12:00-13:00 | 25.1 Citizen scientists interacting with algorithms: the good and the bad // Can't we all just get along? Hybrid human-machine approaches to citizen science in the Zooniverse
Author/s: Laure Kloetzer, Frank Ostermann, Marisa Ponti and Sven Schade // Grant Miller, Chris Lintott, Nora Eisner and Mike Walmsley
Responding to the continued and accelerating rise of Machine Learning (ML), this panel will address how citizen scientists interact and collaborate with algorithms in citizen science applications. During the session, we will:
1) run a mini poll to elicit ideas the audience ideas about one pro and one con of using ML.
2) present the paper Can't we all just get along? Hybrid human-machine approaches to citizen science in the Zooniverse. Using examples from two long-established astronomical projects, Galaxy Zoo and Planet Hunters, we consider the impact that new developments in machine learning are having on citizen science projects which involve visual inspection of large datasets. Suggestions will be made for how machine/human classification systems may produce the next generation of image analysis in citizen science projects.