Truth from the machine: artificial intelligence and the materialization of identity

References

• Ananny M., and K. Crawford. 2018. “Seeing Without Knowing: Limitations of the Transparency Ideal and Its Application to Algorithmic Accountability.” New Media & Society 20 (3): 973–989.
   Web of Science ®Google Scholar
• Bautista M. A., A. Hernández-Vela, S. Escalera, L. Igual, O. Pujol, J. Moya, V. Violant, and M. T. Anguera. 2015. “A Gesture Recognition System for Detecting Behavioral Patterns of Adhd.” IEEE Transactions on Cybernetics 46 (1): 136–147.
   Web of Science ®Google Scholar
• Beer D. G. 2017. “The Social Power of Algorithms.” Information, Communication and Society 20: 1–13.
   Web of Science ®Google Scholar
• Bhagat R. B. 2006. “Census and Caste Enumeration: British Legacy and Contemporary Practice in India.” Genus 62: 119–134.
   Google Scholar
• Bivens R., and O. L. Haimson. 2016. “Baking Gender Into Social Media Design: How Platforms Shape Categories for Users and Advertisers.” Social Media+ Society 2 (4). doi: https://doi.org/10.1177/2056305116672486.
   Web of Science ®Google Scholar
• Bowker G. C. 2014. “Big Data, Big Questions— the Theory/Data Thing.” International Journal of Communication 8: 1795–1799.
   Web of Science ®Google Scholar
• Braun L. 2014. Breathing Race into the Machine: The Surprising Career of the Spirometer From Plantation to Genetics. Minneapolis: University of Minnesota Press.
   Google Scholar
• Brookey R. A. 2002. Reinventing the Male Homosexual: The Rhetoric and Power of the Gay Gene. Bloomington: Indiana University Press.
   Google Scholar
• Browne S. 2015. Dark Matters: On the Surveillance of Blackness. Durham: Duke University Press.
   Google Scholar
• Bucher T. 2016. “Neither Black nor Box: Ways of Knowing Algorithms.” In: Innovative Methods in Media and Communication Research, 81–98. Cham: Springer.
   Google Scholar
• Burrell J. 2016. “How the Machine ‘Thinks’: Understanding Opacity in Machine Learning Algorithms.” Big Data & Society 3 (1). doi: https://doi.org/10.1177/2053951715622512.
   Web of Science ®Google Scholar
• Calude C. S., and G. Longo. 2017. “The Deluge of Spurious Correlations in Big Data.” Foundations of Science 22 (3): 595–612.
   Web of Science ®Google Scholar
• Calvo R. A., and S. D'Mello. 2010. “Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications.” IEEE Transactions on Affective Computing 1 (1): 18–37.
   Web of Science ®Google Scholar
• Campolo A., and K. Crawford. 2020. “Enchanted Determinism: Power Without Responsibility in Artificial Intelligence.” Engaging Science, Technology, and Society 6: 1–19.
   Web of Science ®Google Scholar
• Carrasquilla J., and R. G. Melko. 2017. “Machine Learning Phases of Matter.” Nature Physics 13 (5): 431.
   Web of Science ®Google Scholar
• Cetina K. K. 2009. Epistemic Cultures: How the Sciences Make Knowledge. Cambridge, MA: Harvard University Press.
   Google Scholar
• Cheney-Lippold J. 2011. “A New Algorithmic Identity: Soft Biopolitics and the Modulation of Control.” Theory, Culture & Society 28 (6): 164–181.
   Web of Science ®Google Scholar
• Coopmans C., J. Vertesi, M. E. Lynch, and S. Woolgar. 2014. Representation in Scientific Practice Revisited. Cambridge, MA: MIT Press.
   Google Scholar
• De Vries K. 2010. “Identity, Profiling Algorithms and a World of Ambient Intelligence.” Ethics and Information Technology 12 (1): 71–85.
   Web of Science ®Google Scholar
• Downing L., I. Morland, and N. Sullivan. 2015. Fuckology: Critical Essays on John Money's Diagnostic Concepts. Chicago: University of Chicago Press.
   Google Scholar
• Dumit J. 2004. Picturing Personhood: Brain Scans and Biomedical Identity. Princeton: Princeton University Press.
