Advancing Research in AI & Data Privacy

The Institute of AI and Data Privacy is dedicated to advancing independent, interdisciplinary research on the ethical, legal, and social implications of emerging technologies. Our research program explores how artificial intelligence, data governance, and automation affect privacy, human rights, and democratic accountability.

By combining empirical analysis with normative inquiry, the Institute seeks to inform global debates on responsible innovation. Our work bridges the gap between computer science, law, and public policy – promoting an evidence-based approach to AI governance and data protection.

Research Areas

Artificial Intelligence

We study the foundations and frontiers of artificial intelligence, exploring its technical development and its broader impact on how humans create and use information.

Data Privacy & Security

We study how data is collected, stored, and protected across digital systems, exploring methods and frameworks that preserve individual privacy while maintaining the integrity and security of information in an interconnected world.

Societal Impact

We examine how emerging technologies influence social structures, labor, education, and governance – analyzing both the opportunities and disruptions created by AI and data-driven systems in everyday life.

Law & Compliance

We research the legal and regulatory frameworks that govern artificial intelligence and data use, focusing on how evolving laws, standards, and compliance systems can ensure accountability, transparency, and the protection of fundamental rights.

Sustainability

We explore how artificial intelligence and data technologies can advance environmental sustainability – studying their potential to optimize resource use, reduce waste, and support long-term ecological and social resilience.

Human Rights

We investigate how technological innovation intersects with fundamental rights and freedoms, ensuring that the development and deployment of AI and data systems uphold human dignity, equality, and justice.

Published Research

Web Security and Compliance Guidelines 2026

In the digital landscape of 2026, web security and regulatory compliance are no longer peripheral IT concerns; they are foundational to business integrity and market competitiveness. This white paper provides a comprehensive overview of the primary security frameworks—including SOC 2, HIPAA, GDPR, ISO 27001, and SOX—and outlines strategic imperatives for organizations to protect sensitive data, mitigate risk, and foster stakeholder trust in a cloud-native environment.

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Current Research & Working Papers

A Systematic Review of Artificial Intelligence Adoption and Applications Across Academic and Industrial Domains

This study reviews current academic and industry research to map how artificial intelligence is being applied across disciplines and sectors. By analyzing emerging trends in fields such as healthcare, finance, education, and law, the paper identifies common use cases, methodological approaches, and areas of rapid growth. The goal is to provide a comprehensive overview of where AI is creating measurable impact and how its adoption patterns shape future research and policy directions.

Evaluating the Computational Accuracy of ChatGPT: Capabilities, Limitations, and Misapplications

This study examines how large language models (LLMs) handle mathematical and quantitative reasoning tasks, despite not being explicitly designed for computation. By systematically testing model performance across a range of calculation types and complexities, the research assesses the reliability and error patterns of LLMs when used outside their intended linguistic scope. The findings aim to clarify the boundaries between linguistic generation and numerical reasoning in AI systems.

The Energy Cost of Intelligence: A Systematic Review of Power Consumption in Large Language Models

This systematic review investigates the energy requirements associated with training and operating large language models such as ChatGPT. By analyzing recent empirical studies and benchmarking reports, the research synthesizes current knowledge on computational energy use, carbon impact, and efficiency trade-offs in model scaling. The goal is to contextualize AI’s environmental footprint and highlight emerging strategies for sustainable model development and deployment.