{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/shap/papers/5","list_of":"/method/shap","method":"SHAP","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":5,"pages_in_order":6,"rows_per_page":100,"rows":[401,500],"of":550,"counts":{"archive_papers_tagged":550,"with_a_code_link":175,"where_syntology_ran_a_sample":26,"not_listed_spam_title":0,"listed":550,"listed_where_code_ran":26,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":19,"every_run_a_failure_of_syntologys_instrument":7,"listed_with_a_run_with_no_instrument_failure":19,"listed_every_run_a_failure_of_syntologys_instrument":7,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/shap","prev":"/method/shap/papers/4","next":"/method/shap/papers/6","papers":[{"paper":null,"slug":"philaex-explaining-the-failure-and-success-of","title":"PhilaeX: Explaining the Failure and Success of AI Models in Malware Detection","date":"2022-07-02","arxiv_id":"2207.00740","n_code_links":0,"syntology":null},{"paper":null,"slug":"analyzing-the-effects-of-classifier-1","title":"Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions","date":"2022-06-24","arxiv_id":"2206.12481","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-in-sports-a-case-study-on","title":"Machine Learning in Sports: A Case Study on Using Explainable Models for Predicting Outcomes of Volleyball Matches","date":"2022-06-18","arxiv_id":"2206.09258","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-bias-variance-characteristics-of-lime","title":"On the Bias-Variance Characteristics of LIME and SHAP in High Sparsity Movie Recommendation Explanation Tasks","date":"2022-06-09","arxiv_id":"2206.04784","n_code_links":0,"syntology":null},{"paper":"/paper/balanced-background-and-explanation-data-are","slug":"balanced-background-and-explanation-data-are","title":"Balanced background and explanation data are needed in explaining deep learning models with SHAP: An empirical study on clinical decision making","date":"2022-06-08","arxiv_id":"2206.04050","n_code_links":1,"syntology":null},{"paper":"/paper/do-we-need-another-explainable-ai-method","slug":"do-we-need-another-explainable-ai-method","title":"Do We Need Another Explainable AI Method? Toward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional Benchmark","date":"2022-06-08","arxiv_id":"2207.14160","n_code_links":1,"syntology":null},{"paper":"/paper/which-explanation-should-i-choose-a-function","slug":"which-explanation-should-i-choose-a-function","title":"Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations","date":"2022-06-02","arxiv_id":"2206.01254","n_code_links":1,"syntology":null},{"paper":null,"slug":"attribution-based-explanations-that-provide","title":"Attribution-based Explanations that Provide Recourse Cannot be Robust","date":"2022-05-31","arxiv_id":"2205.15834","n_code_links":0,"syntology":null},{"paper":"/paper/fooling-shap-with-stealthily-biased-sampling","slug":"fooling-shap-with-stealthily-biased-sampling","title":"Fool SHAP with Stealthily Biased Sampling","date":"2022-05-30","arxiv_id":"2205.15419","n_code_links":1,"syntology":null},{"paper":"/paper/unfooling-perturbation-based-post-hoc","slug":"unfooling-perturbation-based-post-hoc","title":"Unfooling Perturbation-Based Post Hoc Explainers","date":"2022-05-29","arxiv_id":"2205.14772","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["craymichael/unfooling"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/deletion-and-insertion-tests-in-regression","slug":"deletion-and-insertion-tests-in-regression","title":"Deletion and Insertion Tests in Regression Models","date":"2022-05-25","arxiv_id":"2205.12423","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/the-solvability-of-interpretability","slug":"the-solvability-of-interpretability","title":"The Solvability of Interpretability Evaluation Metrics","date":"2022-05-18","arxiv_id":"2205.08696","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-generalisability-of-machine-learning-based","title":"On Generalisability of Machine Learning-based Network Intrusion Detection Systems","date":"2022-05-09","arxiv_id":"2205.04112","n_code_links":0,"syntology":null},{"paper":null,"slug":"explainable-multi-class-anomaly-detection-on","title":"Explainable multi-class anomaly detection on functional