Abstract
Objectives
Although Artificial Intelligence (AI) has been a part of the computer science field for many decades, it has only recently been applied to different areas of behavioral and social sciences. This article provides an examination of the applications of AI methodologies to behavioral and social sciences exploring the areas where they are now utilized, the different tools used and their effectiveness.
Methods
The study is a systematic research examination of peer-reviewed articles (2010–2019) that used AI methodologies in social and behavioral sciences with a focus on children and families.
Results
The results indicate that artificial intelligence methodologies have been successfully applied to three main areas of behavioral and social sciences, namely (1) to increase the effectiveness of diagnosis and prediction of different conditions, (2) to increase understanding of human development and functioning, and (3) to increase the effectiveness of data management in different social and human services. Random forests, neural networks, and elastic net are among the most frequent AI methods used for prediction, supplementing traditional approaches, while natural language processing and robotics continue to increase their role in understanding human functioning and improve social services.
Conclusions
Applications of AI methodologies to behavioral and social sciences provide opportunities and challenges that need to be considered. Recommendations for future research are also included.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Afzali, M. H., Sunderland, M., Stewart, S., Masse, B., Seguin, J., Newton, N., Teesson, M., & Conrod, P. (2019). Machine-learning prediction of adolescent alcohol use: a cross-study, cross-cultural validation. Addiction, 114(4), 662671. https://doi.org/10.1111/add.14504.
AI for Good Summit. (2018). UN Geneva, Switzerland. https://www.itu.int/en/ITUT/AI/2018/Pages/default.aspx.
AI Social Good Services. (2019). Transforming social services How cognitive technology is helping to protect the most vulnerable. https://www.ibm.com/watson/advantage-reports/ai-socialgood-social-services.html.
Ahn, W., & Vassileva, J. (2016). Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence. Drug and Alcohol Dependence, 161, 247–257.
Amrit, C., Paauw, T., Aly, R., & Lavric, M. (2017). Identifying child abuse through text mining and machine learning. Expert Systems with Applications: An International Journal, 88(C), 402–418.
Askland, K. D., Garnaat, S., Sibrava, N. J., Boisseau, C. L., Strong, D., Mancebo, M., Greenberg, B., Rasmussen, S., & Eisen, J. (2015). Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy. International Journal of Methods in Psychiatric Research, 24(2), 156–169.
Battista, P., Salvatore, C., & Castiglioni, I. (2017). Optimizing neuropsychological assessments for cognitive, behavioral, and functional impairment classification: a machine learning study. Behavioural Neurology, 1850909. https://doi.org/10.1155/2017/1850909.
Bedaf, S., Gelderblom, G. J., & De Witte, L. (2015). Overview and categorization of robots supporting independent living of elderly people: what activities do they support and how far have they developed. Assistive Technology, 27(2), 88–100.
Bose, E., Maganti, S., Bowles, K. H., Brueshoff, B. L., & Monsen, K. A. (2019). Machine learning methods for identifying critical data elements in nursing documentation. Nursing Research, 68(1), 65–72.
Bostrom, N., & Yudkowski, E. (2014). The ethics or artificial Intelligence. In F. William & M. Ramsey (Eds.), The Cambridge handbook of artificial intelligence (pp. 316–330). Cambridge, UK: Cambridge University Press.
Camp, L. J., & Huber, L. L. (2017). Privacy implications of aware, active, and adaptive technologies. In S. Kwon (Ed), Gerontechnology: research, practice, and principles in the field of technology and aging (pp. 91–114). New York, NY: Springer Publishing.
Chang, T. S., Coen, M. H., Rue, A. L., Jonaitis, E., Koscik, R., Hermann, B., & Sager, M. (2012). Machine learning amplifies the effect of parental family history of Alzheimer’s disease on list learning strategy. Journal of the International Neuropsychological Society, 18(3), 428–439.
Chen, Y., Argentinis, J. E., & Weber, G. (2016). IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research. Clinical Therapeutics, 38(4), 688–701.
Chen, H. Y., Hou, T. W., & Chuang, C. H., TBPS Research Group. (2010). Applying data mining to explore the risk factors of parenting stress. Expert Systems with Applications, 37(1), 598–601.
