AI Facial Recognition:
a Bibliography
My sources for the article "The Oppenheimer of AI." If you want one and hit a paywall, send me a note and I'll see what I can do.
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Agüera y Arcas, Blaise, Margaret Mitchell, et al. Physiognomy’s New Clothes. 20 May 2017, https://medium.com/@blaisea/physiognomys-new-clothes-f2d4b59fdd6a.
Barrett, Lisa Feldman. “Debate about Universal Facial Expressions Goes Big.” Nature, vol. 589, Jan. 2021, pp. 200–01.
Binetti, Nicola, et al. “Genetic Algorithms Reveal Profound Individual Differences in Emotion Recognition.” Proceedings of the National Academy of Sciences, vol. 119, no. 45, Nov. 2022, p. e2201380119, https://doi.org/10.1073/pnas.2201380119.
Cao, Xubo, and Michal Kosinski. “Large Language Models Know How the Personality of Public Figures Is Perceived by the General Public.” Scientific Reports, vol. 14, no. 1, Mar. 2024, p. 6735, https://doi.org/10.1038/s41598-024-57271-z.
Clayton, Aubrey. “How Eugenics Shaped Statistics.” Nautilus, 27 Oct. 2020, https://nautil.us/how-eugenics-shaped-statistics-238014/.
Cowen, Alan S., et al. “Sixteen Facial Expressions Occur in Similar Contexts Worldwide.” Nature, vol. 589, no. 7841, Jan. 2021, pp. 251–57, https://doi.org/10.1038/s41586-020-3037-7.
Daub, Adrian. “The Return of the Face.” Longreads, 3 Oct. 2018, https://longreads.com/2018/10/03/the-return-of-the-face/.
Durán, Juan I., and José-Miguel Fernández-Dols. “Do Emotions Result in Their Predicted Facial Expressions? A Meta-Analysis of Studies on the Co-Occurrence of Expression and Emotion.” Emotion, vol. 21, no. 7, Oct. 2021, pp. 1550–69, https://doi.org/10.1037/emo0001015.
Goldenfein, Jake. “The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism.” Proceedings of the Conference on Fairness, Accountability, and Transparency, ACM, 2019, pp. 110–19, https://doi.org/10.1145/3287560.3287568.
Hagendorff, Thilo, et al. “Human-like Intuitive Behavior and Reasoning Biases Emerged in Large Language Models but Disappeared in ChatGPT.” Nature Computational Science, vol. 3, no. 10, Oct. 2023, pp. 833–38, https://doi.org/10.1038/s43588-023-00527-x.
Kosinski, Michal. Evaluating Large Language Models in Theory of Mind Tasks. arXiv:2302.02083, arXiv, 16 Feb. 2024, http://arxiv.org/abs/2302.02083.
Kosinski, Michal, Sandra C. Matz, et al. “Facebook as a Research Tool for the Social Sciences: Opportunities, Challenges, Ethical Considerations, and Practical Guidelines.” American Psychologist, vol. 70, no. 6, Sept. 2015, pp. 543–56, https://doi.org/10.1037/a0039210.
Kosinski, Michal, Poruz Khambatta, et al. “Facial Recognition Technology and Human Raters Can Predict Political Orientation from Images of Expressionless Faces Even When Controlling for Demographics and Self-Presentation.” American Psychologist, Mar. 2024, https://doi.org/10.1037/amp0001295.
Kosinski, Michal. “Facial Recognition Technology Can Expose Political Orientation from Naturalistic Facial Images.” Scientific Reports, vol. 11, no. 1, Jan. 2021, p. 100, https://doi.org/10.1038/s41598-020-79310-1.
Kosinski, Michal, David Stillwell, et al. “Private Traits and Attributes Are Predictable from Digital Records of Human Behavior.” Proceedings of the National Academy of Sciences, vol. 110, no. 15, 2013, pp. 5802–05, https://doi.org/10.1073/pnas.1218772110.
Latif, Siddique, et al. AI-Based Emotion Recognition: Promise, Peril, and Prescriptions for Prosocial Path. arXiv:2211.07290, arXiv, 14 Nov. 2022. arXiv.org, http://arxiv.org/abs/2211.07290.
Lazer, David, et al. “Computational Social Science.” Science, vol. 323, no. 5915, Feb. 2009, pp. 721–23, https://doi.org/10.1126/science.1167742.
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Ludwig, Jens, and Sendhil Mullainathan. Machine Learning as a Tool for Hypothesis Generation. Working paper, 2023–28, Becker Friedman Institute for Economics, Mar. 2023, https://www.nber.org/papers/w31017.
