Applications of Artificial Intelligence Methodologies to Behavioral and Social Sciences

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. 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. 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. 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.

[1]  Dhavan V. Shah,et al.  Detecting Recovery Problems Just in Time: Application of Automated Linguistic Analysis and Supervised Machine Learning to an Online Substance Abuse Forum , 2018, Journal of medical Internet research.

[2]  L. Metsch,et al.  Factors Associated with HIV Testing Among Participants from Substance Use Disorder Treatment Programs in the US: A Machine Learning Approach , 2017, AIDS and Behavior.

[3]  A. Clark.  Whatever next? Predictive brains, situated agents, and the future of cognitive science. , 2013, The Behavioral and brain sciences.

[4]  Nicola Smania,et al.  Effects of Robot-Assisted Training for the Unaffected Arm in Patients with Hemiparetic Cerebral Palsy: A Proof-of-Concept Pilot Study , 2017, Behavioural neurology.

[5]  Juyoung Song,et al.  Stay alert: Forecasting the risks of sexting in Korea using social big data , 2018, Comput. Hum. Behav..

[6]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[7]  R. Ghani,et al.  Machine Learning for Social Services: A Study of Prenatal Case Management in Illinois. , 2017, American journal of public health.

[8]  Sebastian Sauer,et al.  Prediction of outcome in internet‐delivered cognitive behaviour therapy for paediatric obsessive‐compulsive disorder: A machine learning approach , 2018, International journal of methods in psychiatric research.

[9]  D. Schwartze,et al.  The quality of randomized controlled trials of psychotherapy for eating disorders , 2018, International journal of methods in psychiatric research.

[10]  Jicheng Fu,et al.  Identifying Discriminative Attributes to Gain Insights Regarding Child Obesity in Hispanic Preschoolers Using Machine Learning Techniques , 2017, 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI).

[11]  Colin G. Walsh,et al.  Predicting Risk of Suicide Attempts Over Time Through Machine Learning , 2017 .

[12]  Matthias S. Treder,et al.  Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry , 2016, Journal of psychiatric research.

[13]  Robin Aly,et al.  Identifying child abuse through text mining and machine learning , 2017, Expert Syst. Appl..

[14]  J. Gradus,et al.  Gender Differences in Machine Learning Models of Trauma and Suicidal Ideation in Veterans of the Iraq and Afghanistan Wars. , 2017, Journal of traumatic stress.

[15]  Kathryn H Bowles,et al.  Machine Learning Methods for Identifying Critical Data Elements in Nursing Documentation , 2019, Nursing research.

[16]  Sandra Bedaf,et al.  Overview and Categorization of Robots Supporting Independent Living of Elderly People: What Activities Do They Support and How Far Have They Developed , 2015, Assistive technology : the official journal of RESNA.

[17]  Ting Liu,et al.  Recent advances in convolutional neural networks , 2015, Pattern Recognit..

[18]  Constantine E. Kontokosta,et al.  Applications of Machine Learning Methods to Predict Readmission and Length-of-Stay for Homeless Families: The Case of Win Shelters in New York City , 2018 .

[19]  Juyoung Song,et al.  Data Mining of Web-Based Documents on Social Networking Sites That Included Suicide-Related Words Among Korean Adolescents. , 2016, The Journal of adolescent health : official publication of the Society for Adolescent Medicine.

[20]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[21]  Christopher D. Manning,et al.  Advances in natural language processing , 2015, Science.

[22]  Wojciech Samek,et al.  Methods for interpreting and understanding deep neural networks , 2017, Digit. Signal Process..

[23]  John Wallert,et al.  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 , 2018, Journal of medical Internet research.

[24]  T. Insel,et al.  The NIH BRAIN Initiative , 2013, Science.

[25]  Tianxi Cai,et al.  Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports , 2015, Molecular Psychiatry.

[26]  Patrick Kenny,et al.  SimCoach: an intelligent virtual human system for providing healthcare information and support , 2011 .

[27]  Yoshiko Takahashi,et al.  An Application of Machine Learning for Predicting Rearrests: Significant Predictors for Juveniles , 2018 .

[28]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[29]  K. Yun,et al.  Classification of Suicide Attempts through a Machine Learning Algorithm Based on Multiple Systemic Psychiatric Scales , 2017, Front. Psychiatry.

[30]  Vivienne Sze,et al.  Efficient Processing of Deep Neural Networks: A Tutorial and Survey , 2017, Proceedings of the IEEE.

[31]  Ana Ramos-Hernandez,et al.  Predictive and prescriptive analytics, machine learning and child welfare risk assessment: The Broward County experience , 2017 .

[32]  Jack Stilgoe,et al.  Machine learning, social learning and the governance of self-driving cars , 2017, Social studies of science.

[33]  E. Hafen,et al.  Will Democracy Survive Big Data and Artificial Intelligence? , 2017, Towards Digital Enlightenment.

