Human-centered interactions with text classifiers: fusing concept-based knowledge with local surrogate explanation models
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Bamberg
2023
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| Schlagworte: | |
| Online-Zugang: | kostenfrei kostenfrei Inhaltsverzeichnis |
| Beschreibung: | Kumulative Dissertation |
| Beschreibung: | XV, 155 Seiten Illustrationen, Diagramme (teilweise farbig) |
| DOI: | 10.20378/irb-89771 |
Internformat
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Datensatz im Suchindex
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| adam_text |
Table of Contents I Synopsis of Thesis 1 Introduction 1 1.1 Motivation and Background. 1 1.2 Application Domain . 4 1.3 Research Questions. 5 1.4 Structure of Thesis. 6 2 A Framework for Comprehensible and Interactive Artificial Intelligence 8 2.1 The Need for Comprehensibility and Interactivity. 10 2.2 Comprehensible Artificial Intelligence . 13 2.3 Scrutable and Interactive Artificial Intelligence . 14 3 Concept-based Explainable Machine Learning for Text Classification 16 3.1 Explainable Artificial Intelligence. 17 3.2 Local Interpretable Model-agnostic Explanations. 18 3.3 Desiderata for Human-friendly Explanations. 20 3.4 Topic-based Approach for Contextual Explanations. 22 3.4.1 Latent Dirichlet Allocation for Semantic Alignment. 23 3.4.2 TopicLIME. 27 Local Fidelity of Surrogate Explanation Models. 32 Empirical Evaluation of Topic-based Explanations. 35 3.5.1 Preference Selection
Task. 35 3.5.2 Forward Prediction Task. 38 Implications and Discussion. 41 3.4.3 3.5 3.5.3 3.6 Excursus: Model-agnostic and Receiver-dependent Explanations for Unsupervised Anomaly Detection. 41 X
Concept-based Explanatory Interactive Machine Learning for Text Classification 47 4 4.1 Explanatory Interactive Machine Learning. 4.2 CAIPI. 50 4.3 Desiderata for Human-friendly and Efficient Corrections. 52 4.4 Topic-based Approach for Contextual and ConstructiveCorrections 54 4.4.1 Semantic Push. 55 4.4.2 Predictive Performance and Local Explanation Quality. 57 Conclusion and Outlook 5 48 62 References II 65 Appendix A Publications and Details on Written Scientific Contributions A.1 A.1.1 A.2 81 A Framework for Comprehensible and Interactive Artificial Intelligence . 81 Kiefer et al. "The Next Generation of Medical Decision Support: A Roadmap Toward Transparent Expert Companions.” In: frontiers in Artificial Intelligence 2020 . 81 Concept-based Explainable Machine Learning for Text Classification. 96 A.2.1 Kiefer, S. "CaSE: Explaining Text Classifications by Fusion of Local Surrogate Explanation Models with Contextual and Semantic Knowledge.” In: Information Fusion 2022 . 96 A.2.2 Kiefer et al. "Unsupervised Anomaly Detection for Financial Auditing with Model-Agnostic Explanations.” In: KI 2021: Advances in
Artificial Intelligence.109 A.3 A.3.1 Concept-based Explanatory Interactive Machine Learning for Text Classification. 129 Kiefer et al. "Semantic Interactive Learning for Text Classification: A Constructive Approach for Contextual Interactions." In: Machine Learning and Knowledge Extraction 2022 . 129 В Additional Results B.1 148 Empirical Evaluation of Topic-based Explanations. 148 xi |
| adam_txt |
Table of Contents I Synopsis of Thesis 1 Introduction 1 1.1 Motivation and Background. 1 1.2 Application Domain . 4 1.3 Research Questions. 5 1.4 Structure of Thesis. 6 2 A Framework for Comprehensible and Interactive Artificial Intelligence 8 2.1 The Need for Comprehensibility and Interactivity. 10 2.2 Comprehensible Artificial Intelligence . 13 2.3 Scrutable and Interactive Artificial Intelligence . 14 3 Concept-based Explainable Machine Learning for Text Classification 16 3.1 Explainable Artificial Intelligence. 17 3.2 Local Interpretable Model-agnostic Explanations. 18 3.3 Desiderata for Human-friendly Explanations. 20 3.4 Topic-based Approach for Contextual Explanations. 22 3.4.1 Latent Dirichlet Allocation for Semantic Alignment. 23 3.4.2 TopicLIME. 27 Local Fidelity of Surrogate Explanation Models. 32 Empirical Evaluation of Topic-based Explanations. 35 3.5.1 Preference Selection
Task. 35 3.5.2 Forward Prediction Task. 38 Implications and Discussion. 41 3.4.3 3.5 3.5.3 3.6 Excursus: Model-agnostic and Receiver-dependent Explanations for Unsupervised Anomaly Detection. 41 X
