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EML4U: Erklärbares Maschinelles Lernen für interaktive episodische Updates von Modellen

Overview

The goal of the project is to develop methods of ML explainability for a question that is highly relevant for practice: Which explanations can be offered to the user to make episodic interactive learning efficient and valid, especially in applications where manual data annotation is costly? In addition to a classical feature representation of data, the project will also consider latent representations in embedding spaces (as is common in automatic language processing and knowledge graph processing) that are relevant for practice.

Funding program: Erklärbarkeit und Transparenz des Maschinellen Lernens und der Künstlichen Intelligenz

Funding program

BMBF, Grant No. 001IS19080B

Key Facts

Project duration:
04/2020 - 03/2022
Funded by:
BMBF
Websites:
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Principal Investigators

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Dr. Stefan Heindorf

Data Science Junior Research Group

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Prof. Dr. Axel-Cyrille Ngonga Ngomo

Transregional Collaborative Research Centre 318

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Project Team

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Adrian Wilke, M.Sc.

Data Science / Heinz Nixdorf Institute

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Cooperating Institutions

Universität Bielefeld

Cooperating Institution

Semalytix

Cooperating Institution

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