[34]  D. Moher,et al.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement , 2009, BMJ : British Medical Journal.

[35]  Ting-Wei Hou,et al.  Applying data mining to explore the risk factors of parenting stress , 2010, Expert Syst. Appl..

[36]  M. Sunderland,et al.  Machine‐learning prediction of adolescent alcohol use: a cross‐study, cross‐cultural validation , 2018, Addiction.

[37]  Xiong Chen,et al.  Facial expression recognition from image sequences using twofold random forest classifier , 2015, Neurocomputing.

[38]  James J. Chen,et al.  Text mining for identifying topics in the literatures about adolescent substance use and depression , 2016, BMC Public Health.

[39]  Pei-Kuei Tsung,et al.  Techology trend of edge AI , 2018, 2018 International Symposium on VLSI Design, Automation and Test (VLSI-DAT).

[40]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[41]  Claire A. G. J. Huijnen,et al.  Robots as New Tools in Therapy and Education for Children with Autism , 2017 .

[42]  Ying Chen,et al.  IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research. , 2016, Clinical therapeutics.

[43]  Peter Szolovits,et al.  Artificial intelligence, machine learning and health systems , 2018, Journal of global health.

[44]  Adrian B. R. Shatte,et al.  Exploring the Transition to Fatherhood: Feasibility Study Using Social Media and Machine Learning , 2018, JMIR pediatrics and parenting.

[45]  Timothy R. Tangherlini,et al.  “Mommy Blogs” and the Vaccination Exemption Narrative: Results From A Machine-Learning Approach for Story Aggregation on Parenting Social Media Sites , 2016, JMIR public health and surveillance.

[46]  Christina L. Boisseau,et al.  Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy , 2015, International journal of methods in psychiatric research.

[47]  E. Brown,et al.  Artificial Intelligence in Medical Practice: The Question to the Answer? , 2017, The American journal of medicine.

[48]  Hyeongrae Lee,et al.  Use of a Machine Learning Algorithm to Predict Individuals with Suicide Ideation in the General Population , 2018, Psychiatry investigation.

[49]  Robert Winter,et al.  Service robots in hospitals: new perspectives on niche evolution and technology affordances , 2017, Eur. J. Inf. Syst..

[50]  Roland Siegwart,et al.  Introduction to Autonomous Mobile Robots , 2004 .

[51]  Abhinav Vishnu,et al.  Scaling Deep Learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing , 2020, Future Gener. Comput. Syst..

[52]  Paul W. Eastwick,et al.  Is Romantic Desire Predictable? Machine Learning Applied to Initial Romantic Attraction , 2017, Psychological science.

[53]  Jonna Häkkilä,et al.  Elderly Healthcare Monitoring Using an Avatar-Based 3D Virtual Environment , 2013, International journal of environmental research and public health.

[54]  Cynthia Rudin,et al.  Machine learning for science and society , 2013, Machine Learning.

[55]  P. Gillingham.  Predictive Risk Modelling to Prevent Child Maltreatment: Insights and Implications from Aotearoa/New Zealand , 2017 .

[56]  Daniel A. Newman,et al.  Twitter Analysis: Studying US Weekly Trends in Work Stress and Emotion , 2016 .

[57]  Eliezer Yudkowsky,et al.  The Ethics of Artificial Intelligence , 2014, Artificial Intelligence Safety and Security.

[58]  Philip Gillingham,et al.  Predictive Risk Modelling to Prevent Child Maltreatment and Other Adverse Outcomes for Service Users: Inside the ‘Black Box’ of Machine Learning , 2015, British journal of social work.

[59]  Woo-Young Ahn,et al.  Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence. , 2016, Drug and alcohol dependence.

[60]  Drew McDermott,et al.  Introduction to artificial intelligence , 1986, Addison-Wesley series in computer science.

[61]  Christian Salvatore,et al.  Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study , 2017, Behavioural neurology.

[62]  Shuo Yang,et al.  Identifying Rare Diseases from Behavioural Data: A Machine Learning Approach , 2016, 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE).

[63]  D. Wall,et al.  Use of Artificial Intelligence to Shorten the Behavioral Diagnosis of Autism , 2012, PloS one.

[64]  Gérard Cornet,et al.  Robot companions and ethics a pragmatic approach of ethical design. , 2013, Journal international de bioethique = International journal of bioethics.

[65]  M. Coen,et al.  Machine Learning Amplifies the Effect of Parental Family History of Alzheimer's Disease on List Learning Strategy , 2012, Journal of the International Neuropsychological Society.

[66]  Rik Crutzen,et al.  Identifying binge drinkers based on parenting dimensions and alcohol-specific parenting practices: building classifiers on adolescent-parent paired data , 2014, BMC Public Health.

[67]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.

[68]  Andreas M. Kaplan,et al.  Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence , 2019, Business Horizons.