We bridge the gap between complex AI systems and meaningful human understanding - through rigorous research, interpretable design, and responsible deployment.
Post-hoc and ante-hoc analysis of model decision-making processes
Bias detection, equity analysis, and algorithmic accountability
Human-centered explanation interfaces for real-world deployment
Policy frameworks, regulatory compliance, and responsible AI strategy
Our work spans the full lifecycle of explainable AI — from foundational methods research to deployed explanation systems in high-stakes domains.
Research on attribution, counterfactuals, concept-based explanations, and emerging approaches to foundation model interpretability.
User studies, cognitive load analysis, and iterative interface design that ensures explanations are genuinely useful to the people who need them.
Real-world deployments across healthcare, finance, government, and technology - translating research into actionable transparency for high-stakes AI systems.
Get in touch to discuss a research collaboration or consulting engagement.
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