{"id":"W3210032839","doi":"","title":"Developing Privacy Best Practices for Direct-to-Public Legal Apps: Observations and Lessons Learned","year":2020,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; McGill University; University of Ottawa","keywords":"Mandate; Best practice; Context (archaeology); Privacy law; Legal advice; Internet privacy; Information privacy; Privacy by Design; Process (computing); Work (physics); Privacy policy; Public relations; Business; Political science; Computer science; Law; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.03855594,0.0008003631,0.000629698,0.00309249,0.01266959,0.01381255,0.005586342,0.004084051,0.003341913],"category_scores_gemma":[0.1095565,0.0009270785,0.0007078727,0.00500186,0.01035327,0.01400027,0.005365275,0.008299016,0.0007325107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0566645,"about_ca_system_score_gemma":0.1270903,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.7795006,"about_ca_topic_score_gemma":0.8398107,"domain_scores_codex":[0.9524682,0.01587952,0.001959132,0.002557389,0.02201377,0.005122054],"domain_scores_gemma":[0.8540698,0.07374088,0.004315199,0.007806542,0.05339761,0.006669942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001907627,0.00147219,0.043408,0.001931269,0.00005757527,0.003843333,0.3766306,0.001774729,0.0031869,0.04862788,0.07436764,0.4445091],"study_design_scores_gemma":[0.0001071324,0.0003378812,0.05313753,0.003474908,0.0001282293,0.001228036,0.5168712,0.007252737,0.004904993,0.01117409,0.4010594,0.0003238722],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5950349,0.0101424,0.03826158,0.160523,0.0006067557,0.00519858,0.001443069,0.001858766,0.186931],"genre_scores_gemma":[0.9150246,0.009512069,0.04344264,0.008963155,0.0001040628,0.0006808885,0.0006784413,0.0003866898,0.02120749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7795006,"threshold_uncertainty_score":0.4435959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3226444354339228,"score_gpt":0.426523854878491,"score_spread":0.1038794194445681,"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."}}