eXplainable cooperative machine learning with NOVA
| Autor: | Michel Valstar, Elisabeth André, Florian Lingenfelser, Tobias Baur, Björn Schuller, Alexander Heimerl, Johannes Wagner |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Process (engineering)
Active learning (machine learning) business.industry Computer science 02 engineering and technology Machine learning computer.software_genre Term (time) Annotation Workflow Nova (rocket) Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering Human-in-the-loop 020201 artificial intelligence & image processing Artificial intelligence Decision-making ddc:004 business computer |
| Popis: | In the following article, we introduce a novel workflow, which we subsume under the term “explainable cooperative machine learning” and show its practical application in a data annotation and model training tool calledNOVA. The main idea of our approach is to interactively incorporate the ‘human in the loop’ when training classification models from annotated data. In particular, NOVA offers a collaborative annotation backend where multiple annotators join their workforce. A main aspect is the possibility of applying semi-supervised active learning techniques already during the annotation process by giving the possibility to pre-label data automatically, resulting in a drastic acceleration of the annotation process. Furthermore, the user-interface implements recent eXplainable AI techniques to provide users with both, a confidence value of the automatically predicted annotations, as well as visual explanation. We show in an use-case evaluation that our workflow is able to speed up the annotation process, and further argue that by providing additional visual explanations annotators get to understand the decision making process as well as the trustworthiness of their trained machine learning models. |
| Databáze: | OpenAIRE |
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