ALAYNA
KENNEDY
AI Governance Researcher & Writer
AI Governance Researcher & Writer
I'm a pioneering AI governance researcher and strategic advisor who transforms complex ethical frameworks into actionable business strategies that drive innovation while ensuring responsibility.
As an AI Governance Researcher at Mastercard, I lead initiatives that operationalize responsible AI practices across global financial systems, directly impacting millions of users worldwide. My unique approach bridges technical implementation and policy development, creating governance frameworks that balance innovation with accountability.
With a proven track record spanning Fortune 500 companies, government agencies, and cutting-edge startups, I've successfully launched enterprise-wide AI ethics programs, secured significant funding for governance initiatives, and published influential research that shapes industry standards.
I hold a master's degree in Science and Technology in Society from the University of Edinburgh and a bachelor's in Engineering Science from Penn State University. Beyond my corporate work, I write about technology's societal impact on my Substack, Cruel Miracles.
Transforming principles into practice by implementing governance frameworks at scale. My work includes assessing AI risks for 100+ products at Mastercard, creating IBM's first federal AI ethics program, and developing evaluation frameworks that bridge technical implementation with ethical oversight.
Power dynamics and social contexts fundamentally shape how AI systems are developed and deployed. Through research on multi-stakeholder governance and the political economy of AI ethics, I've explored these relationships while bridging technical and legal communities via the Digital Freedom Fund.
Advancing auditable AI systems requires both technical tools and procedural frameworks. My contributions include developing model evaluation frameworks at Mastercard, researching algorithmic hiring audits with Data & Society, and presenting interpretability standards at NeurIPS workshops.
Safe AI capability growth demands robust governance structures at every stage. I've implemented federated AI governance across 12+ divisions at a 30,000+ person company, established cross-functional working groups, and developed stage-gate processes for responsible deployment.
Effective AI governance requires collaboration between industry, civil society, and government. My work includes publishing research on NYC's algorithmic hiring law, facilitating tech-legal community dialogues, and engaging federal agencies on AI adoption strategies.
Contributing to academic discourse on AI alignment and safety through peer-reviewed research. Publications include 'Auditing Government AI' (NeurIPS 2020), algorithmic fairness research in FAccT venues, and analysis of AI ethics operationalization frameworks in industry contexts.
A comprehensive report drawing on experiences from hundreds of ethics professionals on implementing responsible AI in industry.
Research on accountability mechanisms for AI systems in public sector applications, presented at NeurIPS workshop.
In-depth discussion on the intersection of AI safety, ethics, and practical implementation in enterprise settings.
Policy analysis of New York City's groundbreaking legislation on automated employment decision tools and algorithmic bias auditing requirements.
Co-organized and hosted ALT FAccT 2025, an alternative conference challenging traditional AI fairness venues. Brought together 100+ researchers, practitioners, and activists to reimagine accountability in AI systems. Led an industry panel on operationalizing ethics beyond compliance frameworks.
Authored master's dissertation in collaboration with Data & Society's AI on the Ground initiative, analyzing how industry professionals operationalize AI ethics principles in practice. Through ethnographic interviews with responsible technology professionals, developed evidence-based recommendations for implementing ethical AI governance at scale.
Thanks for reaching out! I'm happy to respond to questions about my work, comments on AI, or any other inquiries. I'd love to hear from you!
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