This paper assesses Artificial Intelligences in the development of psychological profiles where it has

possible development of AI in psychological profiling, focusing on possible interdisciplinary

cooperation and the importance of ethical regulation for achieving the best result.

Keywords: artificial intelligence, psychological profiles, machine learning, mental health, data analysis

Este artículo explora estas cuestiones, las ventajas y la eficiencia de emplear soluciones de IA en la

adicionales que podrían superar las deficiencias actuales. En esta revisión bibliográfica y estudio

sistemático, el objetivo del autor es esclarecer las tendencias actuales y el posible desarrollo de la IA

en el perfil psicológico, centrándose en la posible cooperación interdisciplinaria y la importancia de la

regulación ética para lograr mejores resultados.

Palabras clave: inteligencia artificial, perfiles psicológicos, machine learning, salud mental, análisis de

datos

Crossing the field of Artificial Intelligence with the field of psychology has been a breakthrough for

studying humanity. With improved advancements in the area of AI the development of psychological

models has been made easier so they are used in numerous cases from clinical assessments for patients

artificial intelligence is being used in developing psychological profiles as well as the milestones, the

drawbacks, and the prospective future concerning this research line.

psychological profiling. Previous approaches to psychological assessment had been done and for all

their functionality, have been predominantly employed to have certain drawbacks that are connected to

the time it takes for the assessment. This as well as to the fact that they are administered by people who

different types of data (textual and natural/organic), coming from social networks or related to the

of the person. For example, in their studies, Reece et al., (2017) and Settanni et al. (2018) & O’Connor

(2016) have designed classifiers for the detection of the huge signs of depression and anxiety and social

media activities to take early interventions.

Through better computational and AI-related tools, psychologists and psychiatrists are now employing

them with unprecedented success to diagnose and develop treatment plans for patients (Esteva et al.,

2017; Topol, 2019).

While there has been progress in conflict resolution and the enhancement of Artificial Intelligence in

psychological profiling, this comes with challenges.

functioning and its capabilities with regards to analyzing relevant personal data. It will be essential to

consider such values as privacy, consent, and data security primarily based on the type of information

related to psychology we are dealing with. It is therefore up to the researchers and practitioners to

interact with these categories for ethical considerations in order to avoid negative consequences of

failing to do so (Floridi et al., 2018). Furthermore, the specificity and certainty of AI models depend on

the quantity and variety of data that has been inputted into the system. For instance, data elicitation can

result in a prejudiced outcome, which is unadvisable in a psychological setup (Mehrabi et al., 2022;

Obermeyer et al., 2019).

The final contentious topic is the explanatory AI models. For psychological applications of AI these

aspects may be useful, but the main strength of AI relays on the accuracy of predictions, and in the

disorders. Therefore, the individualized treatments, which are oriented on the specific needs of clients,

systems can use psychological tests and questionnaires to recognize a client’s psychological

characteristics, suggest treatments, and track clients’ changes to modify the treatment accordingly at

once. Through such interaction and real-time monitoring, it delivers improved mental health treatments

(Guntuku, et al., 2019; Topol, 2019).

between one and the other and find patterns of behavior that are not easily recognizable. For instance,

which require attention (De Choudhury et al., 2013).

health with the use of AI technologies problems are tackled before they reach a critical level.

However, is the effective use of AI in psychological profiling that need to be worked on with the

assistance of IT professionals in conjunction with mental health professionals. This would allow

interdisciplinary teams to design AI tools based on theoretical knowledge and implementation of

psychological concepts and protocols. It can also assist in resolving the ethical and practical questions

related to the use of AI solutions in psychology and making sure that the usage of AI instruments in

psychology is both potential and moral.

This paper aims to address the following research question: What are the most accurate methods that

can be employed to assess psychologically correctness and ethical personal profiles? In providing an

answer to this question, the paper will briefly look at the modern developments in AI technology for

The rise of social media platforms has brought in a new era of digital interaction, providing

as a potent tool for predicting psychological traits and health outcomes. For instance, Eichstaedt et al.,

(2015) demonstrated that psychological language used on Twitter could predict county-level heart

disease mortality, showcasing the potential of social media data to reflect broader health trends. These

profiles are very helpful for explaining the individuals’ behaviour, for diagnosing mental disease, and

for creating tailored prevention and treatment plans. Some of the trends had been shown as follows; the

realization of these developments has been aided by essential emerging AI techniques like machine

learning and deep learning (Russell & Norvig, 2021).