data","date":"2022-05-03","arxiv_id":"2205.02935","n_code_links":0,"syntology":null},{"paper":"/paper/explainable-artificial-intelligence-for-6","slug":"explainable-artificial-intelligence-for-6","title":"Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamics","date":"2022-04-30","arxiv_id":"2205.00202","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["maikejulie/DNN4Cli"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"identifying-critical-lms-features-for","title":"Identifying Critical LMS Features for Predicting At-risk Students","date":"2022-04-27","arxiv_id":"2204.13700","n_code_links":0,"syntology":null},{"paper":"/paper/an-empirical-study-of-the-effect-of","slug":"an-empirical-study-of-the-effect-of","title":"An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning models","date":"2022-04-24","arxiv_id":"2204.11351","n_code_links":1,"syntology":null},{"paper":null,"slug":"feature-visualization-for-convolutional","title":"Feature visualization for convolutional neural network models trained on neuroimaging data","date":"2022-03-24","arxiv_id":"2203.13120","n_code_links":0,"syntology":null},{"paper":null,"slug":"explainable-machine-learning-for-predicting","title":"Explainable Machine Learning for Predicting Homicide Clearance in the United States","date":"2022-03-09","arxiv_id":"2203.04768","n_code_links":0,"syntology":null},{"paper":null,"slug":"robustness-and-usefulness-in-ai-explanation","title":"Robustness and Usefulness in AI Explanation Methods","date":"2022-03-07","arxiv_id":"2203.03729","n_code_links":0,"syntology":null},{"paper":null,"slug":"approximating-a-deep-reinforcement-learning","title":"Approximating a deep reinforcement learning docking agent using linear model trees","date":"2022-03-01","arxiv_id":"2203.00369","n_code_links":0,"syntology":null},{"paper":"/paper/explainable-deepfake-and-spoofing-detection","slug":"explainable-deepfake-and-spoofing-detection","title":"Explainable deepfake and spoofing detection: an attack analysis using SHapley Additive exPlanations","date":"2022-02-28","arxiv_id":"2202.13693","n_code_links":1,"syntology":null},{"paper":null,"slug":"double-barreled-question-detection-at","title":"Double-Barreled Question Detection at Momentive","date":"2022-02-12","arxiv_id":"2203.03545","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-aided-holistic-handover","title":"Machine Learning Aided Holistic Handover Optimization for Emerging Networks","date":"2022-02-06","arxiv_id":"2202.02851","n_code_links":0,"syntology":null},{"paper":"/paper/hierarchical-shrinkage-improving-the-accuracy","slug":"hierarchical-shrinkage-improving-the-accuracy","title":"Hierarchical Shrinkage: improving the accuracy and interpretability of tree-based methods","date":"2022-02-02","arxiv_id":"2202.00858","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["csinva/imodels","yu-group/imodels-experiments"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/monitoring-model-deterioration-with","slug":"monitoring-model-deterioration-with","title":"Monitoring Model Deterioration with Explainable Uncertainty Estimation via Non-parametric Bootstrap","date":"2022-01-27","arxiv_id":"2201.11676","n_code_links":2,"syntology":null},{"paper":null,"slug":"learning-from-disagreement-a-model-comparison","title":"Learning-From-Disagreement: A Model Comparison and Visual Analytics Framework","date":"2022-01-19","arxiv_id":"2201.07849","n_code_links":0,"syntology":null},{"paper":null,"slug":"mcxai-local-model-agnostic-explanation-as-two-1","title":"McXai: Local model-agnostic explanation as two games","date":"2022-01-04","arxiv_id":"2201.01044","n_code_links":0,"syntology":null},{"paper":null,"slug":"explainable-artificial-intelligence-for-4","title":"Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?","date":"2021-12-25","arxiv_id":"2112.13210","n_code_links":0,"syntology":null},{"paper":null,"slug":"explainable-signature-based-machine-learning","title":"Explainable Signature-based Machine Learning Approach for Identification of Faults in Grid-Connected Photovoltaic Systems","date":"2021-12-25","arxiv_id":"2112.14842","n_code_links":0,"syntology":null},{"paper":null,"slug":"more-than-words-towards-better-quality","title":"More Than Words: Towards Better Quality Interpretations of Text Classifiers","date":"2021-12-23","arxiv_id":"2112.12444","n_code_links":0,"syntology":null},{"paper":null,"slug":"explanation-of-machine-learning-models-using","title":"Explanation of Machine Learning Models Using Shapley Additive Explanation and