   Google Scholar
• Ecker C. 2011. “Autism Biomarkers for More Efficacious Diagnosis.” Biomarkers in Medicine 5 (2): 193–195.
   PubMed Web of Science ®Google Scholar
• Elish M. C., and D. Boyd. 2018. “Situating Methods in the Magic of Big Data and AI.” Communication Monographs 85 (1): 57–80.
   Web of Science ®Google Scholar
• Genetic Engineering and Biotechnology New. 2019. AI Finds Autism-Causing Mutations in ‘Junk’ DNA. Genetic Engineering and Biotechnology News. https://www.genengnews.com/news/ai-finds-autism-causing-mutations-in-junk-dna/.
   Google Scholar
• Eubanks V. 2018. Automating Inequality: How High-tech Tools Profile, Police, and Punish the Poor. New York: St. Martin's Press.
   Google Scholar
• Eyal G. 2010. The Autism Matrix. Cambridge: Polity.
   Google Scholar
• Felt U., J. Igelsböck, A. Schikowitz, and T. Völker. 2016. “Transdisciplinary Sustainability Research in Practice: Between Imaginaries of Collective Experimentation and Entrenched Academic Value Orders.” Science, Technology, & Human Values 41 (4): 732–761.
   Web of Science ®Google Scholar
• Fitzgerald D. 2017. Tracing Autism: Uncertainty, Ambiguity, and the Affective Labor of Neuroscience. Seattle: University of Washington Press.
   Google Scholar
• Fu S., H. He, and Z. G. Hou. 2014. “Learning Race From Face: A Survey.” IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (12): 2483–2509.
   PubMed Web of Science ®Google Scholar
• Galison P., and L. Daston. 2007. Objectivity.
   Google Scholar
• Geertz C. 1973. Local Knowledge: Further Essays in Interpretive Anthropology. New York: Basic books.
   Google Scholar
• Gillespie T. 2014. “The Relevance of Algorithms.” Media Technologies: Essays on Communication, Materiality, and Society 167: 167.
   Google Scholar
• Hames-Garcia M. R. 2011. Identity Complex: Making the Case for Multiplicity. Minneapolis: University of Minnesota Press.
   Google Scholar
• Hashemi J., T. V. Spina, M. Tepper, A. Esler, V. Morellas, N. Papanikolopoulos, and G. Sapiro. 2012. “A Computer Vision Approach for the Assessment of Autism-Related Behavioral Markers.” In: 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL). 1–7, IEEE.
   Google Scholar
• Haslam N., and S. R. Levy. 2006. “Essentialist Beliefs About Homosexuality: Structure and Implications for Prejudice.” Personality and Social Psychology Bulletin 32 (4): 471–485.
   PubMed Web of Science ®Google Scholar
• Hayes J., R. McCabe, T. Ford, and G. Russell. 2020. “Drawing a Line in the Sand: Affect and Testimony in Autism Assessment Teams in the UK.” Sociology of Health & Illness 42: 825–843.
   PubMed Web of Science ®Google Scholar
• Helbing D., B. S. Frey, G. Gigerenzer, E. Hafen, M. Hagner, Y. Hofstetter, J. Van Den Hoven, R. V. Zicari, and A. Zwitter. 2019. “Will democracy survive big data and artificial intelligence?” In: Towards Digital Enlightenment, 73–98. Cham: Springer.
   Google Scholar
• Hird M. J. 2004. Sex, Gender, and Science. Basingstoke: Palgrave Macmillan.
   Google Scholar
• Hollin G. 2014. “Constructing a Social Subject: Autism and Human Sociality in the 1980s.” History of the Human Sciences 27 (4): 98–115.
   Web of Science ®Google Scholar
• Jordan-Young, Rebecca M. 2011. Brain Storm: The Flaws in the Science of Sex Differences. Cambridge, MA: Harvard University Press.
   Google Scholar
• Kassraian-Fard P., C. Matthis, J. H. Balsters, M. H. Maathuis, and N. Wenderoth. 2016. “Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism As An Example.” Frontiers in Psychiatry 7: 177.
