Computer Language Research Articles

Billy-Buddy against Cyberbullying

Cyberbullying has become a pervasive issue in the digital age, affecting individuals across demographics and causing significant psychological, emotional, and social harm. Despite various attempts to address this problem, existing solutions often fall short in providing comprehensive support, particularly in victim care, privacy preservation, and real-time intervention. This paper presents a holistic framework that integrates cutting-edge technologies such as Natural Language Processing (NLP), machine learning, and secure data handling to combat cyberbullying effectively. At the core of this framework is an empathetic AI-powered chatbot, “Billy,” designed to provide victims with real-time emotional support and actionable guidance. Billy uses advanced sentiment analysis to detect distress and offers tailored responses, helping victims navigate the emotional and procedural aspects of cyberbullying incidents. Additionally, the system facilitates anonymous reporting of perpetrators, ensuring victims’ privacy and safety through robust encryption and secure data management. The proposed framework includes a real-time cyberbullying detection mechanism capable of analyzing online interactions, identifying harmful content, and providing immediate feedback. Statistical tools analyze incident data to identify high-risk regions, enabling law enforcement to prioritize resources effectively. The system also incorporates educational initiatives to raise awareness about cyberbullying, promote safe online practices, and encourage proactive prevention.

Ethical frameworks for AI in journalism: Balancing technological innovation and journalistic integrity

This study examined the moral dilemmas and ramifications of incorporating artificial intelligence (AI) into journalism, a discipline that is rapidly changing due to technology. Natural language processing and machine learning are two examples of AI technologies that have greatly improved journalistic practices by automating repetitive activities, enhancing data analysis, and opening up new narrative styles. But there are serious ethical challenges with this integration, such as algorithmic bias, editorial control loss, and transparency problems. The study used a qualitative research approach and evaluated case studies of AI applications in journalism using content analysis and ethical principles from top companies. The results demonstrated AI's dual effects on journalism. Although AI technologies streamline operations and increase accessibility, they also run the danger of strengthening social prejudices, undermining journalistic standards, and decreasing human supervision in editorial procedures. The New York Times and the BBC are two well-known examples of how to balance AI innovation with journalistic responsibility through openness and human monitoring. Disparities in acceptance around the globe and the absence of legally binding, situation-specific ethical frameworks, however, continue to be major obstacles. According to the study, integrating AI into journalism should be guided by a thorough ethical framework that prioritizes accountability, openness, diversity, and justice. Newsrooms and developers must work together to ensure ethical compliance, according to implementation rules, and continuous adaptation supported by dynamic assessment tools like audience feedback and ethical audits. In addition to tackling ethical challenges, this framework aims to uphold fundamental journalistic principles while utilizing AI's revolutionary potential, providing useful advice for encouraging creativity and trust in the changing media environment.

The spectrum of synthetic data generation: A comprehensive review

Synthetic data, which is the data produced to mimic the characteristics of actual data without revealing any confidential information, is a much safer option than original data, especially when it comes to extreme instances such as personal data, financial data, or military intelligence. There are substantial dangers connected with the use of real-life data such as assault with the intent to commit identity theft, fraud, and hacking, but because synthetic data (SD) reproduces some of the elements of real data, without infringing on anyone’s privacy, suffers from these risks. The project concentrates on the cutting-edge fields of Language Learning Models (LLM) and Deep Learning (DL) to generate synthetic data that mimics real-world data in its intricacy. Advances in LSTM networks and Generative Adversarial Networks (GAN) produce plausible and useful data in sequence forms for natural language processing and machine learning (ML) augmentation respectively. Applications of this technology include, but are not limited to, the use of augmented datasets to improve medical diagnosis, advanced finance fraud detection systems, and designing fictitious consumers in order to enhance AI-based system recommendations. The project which is implemented with Python programming language and also takes advantage of some open source packages such as SymPy, Pydbgen, Synthetic Data Vault (SDV), and Scikit-learn offers a solution to data scarcity and quality problems in order to improve the performance of the AI models in various sectors.