The relevance of AI over the history, the fundamental approaches of AI, the uses of AI, the pros and

attributes from digital signatures of behaviours, thus the firms get an opportunity to forecast people’s

personalities from the activity demonstrated on social media. Another study which further elaborated

on this idea was conducted by Schwartz et al., (2013) to analyses personality, gender, and age relying

on language used on social media through an open-vocabulary; authors have found complex

correlations between language used and several personality traits.

Based on this, Park et al., (2015) established techniques for the efficient identification of personalities

from the language used in the social media accounts with excellent convergence when compared with

the conventional personality tests. In like manner, Youyou, Kosinski, and Stillwell (2015) noted that,

as compared to the human judgment, computer-based personality judgments were more accurate, as

this stressed on the exactness of digital assessment.

could be affected by interaction with an agent; results showed that interaction with computerized agents

the user personality and websites choice and activity on online social networks; thus, strengthening the

association of psychological characteristics with the digital behaviour.

The motives that underpinned blogging and how these are linked to personality were studied by Gill,

Nowson, and Oberlander (2009) whose work showed Blog post content and authors’ personality were

correlated.

Building on this line of investigation, Schwartz et al. (2013) examined personality implications from

the language and proposed that it was possible to gain significant psychological understanding. Kern et

al., (2014) used an open vocabulary approach of social media self and observed that the language used

account.

Thus, opening a view to one of the most prominent areas that has been explored: detection of mental

health through social networks. In their systematic paper on detecting depression and mental illness on

the social media sites, Guntuku et al, (2017) urged the possibly of using the social media for spying or

monitoring of mental health. De Choudhury, Counts & Horvitz (2013) employed a qualitative

observation of the postpartum changes in feelings and behaviours using sites, and therefore making a

claim for how data collected digitally can be instrumental in tracking, more so changes on the

psychosocial aspect.

Similarly, the detection of mental health issues using features from SM has also been a popular area of

study. Guntuku et al., (2017) shared an integrative review of experiments on detecting Depression and

on Reddit, which proved the multifunctionality of social networks in psychological research.

health conditions describe their state on social media, including Twitter. Last but not least, Mowery et

al., (2017) also took a corpus-based approach to investigate the use of Twitter for exploring depressive

symptoms and psychosocial stressors towards embracing the relationship between SML and users’

psychological situation.

Modern machine learning technique used in healthcare is deep learning and Esteva et al. (2017)

underscored the electric application in mental health. They indicated that DL enables the analysis of an

e-record, medical image, etc., to determine the patient’s mental condition They pointed out that such

models may offer a better decision to clinicians and a treatment plan according to the patient’s condition

deep learning has a great extent of value in psychological analysis.

The overall approach used in this study is the systematic review method since it provides the framework

for the review of the literature focusing on the use of AI in constructing psychological profiles. Unlike

a primary study, this study will not aim at producing new data instead it will collect and synthesize data

that has been produced in previous research studies with a comparison of result and conclusions.

This study proposes a Systematic review and Meta-Analysis for studies in which the use of AI

technologies had been tested in real world applications. This approach is made with the aim to integrate

various existing methods, passing key conclusions and searching for further gaps; therefore, this

provided in the papers.

The author of this paper conducted a keyword search in the academic databases like SCOPUS, PubMed,

IEEE Xplore, and Google Scholar to gather the studies. The search terms include: Artificial intelligence

With the use of search engines search different academic databases such as Scopus, PubMed, IEEE

Explores, and Google Scholar was performed. The keywords used were AI in psychological profiling,

artificial intelligence for mental health diagnosis, deep learning in mental health assessment, and

comprehending the major patterns and features of human character for enhancing the psychological

results and employing more effective interventions. AI’s role in psychological profiling continues

evolving, and presents a lot of possibilities in the future to augment and transform the perception,

diagnosis and management of psychological features.

Ultimately, along with the further advancement of AI technologies, these applications will gradually be

introduced into different spheres of our lives, including such spheres as health care and mental health

services, as well as education and employment. The constant improvement of the models used in AI

and the increase of the number of available data sources will further improve the accuracy and the areas

of operation of psychological profiling thus leading to more efficient solutions in various fields. This

ever-changing context thus heralds the vision of how psychological analytics birthed from the realm of

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