Teso, Stefano ; Alkan, Öznur ; Stammer, Wolfgang ; Daly, Elizabeth (2023)
Leveraging explanations in interactive machine learning: An overview.
In: Frontiers in Artificial Intelligence, 6
doi: 10.3389/frai.2023.1066049
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
Explanations have gained an increasing level of interest in the AI and Machine Learning (ML) communities in order to improve model transparency and allow users to form a mental model of a trained ML model. However, explanations can go beyond this one way communication as a mechanism to elicit user control, because once users understand, they can then provide feedback. The goal of this paper is to present an overview of research where explanations are combined with interactive capabilities as a mean to learn new models from scratch and to edit and debug existing ones. To this end, we draw a conceptual map of the state-of-the-art, grouping relevant approaches based on their intended purpose and on how they structure the interaction, highlighting similarities and differences between them. We also discuss open research issues and outline possible directions forward, with the hope of spurring further research on this blooming research topic.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Teso, Stefano ; Alkan, Öznur ; Stammer, Wolfgang ; Daly, Elizabeth |
| Art des Eintrags: | Bibliographie |
| Titel: | Leveraging explanations in interactive machine learning: An overview |
| Sprache: | Englisch |
| Publikationsjahr: | 2023 |
| Ort: | Darmstadt |
| Verlag: | Frontiers Media S.A. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Frontiers in Artificial Intelligence |
| Jahrgang/Volume einer Zeitschrift: | 6 |
| Kollation: | 19 Seiten |
| DOI: | 10.3389/frai.2023.1066049 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Explanations have gained an increasing level of interest in the AI and Machine Learning (ML) communities in order to improve model transparency and allow users to form a mental model of a trained ML model. However, explanations can go beyond this one way communication as a mechanism to elicit user control, because once users understand, they can then provide feedback. The goal of this paper is to present an overview of research where explanations are combined with interactive capabilities as a mean to learn new models from scratch and to edit and debug existing ones. To this end, we draw a conceptual map of the state-of-the-art, grouping relevant approaches based on their intended purpose and on how they structure the interaction, highlighting similarities and differences between them. We also discuss open research issues and outline possible directions forward, with the hope of spurring further research on this blooming research topic. |
| Freie Schlagworte: | human-in-the-loop, explainable AI, interactive machine learning, model debugging, model editing |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Künstliche Intelligenz und Maschinelles Lernen |
| Hinterlegungsdatum: | 02 Aug 2024 12:51 |
| Letzte Änderung: | 02 Aug 2024 12:51 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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Leveraging explanations in interactive machine learning: An overview. (deposited 11 Apr 2023 11:57)
- Leveraging explanations in interactive machine learning: An overview. (deposited 02 Aug 2024 12:51) [Gegenwärtig angezeigt]
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