{"id":"W4380901836","doi":"10.2139/ssrn.4470725","title":"The EU AI Liability Directive: shifting the burden from proof to evidence","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute on Governance","funders":"","keywords":"Directive; Liability; Transparency (behavior); Plaintiff; Harm; Closing (real estate); Business; Law and economics; Actuarial science; Burden of proof; Political science; Law; Economics; Computer science; Accounting","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":["metaresearch","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.02048574,0.000114426,0.0001380284,0.00003441285,0.005075039,0.0007820407,0.0009972671,0.0001138366,0.00002206767],"category_scores_gemma":[0.0162249,0.00006720483,0.0001328732,0.0007381242,0.000349387,0.0004579067,0.0001248426,0.0027172,0.00008296734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001101279,"about_ca_system_score_gemma":0.004720863,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01614874,"about_ca_topic_score_gemma":0.05717318,"domain_scores_codex":[0.9954658,0.0009735337,0.0002500715,0.0002125183,0.0008331417,0.002264907],"domain_scores_gemma":[0.9953348,0.003733763,0.0001339024,0.000234445,0.0003953132,0.0001677935],"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":[0.000100833,0.00003453892,0.003028846,0.000004096435,0.0002731569,0.000005029,0.1699382,0.0002228797,0.0003473808,0.6635803,0.007256772,0.155208],"study_design_scores_gemma":[0.0000771436,0.0001035963,0.003015925,0.00005275601,0.00002220929,0.000001317672,0.0565763,0.0000549139,0.00003842274,0.85935,0.08057411,0.0001332971],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.291057,0.002121374,0.0002017937,0.699985,0.0006592853,0.0004205683,0.00000277332,0.00009107789,0.005461098],"genre_scores_gemma":[0.9880311,0.00468824,0.000009437959,0.001181324,0.002433775,0.00001415235,3.37425e-7,0.00001468384,0.003626985],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6988037,"threshold_uncertainty_score":0.9995835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03975016120781771,"score_gpt":0.391750158126928,"score_spread":0.3519999969191103,"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."}}