{"id":106059,"date":"2025-10-07T17:52:35","date_gmt":"2025-10-07T22:52:35","guid":{"rendered":"https:\/\/news.medill.northwestern.edu\/chicago\/?p=106059"},"modified":"2026-01-01T16:04:35","modified_gmt":"2026-01-01T22:04:35","slug":"in-the-zooniverse-ai-cant-take-the-citizen-out-of-citizen-science","status":"publish","type":"post","link":"https:\/\/news.medill.northwestern.edu\/chicago\/in-the-zooniverse-ai-cant-take-the-citizen-out-of-citizen-science\/","title":{"rendered":"In the Zooniverse, AI can\u2019t take the citizen out of citizen science"},"content":{"rendered":"

By Sara Cooper<\/strong><\/p>\n

Medill Reports<\/em><\/p>\n

Dan Caselden had little interest in astronomy before he became an expert in identifying undiscovered celestial bodies. Scrolling through social media in 2017, he heard of a digital project called Backyard Worlds: Planet 9<\/a> where he could volunteer his time to help astronomers identify rogue planets, brown dwarfs and potentially even a hypothesized ninth planet at the fringes of our solar system. He was hooked.<\/p>\n

\u201cThe way they presented it was as a needle-in-the-haystack, data-style problem,\u201d said Caselden, who works professionally as a software engineer. \u201cThat on its own is a good draw.\u201d<\/p>\n

Caselden joined more than 85,000 global volunteers on the Planet 9 project, flipping through millions of images from NASA\u2019s Wide-field Infrared Survey Explorer (WISE) telescope to find moving objects that might represent undiscovered planets or larger substellar objects known as brown dwarfs. As Caselden became more involved in collaboration with researchers and fellow volunteers, he had a thought: This could be a job for AI.<\/p>\n

\"An
This picture from February 2011 is the final image taken by NASA\u2019s Wide-field Infrared Survey Explorer (WISE) telescope on its primary mission to map the universe surrounding Earth. Millions of images like this supply the Backyard Worlds projects on the Zooniverse platform. (Photo courtesy of NASA)<\/figcaption><\/figure>\n

Planet 9 is a classic example of citizen science, a practice that tackles issues of large-scale data collection, processing and analysis by crowdsourcing volunteer effort. Hosted on the Zooniverse<\/a>, one of the mostcitizen science platforms with nearly 3 million registered volunteers, Planet 9 is among dozens of projects on the site that have inspired AI integration. Contrary to fears of automation replacing the human element, however, these projects demonstrate how AI has the potential to strengthen those qualities that inspire citizen scientists to volunteer their most valuable resource of time.<\/p>\n

In 2023, six years after the start of Planet 9, an AI-enhanced twin called Backyard Worlds: Cool Neighbors<\/a> launched on the Zooniverse. Tasking volunteers with a similar type of object detection, Cool Neighbors had one major difference to Planet 9 \u2014 it used a machine-learning algorithm developed by Caselden to pre-select areas of the sky most likely to contain objects of interest.<\/p>\n

The result was nearly three times the number of classifications in six months compared with Planet 9.<\/p>\n

\"Screenshot
In Backyard Worlds: Planet 9, left, volunteers watch a series of images from one section of the sky and try to identify moving objects. Backyard Worlds: Cool Neighbors, right, has a similar interface but filters sections of the sky that are most likely to contain brown dwarfs. Objects of interest are also centered on the screen, and colors are inverted to make identification easier. (Screenshots taken of Zooniverse)<\/em><\/figcaption><\/figure>\n

\u201cTo us there\u2019s a narrative where the idea of focusing on smaller, targeted areas led to better engagement and better performance,\u201d said Aaron Meisner, principal investigator behind the Backyard Worlds projects. \u201cThere\u2019s a synergy with connecting machine learning in a way that made the volunteer\u2019s time better spent and retained their efforts longer.\u201d<\/p>\n

For a platform that relies on the passion of volunteers, many of whom are inspired by the possibility of making novel scientific discoveries, the ability to increase their success rate can be key to strengthening engagement on the Zooniverse.<\/p>\n

\u201cIt\u2019s a really good hybrid model,\u201d said Cliff Johnson, Zooniverse director and science lead. \u201cWe\u2019re able to combine the best parts of machine learning by getting rid of all the boring rote things that the machines are really good at and focus the human attention on the parts that are still unknown.\u201d<\/p>\n

