Research Focus
Ethical & Trustworthy AI for Critical Environments
This work focuses on the design, evaluation, and deployment of AI systems that are fair, transparent, and accountable, particularly in high-impact sectors such as healthcare, cybersecurity, and finance.
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It bridges academic research and real-world consulting, supporting organizations and institutions to:
Apply ethical AI frameworks like Accountability, Responsibility, and Transparency (ART)​
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Identify and mitigate algorithmic bias​
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Ensure compliance with legal, regulatory, and social standards​
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Build human-centered AI systems that inspire trust
Across AI policy advisory, funded research collaboration, and the auditing of decision-making systems, an evidence-based and values-driven approach is applied to every project.
Dr. Vourganas
Designing and advising on ethical, explainable AI systems in healthcare, cybersecurity, and finance, where trust and accountability matter most.
Organizations adopt AI faster than they can govern, secure, or explain it. My work helps close that gap—turning AI innovation into operational, compliant, and trustworthy systems.​
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Ethical, transparent, and dependable AI solutions for environments where trust is non-negotiable.
Research in Action
Real-World Applications of Ethical AI.
This work is grounded in real-world impact. Ethical principles and AI governance models are applied to complex, high-stakes domains to ensure that AI systems are not only powerful, but also transparent, equitable, and aligned with human values.
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Below are examples of how responsible AI is translated into practice:
Cybersecurity & Defense
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Ethical AI for Strategic Defense Systems
Responsible AI supports autonomous threat detection, real-time decision support, and complex security operations within national security and critical-infrastructure environments. Emphasis is placed on explainability, human oversight, and accountability in high-risk operational contexts.
Healthcare
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Trustworthy AI for Clinical Environments
Ethical-by-design AI supports complex clinical decision-making in patient care and digital health systems, with a focus on transparency, safety, and alignment with medical and regulatory standards.
Finance & Risk
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Explainable AI for Regulated Industries
Explainable, auditable, and compliant AI supports decision-making across regulated financial contexts, from credit scoring to risk assessment, aligned with evolving ethical, legal, and supervisory expectations.
Why These Domains Matter Together:​
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Across defense, healthcare, and finance, one requirement is constant:
AI systems must be trustworthy. In these environments, errors are not merely inconvenient, they carry significant human, financial, and societal consequences.
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Working across high-stakes sectors enables a unified focus on ethical design, algorithmic accountability, and real-world resilience, applying AI principles where reliability, transparency, and oversight matter most.
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All of these efforts reflect a single mission: to bridge technical innovation with ethical responsibility, ensuring that AI systems not only perform effectively, but also serve the public good.
Ethical AI in Motion
From principles to prototypes: real-world applications of ethical AI
A real-time system for fairness audits, explainability, and compliance, designed to put trust back into intelligent decision-making.
Applications of Machine Learning in Cyber Security: A Review
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MDPI 2024
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This paper explores how AI is used in cybersecurity, and why poor data quality remains a key challenge. It reviews recent intrusion detection datasets, introduces ethical AI concerns in security, and outlines open research questions for the field.
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Responsible AI for Home-Based Rehab
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MDPI 2021
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​This project presents an AI-powered rehabilitation system that personalizes home care while following ethical design principles. It uses hybrid machine learning to predict patient needs with up to 100% accuracy, all without intrusive monitoring.
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