Viruses and Evolution

The role of viruses within Big History is an important field in understanding the process of evolution; however, it is not yet extensively studied. This is due both to the fact that viruses, being microscopic pathogenic, have left limited physical traces in the geological record, and because there is no firm agreement on the place of viruses in evolution. The purpose of this report is to explore the role of viruses in the context of evolutionary development within the framework of Big History. It aims to provide an overview of current perspectives and challenges associated with understanding the position of viruses in the broader narrative of evolutionary history. According to a dictionary, a virus is defined as ‘any of a large group of submicroscopic infectious agents that are usually regarded as non-living extremely complex molecules. The term ‘agents’ is commonly used in virology due to the difficulty in finding a universal term to describe these unique entities. A virus is not an organism, in our usual sense of the definition, nor is it a chemical compound since viruses have an overly complex structure and behavior that sets them apart. In the field of evolution, viruses pose a perplexing puzzle piece that does not neatly fit into the larger picture. Undeniably, viruses are the most pervasive entities on Earth. However, despite their ubiquitous presence, only a little over 6,000 virus species have been comprehensively described in the existing body of knowledge. In the paper, the author observes evolution and their origins, including regressive, protobiont hypotheses, as well as the symbiotic theory, which according to some researchers was the factor that led to the three cellular domains of life that we now know. The paper also delves into a fascinating aspect of evolution: the unique position of viruses, which exist on the edge of disorder, displaying high instability yet remarkable adaptability. This raises the question of whether viruses can be considered alive or not, leading to an intriguing debate. We also explore the question of whether viruses can be classified as parasites, which may seem simple at first glance but is actually a complex and contradictory topic. The author discusses the role of viruses from an energetic perspective, recognizing their significance in information and energy dynamics within ecosystems. Viruses exert a substantial influence on energy flows in intricate ecological systems. The paper considers viruses from the perspective of information evolution, because in some respects viruses can be regarded as pure information particles. The author also argues that viruses can be considered as basic information agents that transmit genetic information and are thus the basic molecular tool of evolution. Moreover, this tool is beginning to be used by humans for the genetic alteration of many organisms, including ourselves. It is important that viruses will no longer be considered only as dangerous pathogens, since pathogenic viruses make up only a small part of their genus. We argue that breakthroughs in virology in the coming decades could be no less important as a breakthrough in understanding the nature of bacteria, both pathogenic and beneficial. The paper also examines viruses as a complex self-regulating system drawing comparisons to other complex systems such as computer viruses, cancer, and languages. It emphasizes the remarkable similarities between these systems and explores possible explanations for these similarities.

Recognition model for major depressive disorder in Arabic user-generated content

BackgroundOne of the psychological problems that have become very prevalent in the modern world is depression, where mental health disorders have become very common. Depression, as reported by the WHO, is the second-largest factor in the worldwide burden of illnesses. As these issues grow, social media has become a tremendous platform for people to express themselves. A user’s social media behavior may therefore disclose a lot about their emotional state and mental health. This research offers a novel framework for depression detection from Arabic textual data utilizing deep learning (DL), natural language processing (NLP), machine learning (ML), and BERT transformers techniques in light of the disease’s high prevalence. To do this, a dataset of tweets was used, which was collected from 3 sources, as we mention later. The dataset was constructed in two variants, one with binary classification and the other with multi-classification.ResultsIn binary classifications, we used ML techniques such as “support vector machine (SVM), random forest (RF), logistic regression (LR), and Gaussian naive Bayes (GNB),” and used BERT transformers “ARABERT.” In comparison ML with BERT transformers, ARABERT has high accuracy in binary classification with a 93.03 percent accuracy rate. In multi-classification, we used DL techniques such as “long short-term memory (LSTM),” and used BERT transformers “Multilingual BERT.” In comparison DL with BERT transformers, multilingual has high accuracy in multi-classification with an accuracy of 97.8%.ConclusionThrough user-generated content, we can detect depressed people using artificial intelligence technology in a fast manner and with high accuracy instead of medical technology.

Corporate sponsorship as cultural policy: tax incentives in Brazilian contemporary art

ABSTRACT Unprecedented corporate intervention and the withdrawal of government support from the arts and culture characterized the 1980s. Driven by the Thatcher and Reagan administrations, which promoted corporate patronage and art sponsorship, this trend shaped cultural policies worldwide and was adopted by numerous countries. Following this global movement, Brazil introduced tax incentives in the early 1990s to encourage private companies to fund cultural events and institutions. While the literature on Brazilian cultural tax incentives is extensive, researchers have overlooked the connection between these incentives and the country’s artistic production fields. Drawing on data from the past decade of cultural funding and applying Natural Language Processing and machine learning techniques to isolate sponsorship patterns related to contemporary art, this paper examines the financial landscape of Brazil’s contemporary art circuit. The findings reveal that inequalities in resource distribution for contemporary art sponsorship exceed those in other fields, such as music and theater, highlighting the highly institutionalized nature of contemporary art, its reliance on corporate sponsorship, and the substantial costs associated with its production.