Application for Real Data in Hospital","date":"2021-12-21","arxiv_id":"2112.11071","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-prediction-based-approach-for-online","title":"A prediction-based approach for online dynamic patient scheduling: a case study in radiotherapy treatment","date":"2021-12-16","arxiv_id":"2112.08549","n_code_links":0,"syntology":null},{"paper":null,"slug":"utilizing-xai-technique-to-improve","title":"Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)","date":"2021-12-14","arxiv_id":"2112.08442","n_code_links":0,"syntology":null},{"paper":null,"slug":"explainable-predictions-of-different-machine","title":"Explainable predictions of different machine learning algorithms used to predict Early Stage diabetes","date":"2021-11-18","arxiv_id":"2111.09939","n_code_links":0,"syntology":null},{"paper":"/paper/scrutinizing-xai-using-linear-ground-truth","slug":"scrutinizing-xai-using-linear-ground-truth","title":"Scrutinizing XAI using linear ground-truth data with suppressor variables","date":"2021-11-14","arxiv_id":"2111.07473","n_code_links":1,"syntology":{"ran":8,"of":8,"n_ran_checked":8,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["braindatalab/scrutinizing-xai"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/interpreting-bert-architecture-predictions","slug":"interpreting-bert-architecture-predictions","title":"Interpreting BERT architecture predictions for peptide presentation by MHC class I proteins","date":"2021-11-13","arxiv_id":"2111.07137","n_code_links":1,"syntology":null},{"paper":null,"slug":"what-goes-on-inside-rumour-and-non-rumour","title":"What goes on inside rumour and non-rumour tweets and their reactions: A Psycholinguistic Analyses","date":"2021-11-09","arxiv_id":"2112.03003","n_code_links":0,"syntology":null},{"paper":null,"slug":"causal-versus-marginal-shapley-values-for","title":"Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning","date":"2021-11-04","arxiv_id":"2111.02936","n_code_links":0,"syntology":null},{"paper":null,"slug":"decision-support-models-for-predicting-and","title":"Decision Support Models for Predicting and Explaining Airport Passenger Connectivity from Data","date":"2021-11-02","arxiv_id":"2111.01915","n_code_links":0,"syntology":null},{"paper":null,"slug":"diagnosing-web-data-of-icts-to-provide","title":"Diagnosing Data from ICTs to Provide Focused Assistance in Agricultural Adoptions","date":"2021-10-29","arxiv_id":"2111.00052","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-explainability-of-hospitalization","title":"On the explainability of hospitalization prediction on a large COVID-19 patient dataset","date":"2021-10-28","arxiv_id":"2110.15002","n_code_links":0,"syntology":null},{"paper":"/paper/counterfactual-shapley-additive-explanations","slug":"counterfactual-shapley-additive-explanations","title":"Counterfactual Shapley Additive Explanations","date":"2021-10-27","arxiv_id":"2110.14270","n_code_links":2,"syntology":{"ran":11,"of":13,"n_ran_checked":11,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["jpmorganchase/cf-shap"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/partial-order-finding-consensus-among","slug":"partial-order-finding-consensus-among","title":"Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set","date":"2021-10-26","arxiv_id":"2110.13369","n_code_links":1,"syntology":null},{"paper":null,"slug":"provably-robust-model-centric-explanations","title":"Provably Robust Model-Centric Explanations for Critical Decision-Making","date":"2021-10-26","arxiv_id":"2110.13937","n_code_links":0,"syntology":null},{"paper":null,"slug":"lexicon-creation-for-interpretable-nlp-models","title":"Lexicon Creation for Interpretable NLP Models","date":"2021-10-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"robotic-lever-manipulation-using-hindsight","title":"Robotic Lever Manipulation using Hindsight Experience Replay and Shapley Additive Explanations","date":"2021-10-07","arxiv_id":"2110.03292","n_code_links":0,"syntology":null},{"paper":"/paper/shapley-variable-importance-clouds-for","slug":"shapley-variable-importance-clouds-for","title":"Shapley variable importance clouds for interpretable machine learning","date":"2021-10-06","arxiv_id":"2110.02484","n_code_links":1,"syntology":null},{"paper":"/paper/fast-treeshap-accelerating-shap-value","slug":"fast-treeshap-accelerating-shap-value","title":"Fast TreeSHAP: