{"id":"W3176616498","doi":"10.29173/irie423","title":"Tangible and Intangible Impact of AI Usage: AI for Information Accessibility","year":2021,"lang":"en","type":"article","venue":"The International Review of Information Ethics","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Intellect; Equity (law); Welfare; Human welfare; Ethical issues; Environmental ethics; Political science; Sociology; Business; Engineering ethics; Economic growth; Public relations; Economics; Engineering; Law; Epistemology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004338988,0.0004192403,0.0004262058,0.003582594,0.001808616,0.00734339,0.0006492878,0.00252232,0.01014068],"category_scores_gemma":[0.01816751,0.0001619903,0.0005919834,0.003800862,0.01091488,0.009256768,0.004793859,0.003299851,0.0007173585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002858882,"about_ca_system_score_gemma":0.002654318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002355343,"about_ca_topic_score_gemma":0.003336871,"domain_scores_codex":[0.9923508,0.004959813,0.0003655655,0.0003701414,0.001635709,0.0003180438],"domain_scores_gemma":[0.969175,0.02514656,0.002109178,0.0007449646,0.002342928,0.0004813818],"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":[0.00004710312,0.00006880485,0.005135118,0.002355465,0.00007542866,0.0003255244,0.00598121,0.0005842539,0.0003894233,0.8595728,0.008176448,0.1172884],"study_design_scores_gemma":[0.00001453681,0.0001337917,0.02902136,0.0116888,0.0001888084,0.001185287,0.01356226,0.001620459,0.0009516029,0.567804,0.3737485,0.00008062766],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03684141,0.2791173,0.02730683,0.1579214,0.002794164,0.0001066787,0.0003294287,0.0000727577,0.4955101],"genre_scores_gemma":[0.8292207,0.1367821,0.007922709,0.00795654,0.002335347,0.0001842921,0.0001405755,0.00004899447,0.01540873],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01014068,"threshold_uncertainty_score":0.03392398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06440760821050519,"score_gpt":0.4804021699332583,"score_spread":0.4159945617227532,"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."}}