   PubMed Web of Science ®Google Scholar
• Keyes O. 2018. “The Misgendering Machines: Trans/HCI Implications of Automatic Gender Recognition.” Proceedings of the ACM on Human-Computer Interaction 2 (CSCW): 1–22.
   Google Scholar
• Keyes O. 2019. Counting the Countless: Why Data Science is a Profound Threat for Queer People. Cambridge: Real Life 2.
   Google Scholar
• Kitchin R. 2017. “Thinking Critically About and Researching Algorithms.” Information, Communication & Society 20 (1): 14–29.
   Web of Science ®Google Scholar
• Krafft P., M. Young, M. Katell, K. Huang, and G. Bugingo. 2019. Policy Versus Practice: Conceptions of Artificial Intelligence. Available at SSRN 3431304.
   Google Scholar
• Latour B. 1987. Science in Action: How to Follow Scientists and Engineers Through Society. New York: Harvard University Press.
   Google Scholar
• Law J. 2004. After Method: Mess in Social Science Research. New York: Routledge.
   Google Scholar
• Lazer D., A. Pentland, L. Adamic, S. Aral, A. L. Barabási, D. Brewer, N. Christakis, et al. 2009. “Computational Social Science.” Science (New York, N.Y.) 323 (5915): 721–723.
   PubMed Web of Science ®Google Scholar
• L'heureux A., K. Grolinger, H. F. Elyamany, and M. A. Capretz. 2017. “Machine Learning with Big Data: Challenges and Approaches.” IEEE Access 5: 7776–7797.
   Web of Science ®Google Scholar
• Lugones M. 2016. “The coloniality of gender.” In: The Palgrave handbook of gender and development, 13–33. New York: Springer.
   Google Scholar
• Maclure J. 2019. “The New AI Spring: A Deflationary View.” AI & Society 35: 1–4.
   Web of Science ®Google Scholar
• M'charek A., K. Schramm, and D. Skinner. 2014. “Topologies of Race: Doing Territory, Population and Identity in Europe.” Science, Technology, & Human Values 39 (4): 468–487.
   Web of Science ®Google Scholar
• McQuillan Dan. 2017. “Data Science as Machinic Neoplatonism.” Philosophy & Technology 31: 253–272. https://doi.org/https://doi.org/10.1007/s13347-017-0273-3
   Google Scholar
• Morandini J. S., A. Blaszczynski, M. W. Ross, D. S. Costa, and I. Dar-Nimrod. 2015. “Essentialist Beliefs, Sexual Identity Uncertainty, Internalized Homonegativity and Psychological Wellbeing in Gay Men.” Journal of Counseling Psychology 62 (3): 413.
   PubMed Web of Science ®Google Scholar
• Natale S., and A. Ballatore. 2017. “Imagining the Thinking Machine: Technological Myths and the Rise of Artificial Intelligence.” Convergence 26: 3–18.
   Web of Science ®Google Scholar
• Noble S. U. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. New York: NYU Press.
   Google Scholar
• Ortega F., and S. Choudhury. 2011. “‘Wired Up Differently’: Autism, Adolescence and the Politics of Neurological Identities.” Subjectivity 4 (3): 323–345.
   Google Scholar
• Ortoleva P. 2009. “Modern Mythologies, the Media and the Social Presence of Technology.” Observatorio (OBS*) 3 (1).
   Google Scholar
• Passi S., and P. Sengers. 2020. “Making Data Science Systems Work.” Big Data & Society 7 (2). doi: https://doi.org/10.1177/2053951720939605.
   Web of Science ®Google Scholar
• Roberts D. 2011. Fatal Invention: How Science, Politics, and Big Business Re-create Race in the Twenty-first Century. Princeton: New Press/ORIM.
   Google Scholar
• Rose N., and J. M. Abi-Rached. 2013. Neuro: The New Brain Sciences and the Management of the Mind. New York: Princeton University Press.
   Google Scholar
• Samuels E. 2014. Fantasies of Identification: Disability, Gender, Race. Vol. 10, NYU Press.
