Article begins<\/h2>\nHow Astronomers, AI and Citizen Scientists \u201cSee\u201d with Un\/certainty<\/h2>\n
The previous decade has been hailed as \u201cthe golden age of astrophysics\u201d\u00a0<\/sup>by Oxford astronomer Chris Lintott at a Google\u00a0talk<\/a>\u00a0in 2020. Due to recent advancements in data processing and storage capacities, astronomers began \u201cseeing\u201d the universe at scales never reached before, focusing on collections of objects such\u00a0as galaxy\u00a0clusters, and the relations between collections, such that the very structure of the universe is coming into view.\u00a0<\/p>\n In this context, I have been following the emergence of the much-hyped \u201cworld\u2019s largest streaming data processor<\/a>\u201d in the Karoo, South Africa. The SKA (Square Kilometer Array<\/a>)\u00a0is a radio astronomy observatory with an infrastructure spanning Africa and Australia. Connecting these observatories together widens the baseline and increases the resolution of sources in the southern sky.\u00a0This telescope aims to break all records in terms of sensitivity, processing speed, and data collection\u00a0on Earth with estimates of ~1 petabyte of image data per day. In tracking these events, it became clear that not only was astronomy at the forefront of the big data moment, but that discovery practices were being reshaped to accommodate the data deluge.\u00a0In order to manage the vast quantities of data, scientists have turned to citizen science and, more recently, to neural-network based artificial intelligence, with astronomers drawing on their own\u00a0professional vision<\/a> and skills to coordinate these two outsourced strategies.\u00a0AI is hailed as a game-changer in the quantified era. In contrast, citizen science\u2014or amateur astronomy\u2014existed for millennia before the advent of professionalized astronomy.<\/p>\n Science claims to produce knowledge that is rigorous, impartial, objective, and universal. Indeed, astrophysicists insist that astronomy is apolitical because it is grounded in allegedly value-free data from signals \u201creceived\u201d from an active universe. However, as social studies of science have taught us, scientific discovery is far from a neutral process. Astronomers devote considerable technical labor towards diminishing the noise of earthly circumstances so as to receive those signals. Classifying sources is one example of that labor. What astrophysicist Anna Scaife calls \u201cquantifying uncertainty<\/a>\u201d are statistical comparisons of\u00a0the classifying accuracy of \u201cexperts\u201d, \u201ccitizen scientists\u201d and \u201cmachine learning\u201d in terms of time-investment:<\/p>\n \n\t\t\t\t\t\t\tCredit: <\/span> \t\t\t\t\t\t \n\t\t\t\t\t\t\tA screenshot taken from a talk entitled \u201cAI & Bias in Radio Astronomy\u201d on Youtube by Anna Scaife (22:30)\t\t\t\t\t\t<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n The decontextualized, reductive nature of these statistics can make them seem to be the expression of objective fact because they obscure the interpretive steps that produced them. By contrast, attending to the sociocultural process behind the production of probabilities\u2014the work of achieving certainty\u2014reveals a different picture, and an ongoing debate over what counts as signal and noise in identifying sources.<\/p>\n Linguistic anthropology\u2019s theorizing of\u00a0how evidence and responsibility are organized in discourse \u201cepistemic modality\u201dis helpful in sifting through the stakes of these debates. Epistemic modality is a broad field of study spanning particles in linguistic descriptions of grammars to broader discourse stances or psychological states.\u00a0Jane Hill and\u00a0Judith\u00a0Irvine<\/a> developed this in terms familiar to linguistic anthropologists, where \u201cresponsibility\u201d over stretches of discourse could be managed and negotiated by speakers through a variety of strategies such as entextualization or reported speech.\u00a0Recent approaches<\/a>\u00a0move away from individual mental states, towards more intersubjective and relational understandings, such as communication tactics for asserting certainty over the source of knowledge. In my research, I cast a wide net, drawing out the discourse of epistemic modality from\u00a0social media channels, scientific publications, and institutional norms,\u00a0to technical inscriptions in AI process itself\u2014numerical outputs and percentages of un\/certainty.