Concept-based Explanatory Interactive Machine Learning for Text Classification 47 4 4.1 Explanatory Interactive Machine Learning. 4.2 CAIPI. 50 4.3 Desiderata for Human-friendly and Efficient Corrections. 52 4.4 Topic-based Approach for Contextual and ConstructiveCorrections 54 4.4.1 Semantic Push. 55 4.4.2 Predictive Performance and Local Explanation Quality. 57 Conclusion and Outlook 5 48 62 References II 65 Appendix A Publications and Details on Written Scientific Contributions A.1 A.1.1 A.2 81 A Framework for Comprehensible and Interactive Artificial Intelligence . 81 Kiefer et al. "The Next Generation of Medical Decision Support: A Roadmap Toward Transparent Expert Companions.” In: frontiers in Artificial Intelligence 2020 . 81 Concept-based Explainable Machine Learning for Text Classification. 96 A.2.1 Kiefer, S. "CaSE: Explaining Text Classifications by Fusion of Local Surrogate Explanation Models with Contextual and Semantic Knowledge.” In: Information Fusion 2022 . 96 A.2.2 Kiefer et al. "Unsupervised Anomaly Detection for Financial Auditing with Model-Agnostic Explanations.” In: KI 2021: Advances in
Artificial Intelligence.109 A.3 A.3.1 Concept-based Explanatory Interactive Machine Learning for Text Classification. 129 Kiefer et al. "Semantic Interactive Learning for Text Classification: A Constructive Approach for Contextual Interactions." In: Machine Learning and Knowledge Extraction 2022 . 129 В Additional Results B.1 148 Empirical Evaluation of Topic-based Explanations. 148 xi |
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| doi_str_mv | 10.20378/irb-89771 |
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| spelling | Kiefer, Sebastian Verfasser (DE-588)1246702932 aut Human-centered interactions with text classifiers fusing concept-based knowledge with local surrogate explanation models vorgelegt von Sebastian Manuel Kiefer né Bruckert Bamberg 2023 XV, 155 Seiten Illustrationen, Diagramme (teilweise farbig) txt rdacontent n rdamedia nc rdacarrier Kumulative Dissertation Dissertation Otto-Friedrich-Universität Bamberg 2023 Erklärbare künstliche Intelligenz (DE-588)1263068472 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Maschinelles Lernen (DE-588)4193754-5 s Erklärbare künstliche Intelligenz (DE-588)1263068472 s DE-604 Erscheint auch als Online-Ausgabe 10.20378/irb-89771 urn:nbn:de:bvb:473-irb-897715 https://doi.org/10.20378/irb-89771 Verlag kostenfrei Volltext https://nbn-resolving.org/urn:nbn:de:bvb:473-irb-897715 Resolving-System kostenfrei Volltext Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034784407&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kiefer, Sebastian Human-centered interactions with text classifiers fusing concept-based knowledge with local surrogate explanation models Erklärbare künstliche Intelligenz (DE-588)1263068472 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)1263068472 (DE-588)4193754-5 (DE-588)4113937-9 |
| title | Human-centered interactions with text classifiers fusing concept-based knowledge with local surrogate explanation models |
| title_auth | Human-centered interactions with text classifiers fusing concept-based knowledge with local surrogate explanation models |
| title_exact_search | Human-centered interactions with text classifiers fusing concept-based knowledge with local surrogate explanation models |
| title_exact_search_txtP | Human-centered interactions with text classifiers fusing concept-based knowledge with local surrogate explanation models |
| title_full | Human-centered interactions with text classifiers fusing concept-based knowledge with local surrogate explanation models vorgelegt von Sebastian Manuel Kiefer né Bruckert |
| title_fullStr | Human-centered interactions with text classifiers fusing concept-based knowledge with local surrogate explanation models vorgelegt von Sebastian Manuel Kiefer né Bruckert |
| title_full_unstemmed | Human-centered interactions with text classifiers fusing concept-based knowledge with local surrogate explanation models vorgelegt von Sebastian Manuel Kiefer né Bruckert |
| title_short | Human-centered interactions with text classifiers |
| title_sort | human centered interactions with text classifiers fusing concept based knowledge with local surrogate explanation models |
| title_sub | fusing concept-based knowledge with local surrogate explanation models |
| topic | Erklärbare künstliche Intelligenz (DE-588)1263068472 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Erklärbare künstliche Intelligenz Maschinelles Lernen Hochschulschrift |
| url | https://doi.org/10.20378/irb-89771 https://nbn-resolving.org/urn:nbn:de:bvb:473-irb-897715 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034784407&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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