Cho, S. H., & Lee, S. L. (2018). Prediction model for children's cognitive development using machine learning techniques. International Information Institute (Tokyo). Information, 21(1), 123–130.
Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204.
Cormen, T. H., Leiserson, C. E., Rivest, R. L. & Stein, C. (2009). Introduction to Algorithms. Cambridge, MA: MIT Press.
Cornet, G. (2013). Robot companions and ethics a pragmatic approach of ethical design. International Journal of Bioethics, 24(4), 49–58.
Crutzen, R., Giabbanelli, P. J., Jander, A., Mercken, L., & de Vries, H. (2015). Identifying binge drinkers based on parenting dimensions and alcohol-specific parenting practices: building classifiers on adolescent-parent paired data. BMC Public Health, 15(1), 747.
Ertel, W. (2018). Introduction to artificial intelligence. New York, NY: Springer.
Gawande, N. A., Daily, J. A., Siegel, C., Tallent, N. R., & Vishnu, A. (2018). Scaling deep learning workloads: Nvidia dgx-1/pascal and intel knights landing. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2018.04.073.
Gillingham, P. (2016). Predictive risk modeling to prevent child maltreatment and other adverse outcomes for service users: inside the ‘black box’ of machine learning. British Journal of Social Work, 46(4), 1044–1058.
Gillingham, P. (2017). Predictive risk modelling to prevent child maltreatment: insights and implications from Aotearoa/New Zealand. Journal of Public Child Welfare, 11(2), 150–165.
Gradus, J. L., King, M. W., Galatzer-Levy, I., & Street, A. E. (2017). Gender differences in machine learning models of trauma and suicidal ideation in veterans of the Iraq and Afghanistan wars. Journal of Traumatic Stress, 30(4), 362–371.
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., & Chen, T. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354–377.
Hamet., P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism Clinical and Experimental, 69, 36–40.
Helbing, D., Frey, B. S., Gigerenzer, G., Hafen, E., Hagner, M., Hofstetter, Y., ... & Zwitter, A. (2019). Will democracy survive big data and artificial intelligence? In D. Helbing (Ed.), Towards digital enlightenment (pp. 73–98). Cham: Springer.
Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261–266.
Ho, T. K. (1995). Random decision forests. In Proceedings of the Third IEEE International Conference on Document Analysis and Recognition, 1, 278–282.
Hong, B., Malik, A., Lundquist, J., Bellach, I., & Kontokosta, C. E. (2018). Applications of machine learning methods to predict readmission and length-of-stay for homeless families: the case of WIN shelters in New York City. Journal of Technology in Human Services, 36(1), 89–104.
Huijnen, C. A. G. J., Lexis, M. A. S., & de Witte, L. P. (2017). Robots as new tools in therapy and education for children with autism. International Journal of Neurorehabilitation, 4, 278.
Ioannidis, K., Chamberlain, S. R., Treder, M. S., Kiraly, F., Leppink, E. W., Redden, S. A., Stein, D. J., Lochner, C., & Grant, J. E. (2016). Problematic internet use (PIU): associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry. Journal of Psychiatric Research, 83, 94–102.
Insel, T. R., Landis, S. C., & Collins, F. S. (2013). The NIH brain initiative. Science, 340(6133), 687–688.
Joel, S., Eastwick, P. W., & Finkel, E. J. (2017). Is romantic desire predictable? Machine learning applied to initial romantic attraction. Psychological Science, 28(10), 1478–1489.
Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25.
Kessler, R. C., van Loo, H. M., Wardenaar, K. J., Bossarte, R. M., Brenner, L. A., Cai, T., & Zaslavsky, A. M. (2016). Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports. Molecular Psychiatry, 21(10), 1366–1371.
Kornfield, R., Sarma, P. K., Shah, D. V., McTavish, F., Landucci, G., Pe-Romashko, K., & Gustafson, D. H. (2018). Detecting recovery problems just in time: application of automated linguistic analysis and supervised machine learning to an online substance abuse forum. Journal of Medical Internet Research, 20(6), e10136. https://doi.org/10.2196/10136.