   Google Scholar
• Sato J. R., M. Q. Hoexter, P. P. de Magalhães Oliveira Jr, M. J. Brammer, D. Murphy, C. Ecker, and M. A. Consortium. 2013. “Inter-regional Cortical Thickness Correlations are Associated with Autistic Symptoms: A Machine-learning Approach.” Journal of Psychiatric Research 47 (4): 453–459.
   PubMed Web of Science ®Google Scholar
• Schultz S. 2019. Artificial Intelligence Detects a New Class of Mutations Behind Autism. Princeton University https://www.princeton.edu/news/2019/05/28/artificial-intelligence-detects-new-class-mutations-behind-autism.
   Google Scholar
• Seaver N. 2018. “What Should an Anthropology of Algorithms Do?” Cultural Anthropology 33 (3): 375–385.
   Web of Science ®Google Scholar
• Seaver, 2019. “Knowing Algorithms.” in DigitalSTS: A Field Guide for Science & Technology Studies, edited by Janet Vertesi and David Ribes. Princeton: Princeton University Press.
   Google Scholar
• Singh J. S. 2015. Multiple Autisms: Spectrums of Advocacy and Genomic Science. Minneapolis: University of Minnesota Press.
   Google Scholar
• Stark L. 2018. “Algorithmic Psychometrics and the Scalable Subject.” Social Studies of Science 48 (2): 204–231.
   PubMed Web of Science ®Google Scholar
• Terry J. 1999. An American Obsession: Science, Medicine, and Homosexuality in Modern Society. Chicago: University of Chicago Press.
   Google Scholar
• Thompson D. 2015. “What Lies Beneath: Equality and the Making of Racial Classifications.” Social Philosophy & Policy 31 (2): 114.
   Web of Science ®Google Scholar
• Tribune T. 2019. “AI Detects New Class of Genetic Mutations Behind Autism.” The Tribune https://www.tribuneindia.com/news/archive/health/story-779521.
   Google Scholar
• Van der Ploeg I. 2012. “The Body as Data in the Age of Information.” In: Routledge Handbook of Surveillance Studies, 176–183. Cham: Springer.
   Google Scholar
• Verhoeff B. 2013. “Autism in Flux: A History of the Concept From Leo Kanner to DSM-5.” History of Psychiatry 24 (4): 442–458.
   PubMed Web of Science ®Google Scholar
• Waidzunas T. 2015. The Straight Line: How the Fringe Science of Ex-gay Therapy Reoriented Sexuality. Minneapolis: University of Minnesota Press.
   Google Scholar
• Waidzunas T., and S. Epstein. 2015. “For Men Arousal is Orientation: Bodily Truthing, Technosexual Scripts, and the Materialization of Sexualities Through the Phallometric Test.” Social Studies of Science 45 (2): 187–213.
   PubMed Web of Science ®Google Scholar
• Wang Y., and M. Kosinski. 2018. “Deep Neural Networks are More Accurate Than Humans At Detecting Sexual Orientation From Facial Images.” Journal of Personality and Social Psychology 114 (2): 246.
   PubMed Web of Science ®Google Scholar
• Wolf C. T., and D. Paine. 2018. Sensemaking practices in the everyday work of AI/ML software engineering. interface .
   Google Scholar
• Woods R. 2017. “Pathological Demand Avoidance: My Thoughts on Looping Effects and Commodification of Autism.” Disability & Society 32 (5): 753–758.
   Web of Science ®Google Scholar
• Yang K. K., Z. Wu, C. N. Bedbrook, and F. H. Arnold. 2018. “Learned Protein Embeddings for Machine Learning.” Bioinformatics (Oxford, England) 34 (15): 2642–2648.
   PubMed Web of Science ®Google Scholar
• Zhou J., C. Y. Park, C. L. Theesfeld, A. K. Wong, Y. Yuan, C. Scheckel, J. J. Fak, et al. 2019. “Whole-genome Deep-learning Analysis Identifies Contribution of Noncoding Mutations to Autism Risk.” Nature Genetics 51 (6): 973–980.
   PubMed Web of Science ®Google Scholar
• Zimmer F., M. Stock, K. Scheibe, and W. G. Stock. 2019. “Fake News in Social Media: Bad Algorithms or Biased Users?” Journal of Information Science Theory and Practice 7 (2): 40–53.
   Google Scholar