<\/p>\n \n\t\t\t\t\t\t\tCredit: <\/span> \t\t\t\t\t\t \n\t\t\t\t\t\t\tA Screenshot of Galaxy Zoo interface\t\t\t\t\t\t<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n I\u2019ve been participating in and following the activities of the popular citizen science website\u00a0Galaxy Zoo<\/a>. After completing a tutorial, citizen scientists (self-labelled Zooites) are shown photometric images, and, depending on the research goals of the project, click buttons on the interface.\u00a0Zooites, following a decision tree, classify galaxies into categories based on basic\u00a0shapes<\/em>\u00a0(morphologies) such as spiral or elliptical, whether it has a bulge, or is barred,\u00a0textures<\/em>\u00a0(smooth, rough),\u00a0colors<\/em>\u00a0and luminosity and\u00a0movement<\/em>\u00a0(counter\/clockwise).\u00a0<\/p>\n This interpretive work is meant to be a straightforward eyeballing exercise available to anyone independent of any deep reflection or interaction. But in reading the early blog posts linked to the science projects, I learned that astronomers were faced with some surprises. Better results were linked with featured galaxies\u2014such as those with visible spiral arms\u2014rather than boring blobs, statistically.\u00a0Humans produce less accurate results when less confident, when the words \u201cdoing real science\u201d were part of the set up, and more accurate results when told to do this for fun. While Zooites were meant to do their click-work alone, Galaxy Zoo was flooded with so many questions, astronomers opened a blog and interactive tools like \u201changouts\u201d to better explain and reassure the Zooites worried about \u201ccorrect\u201d classifications. In one such \u201changout\u201d, astronomers\u00a0reassure the Zooites: \u201cIf you see an elliptical and others see a spiral, that\u2019s still valuable information. There is no wrong answer. We don\u2019t want you to be persuaded by what others are selecting\u201d.\u00a0<\/p>\n Citizen scientists are meant to find consensus through numbers, not through talk. This discourse reveals a very cognitive, individualist approach to interpretive work. Any uncertainty would be managed statistically. But for the Zooites, the hangouts and blogs were co-opted for group interaction, to discuss, debate, find consensus and solidarity. They even served as sites for the basis of identity formation. Several groups of self-named \u201cPea-hunters\u201d, \u201cPea-pickers\u201d or \u201cPeas-Corps\u201d emerged around the hunt for \u201cPeas<\/a>\u201d\u2014round, gaseous, green formations, that astronomers had hitherto considered \u201cnoise\u201d. This points to the importance of the epistemic process being socially mediated through talk and though category-making, instead of a purely visual and mental approach to pattern recognition. Citizen scientists can launch discussions of images they find \u201cgorgeous\u201d, \u201cawesome\u201d or just curious. The social interactive process of discovery,\u00a0witnessing\u00a0the \u201cvastness\u201d as well as a \u201cweirdness\u201d of the universe with others\u2014producing noise\u2014is highly motivating.<\/p>\n In contrast, rather than discussing uncertainty, AI quantifies it. Convolutional Neural Networks (CNNs) in particular, a multi-layer type of deep learning used for images, extract and measure features to predictively classify objects. Astronomers and data scientists have been tinkering with CNNs with a vast range of techniques (clustering, translation etc.) and measurable properties, analogous to those of humans, but isolated from human reasoning and emotion. This includes pixel brightness, bars of the spiral, color, size or rotation, all geared towards accuracy, generalizability and internal coherence\u2014limiting the noise. This work requires dis-embodying the data\u00a0from human\u00a0perspective, from bias. In a pre-neural network era example,\u00a0astronomers<\/a>\u00a0found that Galaxy Zoo citizen scientists identified more counter-clockwise galaxies than clockwise, violating the cosmological principle\u2014that any wide-enough view of the universe should not depend on your position in it. But when astronomers flipped some of the counter-clockwise to clockwise, the volunteers still identified an excess of counter-clockwise, pointing the bias in the other direction\u2014to the humans. Perspective is noise, and CNNs today are very good at filtering out the factors that interfere with the signal.<\/p>\n \n\t\t\t\t\t\t\tCredit: <\/span> \t\t\t\t\t\t \n\t\t\t\t\t\t\tAn image of how Convolutional Neural Networks \u201csee\u201d. This is a CNN first layer based on pixel brightness for classifying galaxy morphologies.