Abstract
Various key concepts are introduced here, including artificial intelligence and explainable artificial intelligence and their implementation in artificial psychology in model building. We recommend the use of a training set to estimate models and a separate set of data to test, in an unbiased manner, the predictive validity of the model obtained from the training set. This leads us to the increasingly popular techniques of machine learning and deep learning, where the model updates and learns from the data and is able to explore relationships that were not explicitly fed into the model so it relies less on pre-programmed a priori assumptions. These differ from classical statistical inference, where only pre-defined relationships are included in the model. We allude to deductionism and inductivism and look at assumptions underlying these approaches including multicollinearity. A good model should be parsimionious, easy to interpret, explainable, and understandable. We refer to three stages in the fitting of a model: the pre-model stage, the intrinsic stage, and the post-model interpretability stage and to different types of machine learning models.
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References
Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160.
Aguinis, H., & Solarin, A. (2019). Transparency and replicability in qualitative research: The case of interviews with elite informants. Strategic Management, 40(8), 1291–1315. https://doi.org/10.1002/smj.3015
Aslam, N., Khan, I. U., Mirza, S., AlOwayed, A., Anis, F. M., Aljuaid, R. M., & Baageel, R. (2022). Interpretable machine learning models for malicious domains detection using explainable artificial intelligence (XAI). Sustainability, 14, 7375. https://doi.org/10.3390/su14127375
Borsboom, D., van der Maas, H. L., Dalege, J., Kievit, R. A., & Haig, B. D. (2021a). Theory construction methodology: A practical framework for building theories in psychology. Perspectives on Psychological Science, 16, 756–766.
Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., et al. (2021b). Network analysis of multivariate data in psychological science. Nature Reviews Methods Primers, 1(1), 58.
Buchanan, B. G. (2005). A (very) brief history of artificial intelligence. AI Magazine, 26(4), 53–53.
Crabbe, & Schaar. (2022). Proceedings of the 39th international conference on machine learning, PMLR 162, 2022.
Crowder, J., & Friess, S. (2010). Artificial neural emotions and emotional memory. Ic-Ai, 373–378.
Dua, D., & Graff, C. (2017) UCI machine learning repository. https://archive.ics.uci.edu/ml/index.php
Eronen, M. I., & Bringmann, L. F. (2021). Perspectives on Psychological Science, 16(4), 779–788. https://doi.org/10.1177/1745691620970586
Farahani, H. A., Kazemi, Z., Aghamohamadi, S., Bakhtiarvand, F., & Ansari, M. (2011). Examining mental health indices in students using Facebook in Iran. Procedia-Social and Behavioral Sciences, 28, 811–814.
Fielder, E., Von Zglinicki, T., & Jurk, D. (2017). The DNA damage response in neurons: Die by apoptosis or survive in a senescence-like state? Journal of Alzheimer’s Disease, 60(s1), S107–S131.
Gianfagna, L., & Di Cecco, A. (2021a). Model-agnostic methods for XAI. In Explainable AI with python (pp. 81–113). Springer.
Gianfagna, L., & Di Cecco, A. (2021b). Explainable AI with python (pp. 1–202). Springer.
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018, October). Explaining explanations: An overview of interpretability of machine learning. In 2018 IEEE 5th international conference on data science and advanced analytics (DSAA) (pp. 80–89). IEEE.
Grazioli, S., Rosi, E., Mauri, M., Crippa, A., Tizzoni, F., Tarabelloni, A., et al. (2021). Patterns of response to methylphenidate Administration in Children with ADHD: A personalized medicine approach through clustering analysis. Children, 8(11), 1008.
Jaganathan, K., Rehman, M. U., Tayara, H., & Chong, K. T. (2022). XML-CIMT: Explainable machine learning (XML) model for predicting chemical-induced mitochondrial toxicity. International Journal of Molecular Sciences, 23, 15655. https://doi.org/10.3390/ijms232415655
Lipkova, J., Chen, R. J., Chen, B., Lu, M. Y., Barbieri, M., Shao, D., et al. (2022). Artificial intelligence for multimodal data integration in oncology. Cancer Cell, 40(10), 1095–1110.
Markus, A. F., Kors, J. A., & Rijnbeek, P. R. (2021). The role of explain ability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies. Journal of Biomedical Informatics, 113, 103655.
Mischel, W. (2008). The toothbrush problem. APS Observer, 21.
Pratt, M. G., Lepisto, D., & Dane, E. (2019). The hidden side of trust: Supporting and sustaining leaps of faith among firefighters. Administrative Science Quarterly, 64, 398–434. ISI.
Rudin, S. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1, 206–215.
Vélez, J. I. (2021a). Machine learning psychology: Advocating for a data-driven approach. International Journal of Psychological Research, 14(1), 6–11. https://doi.org/10.21500/20112084.5365
Vélez, J. I. (2021b). Machine learning based psychology: Advocating for A data-driven approach. International Journal of Psychological Research, 14(1), 6–11.
Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., & Wang, C. (2018). Machine learning and deep learning methods for cybersecurity. IEEE Access, 6, 35365–35381.
Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100–1122.
Zhou, J., & Chen, F. (2018). Human and machine learning: Visible, explainable, trustworthy and transparent; human–computer interaction series. Springer; ISBN 978-3-319-90402-3.
Zhou, B., Sun, Y., Bau, D., & Torralba, A. (2018). Interpretable basis decomposition for visual explanation. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 119–134).
Zhou, J., Gandomi, A. H., Chen, F., & Holzinger. (2021). A. Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics, 10, 593. https://doi.org/10.3390/electronics10050593
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Farahani, H., Blagojević, M., Azadfallah, P., Watson, P., Esrafilian, F., Saljoughi, S. (2023). Artificial Psychology. In: An Introduction to Artificial Psychology. Springer, Cham. https://doi.org/10.1007/978-3-031-31172-7_2
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DOI: https://doi.org/10.1007/978-3-031-31172-7_2
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