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Workshop "Exploring the Interplay between Human Learning and Machine Learning. The Citizen Science Perspective" April 11-12, 2019

Workshop "Exploring the Interplay between Human Learning and Machine Learning. The Citizen Science Perspective" April 11-12, 2019

Context and Aim of the Workshop

This workshop was organised by the JRC work packages DigiTranScope, CSData and HUMAINT, in collaboration with the COST Action Citizen Science to promote creativity, scientific literacy, and innovation throughout Europe. The workshop was the first event within this COST Action devoted to the interaction between machine learning and action learning in citizen science, and aimed to raise awareness about opportunities and issues emerging from this interrelation.

Over the past few years, machine learning technology has advanced and sophisticated models have been proposed in computer vision, music processing and bioinformatics, to name few areas. Machine learning could help perform research tasks usually given to citizen scientists. It is possible to train an algorithm develop specific image recognition skills which can be used in projects that require classification of large amounts of image data. For example, automatic plant image identification is now receiving attention in both botany and computer communities could be used in citizen science. However, in other fields such as astronomy, the classification of galaxies structure is not yet considered a task for computers and the human eye is still seen as the perfect pattern recognition tool.

A general problem in citizen science (and generally in science) is that data grows much faster than the number of citizen science volunteers. Although human efforts will always be needed in citizen science, combining these efforts with big data techniques has been said to help researchers process more data faster and allow the volunteers to focus on the harder classifications

The workshop was introductory and explored – at a broad level – several issues, including the ways in which human reasoning could complement machine learning, and the challenges of the increased influence of machines on participation in citizen science. The event elicited the different views of attendees. There were 14 participants, including: eight Early-Career Investigators from Hungary, Portugal, Spain, Turkey, Poland and Sweden, one senior researcher from Spain (Davinia Hernandez-Leo, Pompeu Fabra, E). The Early-Career Investigators had diverse background and different levels of familiarity with the topic of the workshop.

Thursday, 11 April, 2019 - 10:30 to Friday, 12 April, 2019 - 16:30
Emilia Gutierrez Gomez (JRC Seville), Vasiliki Charisi (JRC Seville), Sven Schade (JRC Ispra), Marisa Ponti (JRC Ispra), and Laure Kloetzer (University of Neuchâtel, CH).
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