Accelerating SHAP Value Computation for Trees","date":"2021-09-20","arxiv_id":"2109.09847","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":null}},{"paper":"/paper/global-and-local-interpretation-of-black-box","slug":"global-and-local-interpretation-of-black-box","title":"Global and Local Interpretation of black-box Machine Learning models to determine prognostic factors from early COVID-19 data","date":"2021-09-10","arxiv_id":"2109.05087","n_code_links":1,"syntology":null},{"paper":"/paper/misleading-the-covid-19-vaccination-discourse","slug":"misleading-the-covid-19-vaccination-discourse","title":"Misleading the Covid-19 vaccination discourse on Twitter: An exploratory study of infodemic around the pandemic","date":"2021-08-16","arxiv_id":"2108.10735","n_code_links":1,"syntology":null},{"paper":null,"slug":"data-driven-advice-for-interpreting-local-and","title":"Data-driven advice for interpreting local and global model predictions in bioinformatics problems","date":"2021-08-13","arxiv_id":"2108.06201","n_code_links":0,"syntology":null},{"paper":null,"slug":"realised-volatility-forecasting-machine","title":"Realised Volatility Forecasting: Machine Learning via Financial Word Embedding","date":"2021-08-01","arxiv_id":"2108.00480","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-objective-optimization-and-explanation","title":"Multi-objective optimization and explanation for stroke risk assessment in Shanxi province","date":"2021-07-29","arxiv_id":"2107.14060","n_code_links":0,"syntology":null},{"paper":"/paper/feature-synergy-redundancy-and-independence","slug":"feature-synergy-redundancy-and-independence","title":"Feature Synergy, Redundancy, and Independence in Global Model Explanations using SHAP Vector Decomposition","date":"2021-07-26","arxiv_id":"2107.12436","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["BCG-Gamma/facet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"predicting-driver-takeover-time-in","title":"Predicting Driver Takeover Time in Conditionally Automated Driving","date":"2021-07-20","arxiv_id":"2107.09545","n_code_links":0,"syntology":null},{"paper":"/paper/path-integrals-for-the-attribution-of-model","slug":"path-integrals-for-the-attribution-of-model","title":"Attribution of Predictive Uncertainties in Classification Models","date":"2021-07-19","arxiv_id":"2107.08756","n_code_links":1,"syntology":null},{"paper":"/paper/quantifying-explainability-in-nlp-and","slug":"quantifying-explainability-in-nlp-and","title":"Quantifying Explainability in NLP and Analyzing Algorithms for Performance-Explainability Tradeoff","date":"2021-07-12","arxiv_id":"2107.05693","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":1,"n_instrument":3,"unverified":1,"pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["mnaylor5/quantifying-explainability"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/does-dataset-complexity-matters-for-model","slug":"does-dataset-complexity-matters-for-model","title":"Does Dataset Complexity Matters for Model Explainers?","date":"2021-07-06","arxiv_id":"2107.02661","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-machine-learning-approach-to-safer-airplane","title":"A Decision Support System for Safer Airplane Landings: Predicting Runway Conditions Using XGBoost and Explainable AI","date":"2021-07-01","arxiv_id":"2107.04010","n_code_links":0,"syntology":null},{"paper":null,"slug":"semantic-reasoning-from-model-agnostic","title":"Semantic Reasoning from Model-Agnostic Explanations","date":"2021-06-29","arxiv_id":"2106.15433","n_code_links":0,"syntology":null},{"paper":"/paper/synthetic-benchmarks-for-scientific-research","slug":"synthetic-benchmarks-for-scientific-research","title":"Synthetic Benchmarks for Scientific Research in Explainable Machine Learning","date":"2021-06-23","arxiv_id":"2106.12543","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["abacusai/xai-bench"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"interpretable-machine-learning-classifiers","title":"Interpretable Machine Learning Classifiers for Brain Tumour Survival Prediction","date":"2021-06-17","arxiv_id":"2106.09424","n_code_links":0,"syntology":null},{"paper":"/paper/an-imprecise-shap-as-a-tool-for-explaining","slug":"an-imprecise-shap-as-a-tool-for-explaining","title":"An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data","date":"2021-06-16","arxiv_id":"2106.09111","n_code_links":1,"syntology":null},{"paper":"/paper/developing-a-fidelity-evaluation-approach-for","slug":"developing-a-fidelity-evaluation-approach-for","title":"Developing