{"id":"W2943447671","doi":"10.1007/978-3-030-18419-3_2","title":"Privacy and Ethical Challenges in Big Data","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Transparency (behavior); Computer science; Big data; Profiling (computer programming); Personally identifiable information; Data science; Internet privacy; Information privacy; Ethical issues; Population; Computer security; Data mining; Engineering ethics; Engineering","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","metaepi_narrow","open_science","research_integrity"],"consensus_categories":["open_science"],"category_scores_codex":[0.002742879,0.0005943818,0.0007176257,0.001237635,0.0001193865,0.0005394242,0.0806577,0.001199386,0.000005247887],"category_scores_gemma":[0.009604172,0.0005497608,0.00004464201,0.0006488075,0.001236093,0.0008137899,0.2881983,0.003239188,0.00004076268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002312905,"about_ca_system_score_gemma":0.0007236395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002905433,"about_ca_topic_score_gemma":0.0002282537,"domain_scores_codex":[0.993874,0.0000774284,0.0006032874,0.003464694,0.001122454,0.0008581887],"domain_scores_gemma":[0.9747562,0.001427669,0.0002577418,0.02332488,0.0001037365,0.0001298119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000003441278,0.00001649145,0.0001071265,0.00008752685,0.000006871297,0.0001125647,0.0002022801,0.0001170265,0.00002385692,0.01807236,0.0004704527,0.98078],"study_design_scores_gemma":[0.0002645569,0.00009605331,0.0004293975,0.0005805327,0.000003447373,0.00008529878,2.131511e-7,0.3740235,0.0001103548,0.6180293,0.005773085,0.0006043219],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00007484743,0.004584854,0.9298878,0.06084854,0.001976155,0.0004472226,0.00002413966,0.0003613306,0.00179508],"genre_scores_gemma":[0.05569817,0.004362453,0.9374018,0.001971218,0.0004156606,0.000007873333,0.00002515103,0.00005699754,0.00006068694],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9801757,"threshold_uncertainty_score":0.9996954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1257345948200686,"score_gpt":0.3067844270658165,"score_spread":0.1810498322457479,"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."}}