Critical Challenges in Large Language Models and Data Analytics: Trustworthiness, Scalability, and Societal Impact

Dear Colleagues,

The rapid proliferation of large language models (LLMs) and advanced artificial intelligence (AI) systems has fundamentally transformed the landscape of data analytics, creating unprecedented opportunities alongside critical challenges that demand immediate scholarly attention. This Special Issue addresses the most pressing contemporary issues at the intersection of artificial intelligence, data science and analytics, and societal impact, focusing on three interconnected domains of critical importance.

• Trustworthiness and Reliability represents our first major theme, examining the urgent need for robust evaluation frameworks that can assess LLM performance beyond traditional benchmarks. We seek contributions that address hallucination detection and mitigation, bias quantification across diverse populations, uncertainty quantification in model outputs, and the development of explainable AI techniques that can provide meaningful insights into model decision-making processes. The challenge of ensuring consistent performance across different domains, languages, and cultural contexts remains a fundamental barrier to widespread deployment.
• Data Quality and Governance forms our second focus area, recognizing that the unprecedented scale of training data for modern LLMs introduces novel challenges in data curation, privacy preservation, and intellectual property considerations. We welcome research on automated data quality assessment techniques, privacy-preserving training methodologies, federated learning approaches for sensitive datasets, and frameworks for managing the complex ethical and legal implications of large-scale data utilization.
• Scalability and Environmental Impact constitutes our third pillar, addressing the growing computational demands of increasingly sophisticated models and their environmental consequences. We encourage submissions that explore energy-efficient training algorithms, model compression techniques that maintain performance while reducing resource requirements, distributed computing strategies for democratized AI access, and comprehensive lifecycle assessments of AI systems from development through deployment.

This Special Issue aims to present recent advancements while providing practical frameworks for researchers, practitioners, and policymakers navigating these complex relationships.

We particularly encourage interdisciplinary submissions that bridge AI, data science and analytics, ethics, policy, and domain-specific applications.

The scope of this Special Issue includes, but is not limited to, the following topics:

• Healthcare and Biomedical Analytics
• Financial Technology and Risk Analytics
• Educational Technology and Learning Analytics
• Natural Language Processing and Computational Linguistics
• Environmental and Climate Analytics
• Social Media and Digital Humanities
• Industrial and Manufacturing Analytics
• Legal Technology and Compliance

Dr. Oluwaseun Ajao
Dr. Bayode Ogunleye
Dr. Hemlata Sharma
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Analytics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• large language models (LLMs)
• data analytics
• trustworthiness
• data quality
• explainable AI
• bias and fairness
• privacy-preserving AI
• scalability
• model compression
• environmental impact
• ethical AI
• data governance