,0.0009919669,0.001410356,0.0009452417,0.0003581497],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003368936,0.0003382897,0.003520452,0.000358661,0.0000669329,0.0006418273,0.0007579369,0.3952908,0.003875091,0.3230169,0.01644672,0.2553496],"study_design_scores_gemma":[0.00001937168,0.00002474549,0.0003383277,0.00005715709,0.00001158657,0.00005973183,0.00009159295,0.7491187,0.002096826,0.2434413,0.004723046,0.00001755953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0480879,0.0004385314,0.9186525,0.00599242,0.0001479409,0.0001370493,0.0008797624,0.001618465,0.02404555],"genre_scores_gemma":[0.8052146,0.0004212995,0.1827981,0.0006984072,0.0001512515,0.0001376034,0.001178896,0.0001759881,0.009223663],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01189877,"threshold_uncertainty_score":0.03980541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06607011868209936,"score_gpt":0.3496311093982654,"score_spread":0.283560990716166,"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."}}