AfriSign: African sign languages machine translation

Research on sign language translation is ongoing with a high social inclusive goal of crossing the bridge between people with hearing disability using sign language as their basic way to communicate to others who do not understand sign language. Hundreds of different sign languages exist instead of a single universal sign language. Research on translating sign languages from high-income nations has grown significantly, but little is known about translating sign languages from Africa. In this paper, we curate a novel video-to-text African sign languages translation dataset containing sign language videos of Bible verses from six (6) different African countries. We experimented with competitive machine translation and sign language translation techniques on our dataset, including the application of transformers to sign language translation, multilingual training, and cross-transfer learning. We evaluated them in terms of accuracy and precision. The results from our experiments prove that having one Multilingual model for all the languages tends to be a better choice when deployed in real system in terms of memory usage with an accuracy of 94.6% and precision of 97.3%. These results give headway for more multilingual models to be developed to enhance inclusion for the deaf community and bridge the gap between the hearing and the deaf in Africa.

P1241 Identification and characterization of patients with Crohn's disease and complex perianal fistulas using Natural Language Processing and machine learning: INTUITION-CPF Study.

Abstract Background Crohn’s disease (CD) is an inflammatory condition that can cause perianal fistulas (mostly complex), affecting 26-35% of CD patients. There is no gold-standard treatment for perianal disease, leading to significant unmet needs. This study uses Artificial Intelligence (AI) to analyze electronic medical records (EMRs) to identify and characterize CD patients with complex perianal fistulas (CPF). It offers an overview of patient characteristics, treatment patterns, and CPF incidence in CD patients attending inflammatory bowel disease units in Spain. Methods INTUITION-CPF is a national, multicenter, retrospective study. EMRs of patients with CD, diagnosed or not with CPF (overall CD population) from January 2015-December 2021, were reviewed and processed using an AI analysis based on machine learning and Natural Language Processing (NLP). There were two phases in the study. Phase 1: NLP-identified patients with CD+CPF were manually validated to assess sensitivity and specificity; and Phase 2: characteristics, treatments and visits to departments were analysed in eligible patients with CD+/-CPF. Here, we present results from the phase 2. Results A total of 2,182 patients with CD were identified. Median age (range) was 51 years (21-105). 263 patients (12%) suffered from CPF, of which 70% experienced pain, 48% active (suppurative) disease, and 28% perianal abscesses. The estimated annual incidence of CPF among CD patients was 1.6%. Patients with CD+CPF had more medical treatments (91%) compared to overall CD patients (74%). Proportion of patients receiving biologics was near double for CD+CPF compared to overall CD patients (41% vs 21%) with infliximab the most frequently used (26%) (Table 1). 13% of the overall CD population had ≥1 surgery, compared with 36% of patients with CD+CPF. The main surgeries in patients with CD+CPF included seton placement (14%), advancement flap surgery (FLAP) (16%) and ostomy (13%) (Table 1). Hospitalisation (≥24h) was required for 49% of overall CD patients vs 64% of those with CPF. Gastroenterology (73%) was the most frequently visited department, followed by surgery (31%) and emergency departments (27%) (Figure 1). Conclusion This study demonstrates that CD patients with CPF require more complex and multidisciplinary management, including more biological treatments and surgeries, and exhibit higher hospitalization and emergency visit rates compared to overall CD patients.Inconsistencies with the existing literature highlight the opportunity to further enhance NLP techniques to achieve more accurate outcomes.