Lee, Y. L., Tsung, P. K., & Wu, M. (2018). Technology trend of edge AI. In 2018 International Symposium on VLSI Design, Automation and Test (VLSI-DAT), (pp. 1–2). IEEE.
Lenhard, F., Sauer, S., Andersson, E., Månsson, K., Mataix-Cols, D., Rück, C., & Serlachius, E. (2018). Prediction of outcome in internet-delivered cognitive behavior therapy for pediatric obsessive-compulsive disorder: a machine learning approach. International Journal of Methods in Psychiatric Research, 27(1), 1–11.
Liddy, E.D. (2001). Natural language processing. In Encyclopedia of library and information science, 2nd edn, New York, NY. Marcel Decker, Inc.
MacLeod, H., Yang, S., Oakes, K., Connelly, K., & Natarajan, S. (2016). Identifying rare diseases from behavioural data: a machine learning approach. In Connected Health: Applications, Systems and Engineering Technologies Conference, (pp. 130–139).
Mettler, T., Sprenger, M., & Winter, R. (2017). Service robots in hospitals: new perspectives on niche evolution and technology affordances. European Journal of Information Systems, 26(5), 451–468.
Miller, D. D., & Brown, E. W. (2018). Artificial intelligence in medical practice: the question to the answer? The American Journal of Medicine, 131(2), 129–133.
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G., The PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med, 6(7), e1000097. https://doi.org/10.1371/journal.pmed1000097.
Montavon, G., Samek, W., & Müller, K. R. (2018). Methods for interpreting and understanding deep neural networks. Digital Signal Processing, 73, 1–15.
Oh, J., Yun, K., Hwang, J., & Chae, J. (2017). Classification of suicide attempts through a machine learning algorithm based on multiple systemic psychiatric scales. Frontiers in Psychiatry, 8. https://doi.org/10.3389/fpsyt.2017.00192.
Pan, I., Nolan, L. B., Brown, R. R., Khan, R., van der Boor, P., Harris, D. G., & Ghani, R. (2017). Machine learning for social services: a study of prenatal case management in Illinois. American Journal of Public Health, 107(6), 938–944.
Pan, Y., Liu, H., Metsch, L. R., & Feaster, D. J. (2017). Factors associated with HIV testing among participants from substance use disorder treatment programs in the US: a machine learning approach. AIDS and Behavior, 21(2), 534–546.
Panch, T., Szolovits, P., & Atun, R. (2018). Artificial intelligence, machine learning and health systems. Journal of Global Health, 8(2). https://doi.org/10.7189/jogh.08.020303.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., & Vanderplas, J. (2011). Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Pouke, M., & Häkkilä, J. (2013). Elderly healthcare monitoring using an avatar-based 3D virtual environment. International Journal of Environmental Research and Public Health, 10, 7283–7298.
Pu, X., Fan, K., Chen, X., Ji, L., & Zhou, Z. (2016). Facial expression recognition from image sequences using twofold random forest classifier. Neurocomputing, 168, 1173–1180.
Rizzo, A., Lange, B., Buckwalter, J. G., Forbell, E., Kim, J., Sagae, K., Williams, J., Difede, J., Rothbaum, B. O., Reger, G., Parsons, T., & Kenny, P. (2011). SimCoach: an intelligent virtual human system for providing healthcare information and support. International Journal on Disability and Human Development. Special Issue: Disability, Virtual Reality and Assistive Technologies, 10(4), 277–281.
Rudin, C., & Wagstaff, K. L. (2014). Machine learning for science and society. Machine Learning, 95, 1–9.
Russell, S. J., & Norvig, P. (2010). Artificial intelligence: a modern approach. 3rd eds, Upper Saddle River, New Jersey: Pearson Education, Inc.
Ryu, S., Lee, H., Lee, D. K., & Park, K. (2018). Use of a machine learning algorithm to predict individuals with suicide ideation in the general population. Psychiatry Investigation, 15(11), 1030. https://doi.org/10.30773/pi.2018.08.27.
Schwartz, I. M., York, P., Nowakowski-Sims, E., & Ramos-Hernandez, A. (2017). Predictive and prescriptive analytics, machine learning and child welfare risk assessment: the Broward County experience. Children and Youth Services Review, 81, 309–320.
