{"id":"W3037211332","doi":"10.1609/aaai.v34i09.7116","title":"Explaining Image Classifiers Generating Exemplars and Counter-Exemplars from Latent Representations","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Horizon 2020 Framework Programme","keywords":"Artificial intelligence; Computer science; Image (mathematics); Pattern recognition (psychology); Machine learning; Set (abstract data type); Class (philosophy); Counterfactual thinking; Decision tree; Autoencoder; Feature (linguistics); Coherence (philosophical gambling strategy); Fidelity; Probabilistic latent semantic analysis; Mathematics; Artificial neural network; Linguistics; 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":[0.0005104882,0.0006336271,0.0003891503,0.0004127588,0.0002084112,0.0007420021,0.001076696,0.0009594831,0.002356318],"category_scores_gemma":[0.002450656,0.0002737707,0.000800722,0.000268508,0.0006130268,0.001212796,0.0007973649,0.001057813,0.0003881588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005037894,"about_ca_system_score_gemma":0.0003754177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001286005,"about_ca_topic_score_gemma":0.002132265,"domain_scores_codex":[0.9997405,0.00006717164,0.0000142476,0.00008174487,0.00006379899,0.00003251091],"domain_scores_gemma":[0.9991513,0.0004294387,0.0001056379,0.0001968112,0.00008688246,0.00002995321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000232286,0.0001260354,0.004709213,0.0002744766,0.0001718712,0.0008039743,0.0005006961,0.6446383,0.03178965,0.07732011,0.005804763,0.2336286],"study_design_scores_gemma":[0.000008988419,0.00002908154,0.0002901548,0.00001158969,0.00001413853,0.00006942298,0.00002136885,0.9746558,0.004765715,0.01895735,0.001167816,0.000008497947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05170059,0.0002287645,0.9448766,0.0004584311,0.00003986462,0.00006656296,0.000233397,0.0008809985,0.001514851],"genre_scores_gemma":[0.7366478,0.0002934504,0.2590138,0.0002081453,0.00004814858,0.0001160862,0.0008217252,0.0001158438,0.002735016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002356318,"threshold_uncertainty_score":0.007882655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1078777002411825,"score_gpt":0.3035250152619264,"score_spread":0.1956473150207439,"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."}}