In the rapidly evolving AI ecosystem, large language models (LLMs) and autonomous agents have become central to decision-making systems-from fraud detection and credit scoring to welfare distribution. However, these systems operate on probabilities and confidence scores, not absolutes. That poses a critical challenge: How do we ensure fairness, accountability, and trust when AI decisions are inherently uncertain?

This talk offers a deep dive into aligning Responsible AI principles with the probabilistic nature of modern AI systems. We explore how to architect systems that not only predict, but also explain, justify, and remain auditable drawing from real-world implementations in financial oversight.

We will show the following:

• A credit risk assessment agent that explains its eligibility score and confidence band using real DIA data.
• A welfare benefit approval system where LLM outputs come with rational visualizations for auditors and beneficiaries.
• A fraud detection tool that flags risky transactions and lets auditors explore the model's reasoning trail before acting.

This session will empower AI Engineers, Data Scientists, Business Leaders, Auditors, and policymakers alike to navigate probabilistic AI outcomes without compromising on transparency, ethics, or stakeholder trust.