Computational Methods for Information Processing from Natural Language Complaint Processes—A Systematic Review

Complaint processing is of great importance for companies because it allows them to understand customer satisfaction levels, which is crucial for business success. It allows them to show the real perceptions of users and thus visualize the problems, which are regularly processed from oral or written natural language, derived from the provision of a service. In addition, the treatment of complaints is relevant because according to the laws of each country, companies have the obligation to respond to these complaints in a specified time. The specialized literature mentions that enterprises lost USD 75 billion due to poor customer service, highlighting that companies need to know and understand customer perceptions, especially emotions, and product reviews to gain insight and learn about customer feedback because of the importance of the voice of the customer for an organization. In general, it is evident that there is a need for research related to computational language processing to handle user requests. The authors show great interest in computational techniques for the processing of this information in natural language and how this could contribute to the improvement of processes within the productive sector. This work searches in indexed journals for information related to computational methods for processing relevant data from user complaints. It is proposed to apply a systematic literature review (SLR) method combining literature review guides by Kitchenham and the PRISMA statement. The systematic process allows the extraction of consistent information, and after applying it, 27 articles were obtained from which the analysis was conducted. The results show various proposals using linguistic, statistical, machine learning, and hybrid methods. We find that most authors combine Natural Language Processing (NLP) and Machine Learning (ML) to create hybrid methods. The methods extract relevant information from complaints of the customers in natural language in various domains, such as government, medical, banks, e-commerce, public services, agriculture, customer service, environmental, and tourism, among others. This work contributes as support for the creation of new systems that can give companies a significant competitive advantage due to their ability to reduce the response time of the complaints as established by law.

Psychological and Behavioral Insights From Social Media Users: Natural Language Processing-Based Quantitative Study on Mental Well-Being.

Depression significantly impacts an individual's thoughts, emotions, behaviors, and moods; this prevalent mental health condition affects millions globally. Traditional approaches to detecting and treating depression rely on questionnaires and personal interviews, which can be time consuming and potentially inefficient. As social media has permanently shifted the pattern of our daily communications, social media postings can offer new perspectives in understanding mental illness in individuals because they provide an unbiased exploration of their language use and behavioral patterns. This study aimed to develop and evaluate a methodological language framework that integrates psychological patterns, contextual information, and social interactions using natural language processing and machine learning techniques. The goal was to enhance intelligent decision-making for detecting depression at the user level. We extracted language patterns via natural language processing approaches that facilitate understanding contextual and psychological factors, such as affective patterns and personality traits linked with depression. Then, we extracted social interaction influence features. The resultant social interaction influence that users have within their online social group is derived based on users' emotions, psychological states, and context of communication extracted from status updates and the social network structure. We empirically evaluated the effectiveness of our framework by applying machine learning models to detect depression, reporting accuracy, recall, precision, and F1-score using social media status updates from 1047 users along with their associated depression diagnosis questionnaire scores. These datasets also include user postings, network connections, and personality responses. The proposed framework demonstrates accurate and effective detection of depression, improving performance compared to traditional baselines with an average improvement of 6% in accuracy and 10% in F1-score. It also shows competitive performance relative to state-of-the-art models. The inclusion of social interaction features demonstrates strong performance. By using all influence features (affective influence features, contextual influence features, and personality influence features), the model achieved an accuracy of 77% and a precision of 80%. Using affective features and affective influence features also showed strong performance, achieving 81% precision and an F1-score of 79%. The developed framework offers practical applications, such as accelerating hospital diagnoses, improving prediction accuracy, facilitating timely referrals, and providing actionable insights for early interventions in mental health treatment plans.

Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.

Natural language processing (NLP) and machine learning (ML) techniques may help harness unstructured free-text electronic health record (EHR) data to detect adverse drug events (ADEs) and thus improve pharmacovigilance. However, evidence of their real-world effectiveness remains unclear. To summarise the evidence on the effectiveness of NLP/ML in detecting ADEs from unstructured EHR data and ultimately improve pharmacovigilance in comparison to other data sources. A scoping review was conducted by searching six databases in July 2023. Studies leveraging NLP/ML to identify ADEs from EHR were included. Titles/abstracts were screened by two independent researchers as were full-text articles. Data extraction was conducted by one researcher and checked by another. A narrative synthesis summarises the research techniques, ADEs analysed, model performance and pharmacovigilance impacts. Seven studies met the inclusion criteria covering a wide range of ADEs and medications. The utilisation of rule-based NLP, statistical models, and deep learning approaches was observed. Natural language processing/ML techniques with unstructured data improved the detection of under-reported adverse events and safety signals. However, substantial variability was noted in the techniques and evaluation methods employed across the different studies and limitations exist in integrating the findings into practice. Natural language processing (NLP) and machine learning (ML) have promising possibilities in extracting valuable insights with regard to pharmacovigilance from unstructured EHR data. These approaches have demonstrated proficiency in identifying specific adverse events and uncovering previously unknown safety signals that would not have been apparent through structured data alone. Nevertheless, challenges such as the absence of standardised methodologies and validation criteria obstruct the widespread adoption of NLP/ML for pharmacovigilance leveraging of unstructured EHR data.

