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Licensed Unlicensed Requires Authentication Published by Oldenbourg Wissenschaftsverlag April 22, 2021

A User-Centered Approach to Gamify the Manual Creation of Training Data for Machine Learning

Sarah Alaghbari, Annett Mitschick ORCID logo, Gregor Blichmann, Martin Voigt and Raimund Dachselt ORCID logo
From the journal i-com

Abstract

The development of artificial intelligence, e. g. for Computer Vision, through supervised learning requires the input of large amounts of annotated or labeled data objects as training data. Usually, the creation of high-quality training data is done manually which can be repetitive and tiring. Gamification, the use of game elements in a non-game context, is one method to make such tedious tasks more interesting. We propose a multi-step process for gamifying the manual creation of training data for machine learning purposes. In this article, we give an overview of related concepts and existing implementations and present a user-centered approach for a real-life use case. Based on a survey within the target user group we identified annotation use cases and dominant player characteristics. The results served as a foundation for designing the gamification concepts which were then discussed with the participants. The final concept includes levels of increasing difficulty, tutorials, progress indicators and a narrative built around a robot character which at the same time is a user assistant. The implemented prototype is an extension of an existing annotation tool at an AI product company and serves as a basis for further observations.

Funding source: Bundesministerium für Bildung und Forschung

Award Identifier / Grant number: 16SV8396

Funding statement: This work was partially funded by the German Research Foundation (DFG) as part of Germany’s Excellence Strategy – EXC 2050/1 – Project ID 390696704 – Cluster of Excellence “Centre for Tactile Internet with Human-in-the-Loop” (CeTI) of Technische Universität Dresden, and by DFG grant 389792660 as part of TRR 248 (see https://perspicuous-computing.science). Additionally, the work at AI4BD was funded by the Federal Ministry of Education and Research (BMBF) and the research project PANDIA (grant number 16SV8396).

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Published Online: 2021-04-22
Published in Print: 2021-04-27

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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