{"id":"W1574538925","doi":"10.29173/irie91","title":"Ambient Intelligence and Problems with Inferring Desires from Behaviour","year":2007,"lang":"en","type":"article","venue":"The International Review of Information Ethics","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ambient intelligence; Presupposition; Adaptation (eye); Focus (optics); Natural (archaeology); Human intelligence; Relation (database); Psychology; Computer science; Reciprocal; Social psychology; Cognitive psychology; Epistemology; Human–computer interaction; Artificial intelligence; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004269326,0.00005922402,0.00008343127,0.00005402134,0.0002141998,0.00006999861,0.0004348199,0.00006021119,0.00005797966],"category_scores_gemma":[0.00274509,0.00004008082,0.00002255084,0.0001335585,0.000205753,0.0009497814,0.0001291725,0.0002945107,0.00001338847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006534941,"about_ca_system_score_gemma":0.0001294213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004243075,"about_ca_topic_score_gemma":0.001764882,"domain_scores_codex":[0.9987227,0.00007242371,0.0003715494,0.00005782638,0.0006836248,0.00009192208],"domain_scores_gemma":[0.998668,0.0003933596,0.0002986093,0.0001387337,0.0004627158,0.00003863359],"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":[0.0001019278,0.00009510366,0.01367038,0.002147064,0.0001054551,0.000001299544,0.1098557,0.00008930072,0.00006228909,0.6095459,0.0009266838,0.2633989],"study_design_scores_gemma":[0.0008053286,0.0003464828,0.1002904,0.03088481,0.0002241706,0.00003306285,0.03206243,0.001397547,0.007888756,0.1552024,0.669872,0.0009925873],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1618486,0.01597335,0.6886973,0.07762109,0.001652427,0.003228772,0.0001766412,0.0002153263,0.05058644],"genre_scores_gemma":[0.9555914,0.04000452,0.001745509,0.00249103,0.00007902376,0.00001495678,0.00005336312,0.000002935019,0.00001730965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7937428,"threshold_uncertainty_score":0.6414288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08350021407144036,"score_gpt":0.3710282465595428,"score_spread":0.2875280324881024,"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."}}