Early Identification of Language Disorders Using Natural Language Processing and Machine Learning: Challenges and Emerging Approaches.

Recent advances in artificial intelligence provide opportunities to capture and represent complex features of human language in a more automated manner, offering potential means of improving the efficiency of language assessment. This review article presents computerized approaches for the analysis of narrative language and identification of language disorders in children. We first describe the current barriers to clinicians' use of language sample analysis, narrative language sampling approaches, and the data processing stages that precede analysis. We then present recent studies demonstrating the automated extraction of linguistic features and identification of developmental language disorder using natural language processing and machine learning. We explain how these tools operate and emphasize how the decisions made in construction impact their performance in important ways, especially in the analysis of child language samples. We conclude with a discussion of major challenges in the field with respect to bias, access, and generalizability across settings and applications. Given the progress that has occurred over the last decade, computer-automated approaches offer a promising opportunity to improve the efficiency and accessibility of language sample analysis and expedite the diagnosis and treatment of language disorders in children.

How the technologies behind self-driving cars, social networks, ChatGPT, and DALL-E2 are changing structural biology.

The performance of deep Neural Networks (NNs) in the text (ChatGPT) and image (DALL-E2) domains has attracted worldwide attention. Convolutional NNs (CNNs), Large Language Models (LLMs), Denoising Diffusion Probabilistic Models (DDPMs)/Noise Conditional Score Networks (NCSNs), and Graph NNs (GNNs) have impacted computer vision, language editing and translation, automated conversation, image generation, and social network management. Proteins can be viewed as texts written with the alphabet of amino acids, as images, or as graphs of interacting residues. Each of these perspectives suggests the use of tools from a different area of deep learning for protein structural biology. Here, I review how CNNs, LLMs, DDPMs/NCSNs, and GNNs have led to major advances in protein structure prediction, inverse folding, protein design, and small molecule design. This review is primarily intended as a deep learning primer for practicing experimental structural biologists. However, extensive references to the deep learning literature should also make it relevant to readers who have a background in machine learning, physics or statistics, and an interest in protein structural biology.

Leveraging artificial intelligence for predicting spontaneous closure of perimembranous ventricular septal defect in children: a multicentre, retrospective study in China.

Perimembranous ventricular septal defect (PMVSD) is a prevalent congenital heart disease, presenting challenges in predicting spontaneous closure, which is crucial for therapeutic decisions. Existing models mainly rely on structured echocardiographic parameters or restricted data. This study introduces an artificial intelligence (AI)-based model, which uses natural language processing (NLP) and machine learning with the aim of improving spontaneous closure predictability in PMVSD. We did a multicentre, retrospective analysis using data from 29 142 PMVSD patients across six tertiary centres in China from May, 2004, to September, 2022, for training (70%) and validation (30%; dataset 1, 27 269 patients), and from September, 2001, to December, 2009 for testing (dataset 2, 1873 patients). NLP extracted structured data from echocardiography reports and medical records, which were used to develop machine learning models. Models were evaluated for spontaneous closure occurrence and timing by use of area under the receiver operating characteristic curve (AUC), decision curve analysis, and calibration index. Spontaneous closure occurred in 3520 patients (12·1%) at a median of 31 months (IQR 16-56). Eleven NLP-derived predictors, identified via least absolute shrinkage and selection operator, highlighted the importance of defect morphology and patient age. The random survival forest algorithm, selected for its superior concordance indexes, showed excellent predictive performance with validation set AUCs (95% CI) of 0·95 (0·94-0·96) for 1-year and 3-year predictions, and 0·95 (0·95-0·96) for 5-year predictions; testing set AUCs were 0·95 (0·94-0·97) for 1-year predictions, 0·97 (0·96-0·98) for 3-year predictions, and 0·98 (0·97-0·99) for 5-year predictions. The model showed high clinical utility through decision curve analysis, calibration, and risk stratification, maintaining consistent accuracy across centres and subgroups. This AI-based model for predicting spontaneous closure in PMVSD patients represents a substantial advancement, potentially improving patient management, reducing risks of delayed or inappropriate treatment, and enhancing clinical outcomes. National Natural Science Foundation of China, Shanghai Municipal Hospital Clinical Technology Project, Shanghai Municipal Health Commission, and Clinical Research Unit of XinHua Hospital.