Matz, S. C., et al. “Psychological Targeting as an Effective Approach to Digital Mass Persuasion.” Proceedings of the National Academy of Sciences, vol. 114, no. 48, Nov. 2017, pp. 12714–19. DOI.org (Crossref), https://doi.org/10.1073/pnas.1710966114.
Naqvi, Sahin, et al. “Decoding the Human Face: Progress and Challenges in Understanding the Genetics of Craniofacial Morphology.” Annual Review of Genomics and Human Genetics, vol. 23, no. 1, Aug. 2022, pp. 383–412, https://doi.org/10.1146/annurev-genom-120121-102607.
Park, Gregory, et al. “Automatic Personality Assessment through Social Media Language.” Journal of Personality and Social Psychology, vol. 108, no. 6, June 2015, pp. 934–52, https://doi.org/10.1037/pspp0000020.
Rogers, Adam. “The Cambridge Analytica Data Apocalypse Was Predicted in 2007.” Wired, 25 Mar. 2018, https://www.wired.com/story/the-cambridge-analytica-data-apocalypse-was-predicted-in-2007/
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Schwartz, H. Andrew, et al. “Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach.” PLoS ONE, vol. 8, no. 9, Sept. 2013, p. e73791, https://doi.org/10.1371/journal.pone.0073791.
Stark, Luke, and Jevan Hutson. “Physiognomic Artificial Intelligence.” SSRN Electronic Journal, 2021, https://doi.org/10.2139/ssrn.3927300.
Stinson, Catherine. “The Dark Past of Algorithms That Associate Appearance and Criminality.” American Scientist, vol. 109, no. 1, Feb. 2021, p. 26, https://www.americanscientist.org/article/the-dark-past-of-algorithms-that-associate-appearance-and-criminality.
Strümke, Inga, and Marija Slavkovik. Explainability for Identification of Vulnerable Groups in Machine Learning Models. arXiv:2203.00317, arXiv, 6 Sept. 2022. arXiv.org, http://arxiv.org/abs/2203.00317.
Todorov, Alexander, et al. “Social Attributions from Faces: Determinants, Consequences, Accuracy, and Functional Significance.” Annual Review of Psychology, vol. 66, no. 1, Jan. 2015, pp. 519–45. DOI.org (Crossref), https://doi.org/10.1146/annurev-psych-113011-143831.
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Todorov, Alexander, and DongWon Oh. “The Structure and Perceptual Basis of Social Judgments from Faces.” Advances in Experimental Social Psychology, vol. 63, Elsevier, 2021, pp. 189–245, https://doi.org/10.1016/bs.aesp.2020.11.004.
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Wang, Yilun, and Michal Kosinski. “Deep Neural Networks Are More Accurate Than Humans at Detecting Sexual Orientation From Facial Images.” Journal of Personality and Social Psychology, vol. 114, no. 2, Feb. 2018, pp. 246–57, https://doi.org/10.1037/pspa0000098.
Watts, Duncan J., et al. Explanation, Prediction, and Causality: Three Sides of the Same Coin? preprint, Open Science Framework, 31 Oct. 2018, https://doi.org/10.31219/osf.io/u6vz5.
Wu, Wenying, et al. “Gender Classification and Bias Mitigation in Facial Images.” 12th ACM Conference on Web Science, 2020. arXiv.org, https://doi.org/10.1145/3394231.
Wu, Xiaolin, and Xi Zhang. Automated Inference on Criminality Using Face Images. 1611.04135v2, arXiv, 21 Nov. 2016, http://arxiv.org/abs/1611.04135.
---. Responses to Critiques on Machine Learning of Criminality Perceptions. 1611.04135v3, 26 May 2017.
Xiang, Alice. “Mirror, Mirror, on the Wall, Who’s the Fairest of Them All?” Daedalus, vol. 153, no. 1, Mar. 2024, pp. 250–67, https://doi.org/10.1162/daed_a_02058.
Xu, Tian, et al. Investigating Bias and Fairness in Facial Expression Recognition. arXiv:2007.10075, arXiv, 21 Aug. 2020. arXiv.org, http://arxiv.org/abs/2007.10075.
Youyou, Wu, et al. “Computer-Based Personality Judgments Are More Accurate than Those Made by Humans.” Proceedings of the National Academy of Sciences, vol. 112, no. 4, Jan. 2015, pp. 1036–40., https://doi.org/10.1073/pnas.1418680112.