{"id":"W4392864805","doi":"10.1145/3624730","title":"Who Determines What Is Relevant? Humans or AI? Why Not Both?","year":2024,"lang":"en","type":"article","venue":"Communications of the ACM","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Australian Research Council","keywords":"Relevance (law); Computer science; Artificial intelligence; Data science; Political science","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":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0004161937,0.0001298257,0.0001607975,0.0001197524,0.0004202421,0.000645511,0.04197902,0.00006007994,0.00003847187],"category_scores_gemma":[0.00186845,0.00009022733,0.0001358333,0.0008344273,0.0003232777,0.001627699,0.02053994,0.0002647008,0.0001723194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004822913,"about_ca_system_score_gemma":0.0001168953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001256976,"about_ca_topic_score_gemma":0.0003364506,"domain_scores_codex":[0.9987268,0.0001722099,0.0003717841,0.000277161,0.000236778,0.000215223],"domain_scores_gemma":[0.9663938,0.0008823304,0.00009361803,0.03242555,0.0001609291,0.00004381669],"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":[0.00002164944,0.0003056117,0.0003210536,0.0002030923,0.0001426266,0.00001045955,0.02812343,0.0001454037,0.01528454,0.4623253,0.347719,0.1453979],"study_design_scores_gemma":[0.00007113638,0.00009423602,0.0003107107,0.001007307,0.00004407992,0.0000253007,0.001029264,0.08298206,0.1201895,0.3907638,0.4031208,0.0003617055],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01465535,0.01376154,0.02230272,0.9428486,0.00151773,0.0007200218,0.00001834142,0.0005313553,0.003644297],"genre_scores_gemma":[0.846299,0.008057443,0.128202,0.0110822,0.00008949173,0.0001071542,0.000003445637,0.0000443666,0.006114861],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.9317665,"threshold_uncertainty_score":0.9873818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09904252942343997,"score_gpt":0.3619512889113444,"score_spread":0.2629087594879044,"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."}}