{"id":"W4366431100","doi":"10.2139/ssrn.4411227","title":"Detecting and Regulating Deepfakes in India: A Legal and Technological Conundrum","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Political science; Business","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":[0.01106166,0.0002915943,0.0004854019,0.002697312,0.003847884,0.007541029,0.004129502,0.003589081,0.005327726],"category_scores_gemma":[0.04078913,0.0004328336,0.000491478,0.00187829,0.008504276,0.004334283,0.003401895,0.004139624,0.0007332315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004794141,"about_ca_system_score_gemma":0.01921139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05508959,"about_ca_topic_score_gemma":0.09574361,"domain_scores_codex":[0.990139,0.003287017,0.0005616965,0.0008865801,0.002880548,0.00224516],"domain_scores_gemma":[0.917689,0.05667859,0.008987393,0.005264801,0.008671067,0.002709184],"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":[0.0002055477,0.0003733313,0.2033059,0.000830736,0.0001598509,0.001519813,0.01825473,0.009910098,0.005187606,0.4885534,0.03183235,0.2398666],"study_design_scores_gemma":[0.000105134,0.0003849218,0.2121104,0.001732006,0.000228642,0.001410977,0.07001198,0.02857069,0.01925636,0.5036778,0.1620953,0.0004158662],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6117055,0.002530517,0.06246512,0.09778127,0.0005956952,0.000296521,0.0007343934,0.0007959724,0.223095],"genre_scores_gemma":[0.9849974,0.000375345,0.005993007,0.00222919,0.00006309557,0.00004704905,0.000052474,0.00003845089,0.006203996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05508959,"threshold_uncertainty_score":0.1095379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01830260226327363,"score_gpt":0.3206939435302252,"score_spread":0.3023913412669516,"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."}}