The need for this human input isn\u2019t going to be replaced by AI any time soon. As Cool Neighbors shows, even when trained on millions of images, the capabilities of machine learning models are limited.<\/p>\n

\"An
Sara Cooper\/MEDILL<\/figcaption><\/figure>\n

One reason is data in fields like astronomy often require low-signal noise detection. Objects of interest typically don\u2019t stand out clearly from background information, and machines can struggle to make reliable identifications.<\/p>\n

More fundamentally, algorithms can be at odds with the nature of scientific discovery.<\/p>\n

\u201cEven if you could make the AI orders of magnitude better, there\u2019s still the human element in discovering things that you didn\u2019t even realize you were looking for,\u201d Meisner said. \u201cThere\u2019s a serendipity that comes from the fact that each participant has their own pet interest.\u201d<\/p>\n

Still, there remains ample room to expand the use of AI in citizen science. One potential area is in implementing individualized AI feedback to help volunteers level-up their abilities, enriching their learning experience in the process.<\/p>\n

Lucy Fortson, an astronomer at the University of Minnesota and co-founder of the Zooniverse, envisions a cooperative model where human data can train machine-learning models while that algorithm, in turn, can provide feedback to help volunteers sharpen their ability to identify and classify relevant data.<\/p>\n

\u201cIf volunteers are thrown into the deep end on a difficult task, some of them may just leave,\u201d Fortson said. \u201cYou can have a human novice learn and a machine novice learn at the same time. The machine becomes a coach, but the coach can be corrected by the humans as well.\u201d<\/p>\n

While enthusiasm over AI is relatively high among both researchers and citizen scientist volunteers, common ethical concerns around data privacy and mistrust remain. Yet just as AI can highlight the appealing features of citizen science, pushback against its use also echoes those pre-existing concerns.<\/p>\n

\u201cSome papers are fiercely critical of participatory science in the form of citizen science,\u201d said Marisa Ponti, a professor of informatics who studies human-centered AI at the University of Gothenburg in Sweden. \u201cThis concern with the possibility of exploiting people existed far before the use of AI. AI doesn\u2019t introduce a new element, it can just amplify or exacerbate something that already exists.\u201d<\/p>\n

For scientists using the Zooniverse, these ethical concerns demand careful consideration of how they present AI features in their projects.<\/p>\n

\u201cWe have to do this very intentionally and very methodically,\u201d Fortson said. \u201cThe trust that we have with our volunteers, we absolutely do not want to break. We have the opportunity as a platform to be one of the few places where AI can be transparent, and we don\u2019t want to lose that.\u201d<\/p>\n

As Fortson suggests, the field of citizen science may present a unique space for regular people to engage with and learn about AI. As we head towards a future where automation is increasingly difficult to opt out of, education on the utility, limitations and basic operation of AI may alleviate anxieties around the technology for the Zooniverse\u2019s nearly 3 million users.<\/p>\n

\u201cIt\u2019s a good case that someone who is participating in this can basically see the entire life cycle (of machine learning), from their inputs to the final outputs of what it\u2019s used for,\u201d Johnson said. \u201cIt addresses a lot of the questions and concerns that people have about AI in general. Anything we can do as a platform to help educate our volunteers and dispel people\u2019s fears about the unknown, that\u2019s a huge win for us.\u201d<\/p>\n

To learn more about AI and its potential applications in the Zooniverse, explore this collection of papers, The Future of Artificial Intelligence and Citizen Science<\/a>, from the journal Citizen Science: Theory and Practice.<\/p>\n

Sara Cooper is a Health, Environment and Science graduate from Medill. You can see more of her work on her personal website<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

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Scrolling through social media in 2017, he heard of a digital project called Backyard Worlds: Planet 9 where he could volunteer his time to help astronomers identify rogue planets, brown dwarfs and potentially […]<\/p>\n","protected":false},"author":1036,"featured_media":106060,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5539,29,5542],"tags":[],"class_list":["post-106059","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-fall-2025","category-health-and-science","category-msj25"],"yoast_head":"\nIn the Zooniverse, AI can\u2019t take the citizen out of citizen science - Medill Reports Chicago<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/news.medill.northwestern.edu\/chicago\/in-the-zooniverse-ai-cant-take-the-citizen-out-of-citizen-science\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"In the Zooniverse, AI can\u2019t take the citizen out of citizen science - Medill Reports Chicago\" \/>\n<meta property=\"og:description\" content=\"By Sara Cooper Medill Reports Dan Caselden had little interest in astronomy before he became an expert in identifying undiscovered celestial bodies. 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