a Fidelity Evaluation Approach for Interpretable Machine Learning","date":"2021-06-16","arxiv_id":"2106.08492","n_code_links":1,"syntology":null},{"paper":"/paper/mshap-shap-values-for-two-part-models","slug":"mshap-shap-values-for-two-part-models","title":"mSHAP: SHAP Values for Two-Part Models","date":"2021-06-16","arxiv_id":"2106.08990","n_code_links":1,"syntology":null},{"paper":"/paper/to-trust-or-not-to-trust-an-explanation-using","slug":"to-trust-or-not-to-trust-an-explanation-using","title":"To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods","date":"2021-06-01","arxiv_id":"2106.00461","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["amparore/leaf"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/understanding-peacefulness-through-the-world","slug":"understanding-peacefulness-through-the-world","title":"Understanding peacefulness through the world news","date":"2021-06-01","arxiv_id":"2106.00306","n_code_links":1,"syntology":null},{"paper":null,"slug":"evaluating-the-correctness-of-explainable-ai","title":"Evaluating the Correctness of Explainable AI Algorithms for Classification","date":"2021-05-20","arxiv_id":"2105.09740","n_code_links":0,"syntology":null},{"paper":null,"slug":"explainable-activity-recognition-for-smart","title":"Explainable Activity Recognition for Smart Home Systems","date":"2021-05-20","arxiv_id":"2105.09787","n_code_links":0,"syntology":null},{"paper":"/paper/quantified-sleep-machine-learning-techniques","slug":"quantified-sleep-machine-learning-techniques","title":"Quantified Sleep: Machine learning techniques for observational n-of-1 studies","date":"2021-05-14","arxiv_id":"2105.06811","n_code_links":1,"syntology":null},{"paper":null,"slug":"xai-handbook-towards-a-unified-framework-for","title":"XAI Handbook: Towards a Unified Framework for Explainable AI","date":"2021-05-14","arxiv_id":"2105.06677","n_code_links":0,"syntology":null},{"paper":"/paper/a-hybrid-machine-learning-deep-learning-covid","slug":"a-hybrid-machine-learning-deep-learning-covid","title":"A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data","date":"2021-05-13","arxiv_id":"2105.06141","n_code_links":1,"syntology":null},{"paper":null,"slug":"what-s-wrong-with-this-video-comparing","title":"What's wrong with this video? Comparing Explainers for Deepfake Detection","date":"2021-05-12","arxiv_id":"2105.05902","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparing-interpretability-and-explainability","title":"Comparing interpretability and explainability for feature selection","date":"2021-05-11","arxiv_id":"2105.05328","n_code_links":0,"syntology":null},{"paper":null,"slug":"explainable-artificial-intelligence-for-human","title":"Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain","date":"2021-05-05","arxiv_id":"2105.02357","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparison-of-machine-learning-methods-for-2","title":"Winter wheat yield prediction using convolutional neural networks from environmental and phenological data","date":"2021-05-04","arxiv_id":"2105.01282","n_code_links":0,"syntology":null},{"paper":"/paper/trustyai-explainability-toolkit","slug":"trustyai-explainability-toolkit","title":"TrustyAI Explainability Toolkit","date":"2021-04-26","arxiv_id":"2104.12717","n_code_links":1,"syntology":null},{"paper":"/paper/interpretation-of-multi-label-classification","slug":"interpretation-of-multi-label-classification","title":"Interpretation of multi-label classification models using shapley values","date":"2021-04-21","arxiv_id":"2104.10505","n_code_links":1,"syntology":null},{"paper":"/paper/beyond-outlier-detection-outlier","slug":"beyond-outlier-detection-outlier","title":"Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network","date":"2021-04-19","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/an-explainable-machine-learning-based-network","slug":"an-explainable-machine-learning-based-network","title":"Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection","date":"2021-04-15","arxiv_id":"2104.07183","n_code_links":0,"syntology":null},{"paper":"/paper/fast-hierarchical-games-for-image","slug":"fast-hierarchical-games-for-image","title":"Fast Hierarchical Games for Image