\t\t\t\t\t\t<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n At the same time, scientists worry about the lack of good training data and that CNNs are not skilled at actually discovering anything new. In the epistemic discourse of statistics, \u201ccertainty\u201d, \u201cprobability\u201d and \u201cconfidence\u201d of the model\u2019s predictions decline with unusually shaped galaxies. Thus citizen science remains extremely valuable to astronomers as human-labelled datasets are fed into the training process. Galaxy Zoo today capitalizes on the different strengths of the citizen scientists and AI.\u00a0A\u00a0strong distinction is made between machine learning (here an \u201cAI assistant\u201d named\u00a0Zoobot<\/a>) and the exceptional human eye:\u00a0<\/p>\n \u201cYour time is precious. Galaxy Zoo volunteers can recognise and classify the detailed features of galaxies in ways that Zoobot can\u2019t\u2014and nor can any other algorithm. More than that, humans have a unique ability to spot things that look just a little bit weird. Volunteers\u00a0talking about strange objects<\/em>\u00a0[emphasis mine] has led to some of our favourite discoveries, including the\u00a0Voorwerpen<\/a>. Using Zoobot means you will be much more likely to see more diverse galaxies and come across more weird and wonderful objects.\u201d<\/p>\n<\/blockquote>\n There is a division of classifying labor between \u201cmore featured, textured, unusual\u201d galaxies (on the left) and \u201cboring\u201d \u201csmooth\u201d blobs (on the right). Galaxy Zoo will avoid \u201cshowing galaxies where Zoobot is 90% confident that fewer than 2 out of 10 volunteers would click \u201cfeatured\u201d.\u00a0<\/p>\n \n\t\t\t\t\t\t\tCredit: <\/span> \t\t\t\t\t\t \n\t\t\t\t\t\t\tA screenshot of Galaxy Zoo Site News \u201cBetter Galaxies \u2013 Better Science\u201d , by Mike Walmsly\t\t\t\t\t\t<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n Over\u00a0time, astronomers running Galaxy Zoo took note of the value\u00a0of \u201ctalking about strange objects.\u201d In a current\u00a0experiment,<\/a>\u00a0the interface allows citizen scientists to communicate their uncertainty on a sliding scale. If I am uncertain about seeing a strong bar or weak bar,\u00a0I\u00a0can use the slider tool or write a message in a \u201ctalks\u201d function. This relational interpretive work, this epistemic modality, is then buried behind percentages of certainty, extracted from the contexts of their production. What was noise became signal. The automated AI classifier is depicted as an autonomous actor, without a trace of human perspective.\u00a0<\/p>\n \n\t\t\t\t\t\t\tCredit: <\/span> \t\t\t\t\t\t \n\t\t\t\t\t\t\tA screenshot of Galaxy Zoo Project \u201cLetting Things Slide\u201d, by Mike Walmsly\t\t\t\t\t\t<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n This experiment aside, the general thrust of the field is aimed at bigger datasets and unsupervised automated classifying algorithms, leaving citizen scientists and talking behind: \u201chumans don\u2019t scale up\u201d.\u00a0\u00a0But the advent of this future parallels growing questions in astronomy. Is bigger data better science? What is being classified and why? What is the role of the expert astronomer? These point to a field in flux, epistemic uncertainty about signals and noise and even what counts as \u201cdiscovery\u201d.\u00a0\u00a0<\/p>\n Further readings from the\u00a0Journal of Linguistic Anthropology<\/em>\u00a0on migration, language learning, and postcoloniality:<\/strong><\/p>\n Robyn Holly Taylor-Neu, Parasites and Post-Truth Climate<\/a><\/p>\n Colin Michael Egenberger Halverson, Evidence and Expertise in Genetic Nomenclatures<\/a><\/p>\n
\n\t\t\t\t\t\t\tSiri Lamoureaux<\/span>\n\t\t\t\t\t\t<\/p>\n![\"A](\"https:\/\/www.anthropology-news.org\/wp-content\/uploads\/2025\/05\/Picture1-6c7b5f8700e41c03fdbe6cb4e695d49e-856x137.jpg\")
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\n\t\t\t\t\t\t\tSiri Lamoureaux<\/span>\n\t\t\t\t\t\t<\/p>\n![\"An](\"https:\/\/www.anthropology-news.org\/wp-content\/uploads\/2025\/05\/Picture3-220c460cc77d53c831830be5303017ef.jpg\")
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\n\t\t\t\t\t\t\tSiri Lamoureaux<\/span>\n\t\t\t\t\t\t<\/p>\n![\"A](\"https:\/\/www.anthropology-news.org\/wp-content\/uploads\/2025\/05\/Picture4-a28bec9dc7eb1421ad42932151596947-856x481.jpg\")
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\n\t\t\t\t\t\t\tSiri Lamoureaux<\/span>\n\t\t\t\t\t\t<\/p>\n![\"A](\"https:\/\/www.anthropology-news.org\/wp-content\/uploads\/2025\/05\/Picture5-d87f9ede42b976b0a971eaaa899a0089-856x487.png\")
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