AI for Psychological Profiles: Advances, Challenges, and Future Directions
Resumen
This paper assesses Artificial Intelligences in the development of psychological profiles where it has prospects in clinical examination, therapy of mental disorders, and description of behavior. Yet, in the adoption of AI in the field of psychology, there are corresponding challenges to it. Issues include ethical questions in, data protection, and the transparency of the created models. This paper lays down these issues, the advantages and efficiency of employing AI solutions in profiling psychology, and possible recommendations for the further studies and research that could overcome current deficiencies. The literature review and systematic study, shows the author’s goal as to clarify the current trends and possible development of AI in psychological profiling, focusing on possible interdisciplinary cooperation and the importance of ethical regulation for achieving the best result.
Descargas
Citas
Chikersal, P., Tomprou, M., Kim, Y. J., Woolley, A. W., & Dabbish, L. (2017, February). Deep structures of collaboration: Physiological correlates of collective intelligence and group satisfaction. In Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing. Eichstaedt. https://doi.org/10.1145/2998181.2998250
Coppersmith, G., Dredze, M., Harman, C., & Hollingshead, K. (2015). From ADHD to SAD: Analyzing the language of mental health on Twitter through self-reported diagnoses. In Proceedings of the 2nd workshop on computational linguistics and clinical psychology: from linguistic signal to clinical reality, 1-10. https://doi.org/10.3115/v1/W15-1201
De Choudhury, M., Counts, S., & Horvitz, E. (2013, April). Predicting postpartum changes in emotion and behavior via social media. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 3267-3276).
https://dl.acm.org/doi/10.1145/2470654.2466447
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608. https://arxiv.org/abs/1702.08608
Eichstaedt, J. C., Schwartz, H. A., Kern, M. L., Park, G., Labarthe, D. R., Merchant, R. M., ... & Seligman, M. E. (2015). Psychological language on Twitter predicts county-level heart disease mortality. Psychological science, 26(2), 159-169. https://doi.org/10.1177/0956797614557867
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. nature, 542(7639), 115-118. https://pubmed.ncbi.nlm.nih.gov/28117445/
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People—an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds and machines, 28(4), 689-707.
https://doi.org/10.1007/s11023-018-9482-5
Gill, A., Nowson, S., & Oberlander, J. (2009, March). What are they blogging about? Personality, topic and motivation in blogs. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 3, No. 1, pp. 18-25). https://doi.org/10.1609/icwsm.v3i1.13949
Gosling, S. D., & Mason, W. (2015). Internet research in psychology. Annual review of psychology, 66(1), 877-902. https://doi.org/10.1146/annurev-psych-010814-015321
Gratch, J., Lucas, G. M., King, A. A., & Morency, L. P. (2014, May). It's only a computer: the impact of human-agent interaction in clinical interviews. In Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems (pp. 85-92).
https://dl.acm.org/doi/10.5555/2615731.2615748
Guntuku, S. C., Schneider, R., Pelullo, A., Young, J., Wong, V., Ungar, L., ... & Merchant, R. (2019). Studying expressions of loneliness in individuals using twitter: an observational study. BMJ open, 9(11), e030355-e030355. https://doi.org/10.1136/bmjopen-2019-030355
Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2017). Detecting depression and mental illness on social media: an integrative review. Current Opinion in Behavioral Sciences, 18, 43-49. https://doi.org/10.1016/j.cobeha.2017.07.005
Kern, M. L., Eichstaedt, J. C., Schwartz, H. A., Dziurzynski, L., Ungar, L. H., Stillwell, D. J., ... & Seligman, M. E. (2014). The online social self: An open vocabulary approach to personality. Assessment, 21(2), 158-169. https://doi.org/10.1177/1073191113514104
Kern, M. L., McCarthy, P. X., Chakrabarty, D., & Rizoiu, M. A. (2019). Social media-predicted personality traits and values can help match people to their ideal jobs. Proceedings of the National Academy of Sciences, 116(52), 26459-26464.
https://doi.org/10.1073/pnas.1917942116
Kosinski, M., Bachrach, Y., Kohli, P., Stillwell, D., & Graepel, T. (2014). Manifestations of user personality in website choice and behavior on online social networks. Machine Learning, 95(3), 357-380. https://doi.org/10.1007/s10994-013-5415-y
Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the national academy of sciences, 110(15), 5802-5805. https://doi.org/10.1073/pnas.1218772110
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022). A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635.
https://doi.org/10.1145/3457607
Mowery, D., Smith, H., Cheney, T., Stoddard, G., Coppersmith, G., Bryan, C., & Conway, M. (2017). Understanding depressive symptoms and psychosocial stressors on Twitter: a corpus-based study. Journal of medical Internet research, 19(2), e48-e48. https://doi.org/10.2196/jmir.6895
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
Park, G., Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Kosinski, M., Stillwell, D. J., ... & Seligman, M. E. (2015). Automatic personality assessment through social media language. Journal of personality and social psychology, 108(6), 934-952. https://doi.org/10.1037/pspp0000020
Reece, A. G., & Danforth, C. M. (2017). Instagram photos reveal predictive markers of depression. EPJ Data Science, 6(1), 1-12. 10. https://doi.org/10.1140/epjds/s13688-017-0110-z
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). " Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144). https://doi.org/10.1145/2939672.2939778
Russell, S. J., & Norvig, P. (2021). Artificial intelligence: a modern approach. 4th Ed. Pearson.
Scherer, S., Stratou, G., Lucas, G., Mahmoud, M., Boberg, J., Gratch, J., & Morency, L. P. (2014). Automatic audiovisual behavior descriptors for psychological disorder analysis. Image and Vision Computing, 32(10), 648-658. https://doi.org/10.1016/j.imavis.2014.06.001
Schwartz, H. A., Eichstaedt, J. C., Dziurzynski, L., Kern, M. L., Blanco, E., Kosinski, M., ... & Ungar, L. H. (2013, March). Toward personality insights from language exploration in social media. In 2013 AAAI Spring Symposium Series. Retrieved from:
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., ... & Ungar, L. H. (2013). Personality, gender, and age in the language of social media: The open-vocabulary approach. PloS one, 8(9), 1-16. https://doi.org/10.1371/journal.pone.0073791
Settanni, M., Azucar, D., & Marengo, D. (2018). Predicting individual characteristics from digital traces on social media: A meta-analysis. Cyberpsychology, Behavior, and Social Networking, 21(4), 217-228. http://dx.doi.org/10.1089/cyber.2017.0384
Tadesse, M. M., Lin, H., Xu, B., & Yang, L. (2019). Detection of depression-related posts in reddit social media forum. Ieee Access, 7, 44883-44893.
https://doi.org/10.1109/ACCESS.2019.2909180
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature medicine, 25(1), 44-56. https://pubmed.ncbi.nlm.nih.gov/30617339/
Youyou, W., Kosinski, M., & Stillwell, D. (2015). Computer-based personality judgments are more accurate than those made by humans. Proceedings of the National Academy of Sciences, 112(4), 1036-1040. https://doi.org/10.1073/pnas.1418680112
Derechos de autor 2024 Hernan Isaac Ocana Flores, Andrea Luna

Esta obra está bajo licencia internacional Creative Commons Reconocimiento 4.0.