Explanations","date":"2021-04-13","arxiv_id":"2104.06164","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-a-rigorous-evaluation-of","title":"Towards a Rigorous Evaluation of Explainability for Multivariate Time Series","date":"2021-04-06","arxiv_id":"2104.04075","n_code_links":0,"syntology":null},{"paper":null,"slug":"interpretable-ml-driven-strategy-for","title":"Volume-Centred Range Bars: Novel Interpretable Representation of Financial Markets Designed for Machine Learning Applications","date":"2021-03-23","arxiv_id":"2103.12419","n_code_links":0,"syntology":null},{"paper":null,"slug":"explaining-black-box-algorithms-using","title":"Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals","date":"2021-03-22","arxiv_id":"2103.11972","n_code_links":0,"syntology":null},{"paper":"/paper/understanding-heart-failure-patients-ehr","slug":"understanding-heart-failure-patients-ehr","title":"Understanding Heart-Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions","date":"2021-03-20","arxiv_id":"2103.11254","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-new-interpretable-unsupervised-anomaly","title":"A new interpretable unsupervised anomaly detection method based on residual explanation","date":"2021-03-14","arxiv_id":"2103.07953","n_code_links":0,"syntology":null},{"paper":null,"slug":"explaining-network-intrusion-detection-system","title":"Explaining Network Intrusion Detection System Using Explainable AI Framework","date":"2021-03-12","arxiv_id":"2103.07110","n_code_links":0,"syntology":null},{"paper":null,"slug":"ensembles-of-random-shaps","title":"Ensembles of Random SHAPs","date":"2021-03-04","arxiv_id":"2103.03302","n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-driver-fatigue-in-automated","title":"Predicting Driver Fatigue in Automated Driving with Explainability","date":"2021-03-03","arxiv_id":"2103.02162","n_code_links":0,"syntology":null},{"paper":null,"slug":"combat-covid-19-infodemic-using-explainable","title":"Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models","date":"2021-03-01","arxiv_id":"2103.00747","n_code_links":0,"syntology":null},{"paper":null,"slug":"explainable-ai-in-credit-risk-management","title":"Explainable AI in Credit Risk Management","date":"2021-03-01","arxiv_id":"2103.00949","n_code_links":0,"syntology":null},{"paper":null,"slug":"model-agnostic-explainability-for-visual","title":"Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning","date":"2021-02-28","arxiv_id":"2103.00370","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-explainable-artificial-intelligence","title":"An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery","date":"2021-02-23","arxiv_id":"2102.11848","n_code_links":0,"syntology":null},{"paper":"/paper/lift-cam-towards-better-explanations-for","slug":"lift-cam-towards-better-explanations-for","title":"Towards Better Explanations of Class Activation Mapping","date":"2021-02-10","arxiv_id":"2102.05228","n_code_links":1,"syntology":null},{"paper":"/paper/explainable-reinforcement-learning-for","slug":"explainable-reinforcement-learning-for","title":"Explainable Reinforcement Learning for Longitudinal Control","date":"2021-02-06","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/computing-the-hazard-ratios-associated-with","slug":"computing-the-hazard-ratios-associated-with","title":"Computing the Hazard Ratios Associated with Explanatory Variables Using Machine Learning Models of Survival Data","date":"2021-02-01","arxiv_id":"2102.00637","n_code_links":1,"syntology":null},{"paper":"/paper/better-sampling-in-explanation-methods-can-1","slug":"better-sampling-in-explanation-methods-can-1","title":"Better sampling in explanation methods can prevent dieselgate-like deception","date":"2021-01-26","arxiv_id":"2101.11702","n_code_links":1,"syntology":null},{"paper":null,"slug":"how-can-i-choose-an-explainer-an-application","title":"How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations","date":"2021-01-21","arxiv_id":"2101.08758","n_code_links":0,"syntology":null},{"paper":"/paper/sentence-based-model-agnostic-nlp","slug":"sentence-based-model-agnostic-nlp","title":"On the Granularity of Explanations in Model Agnostic NLP Interpretability","date":"2020-12-24","arxiv_id":"2012.13189","n_code_links":1,"syntology":null}],"record_sha256":"c5fe69f785b575f3825ddac89134a9a3f08b8ec4d03e2b0a5eeb07e7dd608641","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}