{"id":"W4378419993","doi":"10.1007/978-3-031-33374-3_37","title":"Disentangled Representation with Causal Constraints for Counterfactual Fairness","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Counterfactual thinking; Computer science; Autoencoder; Representation (politics); Focus (optics); Benchmark (surveying); Artificial intelligence; Machine learning; Theoretical computer science; Artificial neural network; Social psychology; Psychology","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":[0.02666954,0.001166691,0.001964771,0.001562541,0.00173205,0.006431435,0.004475392,0.003626896,0.0158286],"category_scores_gemma":[0.1155346,0.001384036,0.002514595,0.002202118,0.006465775,0.01614094,0.006821968,0.008774798,0.001237414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002762861,"about_ca_system_score_gemma":0.003401151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002236802,"about_ca_topic_score_gemma":0.002067948,"domain_scores_codex":[0.9765187,0.01579116,0.001222804,0.002174431,0.00346481,0.0008280996],"domain_scores_gemma":[0.9070035,0.07217839,0.002414509,0.01469605,0.002937235,0.0007702664],"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":[0.00002889883,0.00001239506,0.00005925931,0.00003133748,0.00001870248,0.00001738743,0.00009949994,0.003927229,0.00006134692,0.9884963,0.0004516774,0.006795947],"study_design_scores_gemma":[0.000006953286,0.000003310384,0.00001931373,0.00001091462,0.000007557581,0.000008392232,0.000008474969,0.01690963,0.00006800293,0.9821594,0.0007930723,0.000004938171],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005271735,0.0002759606,0.9772525,0.001509331,0.0001416206,0.00007141798,0.0001418399,0.00009234439,0.01524315],"genre_scores_gemma":[0.5518578,0.0005355683,0.4308932,0.0009594999,0.0006190712,0.0007283281,0.0004709742,0.0003052658,0.01363036],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02666954,"threshold_uncertainty_score":0.1410437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06107585994059907,"score_gpt":0.3599134219523881,"score_spread":0.2988375620117891,"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."}}