Analysis of Content Consistency in Scientific Journal Based on Natural Language Processing and Machine Learning

Analysis of Content Consistency in Scientific Journal Based on Natural Language Processing and Machine Learning

Lanthanum-based metal-organic framework nanorods for removal of dye pollutants with machine language predictions

Lanthanum-based metal-organic framework nanorods for removal of dye pollutants with machine language predictions

Online reprogramming electronic bits for N dimension fractal soft deformable structures.

Inspired by the complex fractal morphologies and deformations observed in animals and plants, an N-dimensional soft structure composed of stretchable electronic bits has been developed. This soft structure, capable of independent and cooperative motion, can be manipulated through the programming of bits using a machine language based on instruction encoding. This method simplifies the process of changing the bit's step temperature to control its binary state. Theoretical analysis demonstrates that the fractal dimensions and deformation morphologies of the soft structure achieve stability and extremity when the total number of programming bits exceeds eighteen. Considering strip-shaped soft structures as a case study, their ultimate deformation morphologies, covering the reachable regions of all bits, can achieve complexity comparable to that of dandelion tufts and tree crowns. Moreover, the deformation process exhibits agility akin to that of an octopus. We have prepared samples that include strip-shaped soft structures, each containing multiple pairs of bits, and a hand-shaped soft structure equipped with five pairs of bits, intended for conducting deformation programming experiments. These experimental results validated the correctness of the online reprogramming method for soft structures, showing their capability to perform a range of complex deformations, such as the "OK" gesture, and highlighting potential applications in surgical contexts. This design strategy contributes to the development of soft structures, offering contributions from both theoretical and practical perspectives.

The Role of Artificial Intelligence in Analyzing Narrative Structures in English Novels

Artificial Intelligence (AI) is transforming the analysis of narrative structures in literature, offering computational methods that complement traditional approaches. By leveraging tools such as Natural Language Processing (NLP), sentiment analysis, and machine learning, AI enables researchers to uncover patterns, themes, and character relationships in English novels at an unprecedented scale. This study investigates AI's role in literary analysis, emphasizing its ability to explore narrative structures while addressing algorithmic bias, reductionism, and ethical considerations. Using case studies from Pride and Prejudice, 1984, and Great Expectations, it demonstrates AI's transformative potential and proposes strategies for integrating computational tools with traditional literary methodologies

Transforming Contract Management: A Comprehensive Analysis of AI Integration in Legacy and Ongoing Contracts

Artificial intelligence (AI) and contract intelligence offer a revolutionary solution for contemporary contract administration, addressing the unique difficulties presented by both legacy and continuous contracts. This technical article investigates how automated data extraction, risk assessment, and process optimization driven by artificial intelligence transform contract management. Usually kept in conventional repositories, legacy contracts allow the system to retrieve and analyze important data in mass while including human confirmation for accuracy. By means of real-time AI processing for instantaneous analysis of third-party documentation, negotiation facilitation, and pre-signature risk evaluation, continuous contract management reduces the requirement for human review in the processing pipeline. While addressing implementation concerns, system integration issues, and future advancements in the field. This article investigates the technological underpinnings of Contract Intelligence—including Natural Language Processing and Machine Learning algorithms, including By means of thorough comparative research of heritage and contemporary contract management strategies, this article offers insightful information for companies trying to modernize their contract management systems while preserving compliance and operational effectiveness.

Implementing AI powered solutions for Regulatory Compliance in Digital Banking to Foster Financial Inclusion

The intersection of artificial intelligence (AI) and regulatory compliance in digital banking has ushered in transformative opportunities for financial inclusion. This paper examines how AI can streamline compliance processes, mitigate risks, and expand access to financial services for underserved populations. By analyzing key AI technologies like Natural Language Processing (NLP) and Machine Learning (ML), this work discusses their applications in regulatory frameworks and highlights their potential to foster inclusivity in the financial ecosystem. Additionally, ethical considerations, challenges, and best practices are explored to provide a holistic understanding of AI-driven compliance innovation.