Siegwart, R., Nourbakhsh, I. R., Scaramuzza, D., & Arkin, R. C. (2011). Introduction to autonomous mobile robots. Cambridge, MA: MIT press.
Song, J., Song, T. M., Seo, D. C., & Jin, J. H. (2016). Data mining of web-based documents on social networking sites that included suicide-related words among Korean adolescents. Journal of Adolescent Health, 59(6), 668–673.
Song, J., Song, T. M., & Lee, J. R. (2018). Stay alert: forecasting the risks of sexting in Korea using social big data. Computers in Human Behavior, 81, 294–302.
Stilgoe, J. (2018). Machine learning, social learning and the governance of self-driving cars. Social Studies of Science, 48(1), 25–56.
Sze, V., Chen, Y. H., Yang, T. J., & Emer, J. S. (2017). Efficient processing of deep neural networks: a tutorial and survey. Proceedings of the IEEE, 105(12), 2295–2329.
Takahashi, Y., & Evans, L. T. (2018). An application of machine learning for predicting rearrests: significant predictors for juveniles. Race and Social Problems, 10(1), 42–52.
Tangherlini, T. R., Roychowdhury, V., Glenn, B., Crespi, C. M., Bandari, R., Wadia, A., … & Bastani, R. (2016). “Mommy Blogs” and the vaccination exemption narrative: results from a machine-learning approach for story aggregation on parenting social media sites. JMIR Public Health and Surveillance, 2(2). https://doi.org/10.2196/publichealth.6586.
Teague, S. J., & Shatte, A. B. (2018). Exploring the transition to fatherhood: feasibility study using social media and machine learning. JMIR Pediatrics and Parenting, 1(2), e12371. https://doi.org/10.2196/12371.
Walsh, C. G., Ribeiro, J.D. & Franklin, J.C. (2017). Predicting risk of suicide attempts over time through machine learning. Clinical Psychological Science, 1–12. https://doi.org/10.2196/10754.
Wall, D. P., Dally, R., Luyster, R., Jung, J., & DeLuca, T. F. (2012). Use of artificial intelligence to shorten the behavioral diagnosis of autism. PLoS ONE, 7(8), e43855. https://doi.org/10.1371/journal.pone.0043855.
Wallert, J., Gustafson, E., Held, C., Madison, G., Norlund, F., von Essen, L., Olsson, E., & Martin, G. (2017). Predicting adherence to internet-delivered psychotherapy for symptoms of depression and anxiety after myocardial infarction: machine learning insights from the U-CARE heart randomized controlled trial. Journal of Medical Internet Research, 20(10), e10754. https://doi.org/10.2196/10754.
Wang, S. H., Ding, Y., Zhao, W., Huang, Y. H., Perkins, R., Zou, W., & Chen, J. J. (2016). Text mining for identifying topics in the literatures about adolescent substance use and depression. BMC Public Health, 16(1), 279–287.
Wang, W., Hernandez, I., Newman, D. A., He, J., & Bian, J. (2016). Twitter analysis: studying US weekly trends in work stress and emotion. Applied Psychology, 65(2), 355–378.
Wiechmann, P., Lora, K., Branscum, P., & Fu, J. (2017). Identifying discriminative attributes to gain insights regarding child obesity in hispanic preschoolers using machine learning techniques. In Proceedings IEEE ICTAI, (pp. 11–15). https://doi.org/10.1109/ICTAI.2017.00014.
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., … & Bouwman, J. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3–10. https://doi.org/10.1038/sdata.2016.18.
Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301–320.
Author Contributions
M.R. and S.A.R.: co-designed the study, conducted research review, analyzed the data and wrote the paper. In the introduction section M.R. wrote the sections focused on social and behavioral science and S.A.R. wrote the sections focused on AI.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflict of interest.
Ethical Approval
Research involving human participants and/ or animals: This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Robila, M., Robila, S.A. Applications of Artificial Intelligence Methodologies to Behavioral and Social Sciences. J Child Fam Stud 29, 2954–2966 (2020). https://doi.org/10.1007/s10826-019-01689-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10826-019-01689-x