Vamsee Pamisetty on Building Decision Intelligence That Stakeholders Can Actually Trust
Summary
Vamsee Pamisetty's research, published in the International Journal of Advanced Research in Computer Science and Technology (IJARCST), introduces a framework for "Explainable Agentic AI" designed for secure and adaptive decision intelligence in high-stakes environments like food service and financial systems. Drawing on his decade-plus experience, including as a senior technical authority for the District of Columbia's Oracle Cloud-based enterprise financial platform (DIFS), Pamisetty addresses the critical gap where AI systems cannot adequately explain their autonomous decisions. His framework formalizes decision intelligence through mathematical models, an explainability confidence score aggregating transparency, interpretability, and auditability, and adaptive trust modeling that adjusts stakeholder confidence over time. It also incorporates a data governance integrity model to ensure auditability, treating explainability, transparency, auditability, accountability, and adaptability as core design requirements for trustworthy AI deployment.
Key takeaway
For AI Architects and Directors of AI/ML deploying systems in regulated or high-consequence environments, you must integrate explainability as a foundational design requirement, not an optional feature. Your teams should adopt frameworks that measure trust and auditability dynamically, ensuring AI decisions are traceable and verifiable. This approach mitigates institutional risk and builds stakeholder confidence, moving beyond mere predictive performance to achieve true decision intelligence and regulatory compliance.
Key insights
Trustworthy AI requires inherent explainability, treating it as a measurable design requirement, not an afterthought.
Principles
- AI without explanation cannot be trusted.
- Trust in AI is dynamic, not static.
- Explainability is a design requirement.
Method
Pamisetty's framework uses mathematical models, an explainability confidence score (transparency, interpretability, auditability), and adaptive trust modeling to formalize and measure decision intelligence and system trustworthiness.
In practice
- Evaluate AI readiness using five pillars.
- Implement adaptive trust feedback loops.
- Design for auditability from data ingestion.
Topics
- Explainable AI
- Agentic AI
- Decision Intelligence
- Financial Systems
- Supply Chain Management
- AI Governance
- Adaptive Trust Modeling
Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, AI Architect, Director of AI/ML
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Editorial summary, takeaway, and curation by AIssential. Original article published by HackerNoon.