The Pursuit of Responsible AI
AI systems are making consequential decisions in healthcare, finance, and public administration. The gap between ethical aspiration and operational accountability is where harm occurs.
Innovating Audit for an Innovative World
Traditional audit frameworks were built for static systems. AI systems learn, adapt, and shift between training and deployment. This paper maps what audit must become.
Designing Ethical Systems by Auditing Ethics
Ethics treated as a communications function fails when it is tested. This paper argues that ethics must be embedded as a design requirement and audited as such.
Applying Privacy-Enhancing Technologies to LLMs in Critical Infrastructure Contexts
LLMs deployed in critical infrastructure carry data exposure risks that standard security controls were not designed to address. This paper evaluates where privacy-enhancing technologies close that gap.
Securing AI Systems Through Transparency: A CIA Triad-Based Analysis
The CIA Triad has anchored information security for decades. Applying it to AI systems reveals where transparency obligations and security requirements create genuine tension.
Risks to Financial Inclusion by Anti-Money Laundering and Financial Counterterrorism Algorithms
AML and counterterrorism algorithms are built to detect illicit financial flows. This paper examines how those same systems exclude the populations they were built to protect.
Evaluating Organizational Alignment with the NIST AI Risk Management Framework
Citing the NIST AI RMF in governance documentation is not the same as aligning with its substance. This paper provides a methodology for telling the difference.
Evolving AI Risk Management: A Maturity Model Based on the NIST AI Risk Management Framework
The responsible AI community has produced principles. The private sector has not kept pace. This paper provides the first structured maturity model for measuring that gap.
Deploying Responsible AI
AI adoption is outpacing governance. This paper addresses what responsible deployment actually requires in practice: not aspirations, but structures.
Evaluating AI Governance
AI governance programmes vary enormously in their substance. This paper provides a practical methodology for assessing what is real and what is documentation.