sanjhimandowara/trust-calibration

Trust Calibration AI

Adaptive Multi-Source Decision Intelligence under Uncertainty and Adversarial Conditions

Executive Summary

Most AI systems fail not because they lack data, but because they trust the wrong data.

This project builds a reinforcement learning system that dynamically weighs, suppresses, and combines multiple noisy, conflicting, delayed, and adversarial signals to make safe operational decisions.

Instead of predicting directly, the system learns a higher-value capability:

"Which sources should I trust right now?"

This shifts AI from static prediction to adaptive trust management.

Final decisions: IGNORE | INVESTIGATE | ESCALATE

Problem Statement

In real-world systems such as cybersecurity, fraud detection, and monitoring:

• —Multiple sources provide conflicting signals
• —Some sources are unreliable or adversarial
• —High-confidence incorrect signals mislead systems
• —Static rules fail under dynamic conditions

This results in:

• —false alerts (alert fatigue)
• —missed critical threats
• —over-escalation
• —unreliable automation

The core challenge is not prediction — it is trust calibration under uncertainty.

Solution

We model decision-making as a trust allocation problem.

At each step:

1. 1.Multiple signals are generated
2. 2.Signals may conflict or behave adversarially
3. 3.The agent selects a trust strategy
4. 4.Unreliable sources are down-weighted or suppressed
5. 5.Signals are fused into a calibrated decision

Key Innovation

This is not a classification system.

The agent learns:

• —which sources to trust
• —when signals are misleading
• —how to adapt under uncertainty
• —how to prevent cascading errors

This enables robust decision-making under adversarial and uncertain conditions.

System Architecture

Core Components

Signal Generator

• —simulates 4 heterogeneous sources
• —injects adversarial and noisy behavior

Conflict Detector

• —captures disagreement across sources

Uncertainty Estimator

• —measures ambiguity using variance

Meta-Trust Layer

• —tracks reliability over time
• —suppresses misleading sources

Consensus Engine

• —fuses signals using trust + confidence

Decision Engine

• —outputs final operational decision

Reward Engine

• —enforces correctness and safety

Action Space (Trust Strategies)

0 → equal weighting 1 → prioritize anomaly + network 2 → prioritize rule engine 3 → suppress unreliable source 4 → confidence-aware weighting 5 → conflict-aware balancing

Evaluation Results

Metric PPO Baseline Improvement --------------------------------------------------------------------------- Average Reward 16.814 13.295 +26.5% Decision Accuracy 88.40% 81.85% +8.0% False Escalation Rate 0.160 0.200 -20.0% Missed Threat Rate 0.000 0.000 0.0% Adversarial Robustness 78.42% 47.11% +66.5% Avg Conflict 0.143 0.156 +8.5% Avg Uncertainty 0.322 0.331 +2.7%

PPO vs Baseline Comparison

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Why Reinforcement Learning

The problem is sequential and adaptive:

• —trust evolves over time
• —signals change behavior
• —adversarial patterns emerge
• —decisions influence future states

Static models fail here — reinforcement learning is required.

How It Works

1. 1.Signals are generated
2. 2.Adversarial noise is injected
3. 3.Agent assigns trust weights
4. 4.Unreliable sources are suppressed
5. 5.Signals are fused
6. 6.Decision is made

Real-World Impact

Cybersecurity

• —reduces alert fatigue
• —improves threat detection

Fraud Detection

• —handles conflicting risk signals

Monitoring Systems

• —improves reliability under noise

This system moves closer to real-world decision intelligence than traditional ML pipelines.

Why This Stands Out

• —dynamic trust adaptation
• —adversarial robustness
• —uncertainty-aware decision-making
• —source suppression capability
• —measurable improvement over baselines

This is not a toy model — it is a structured decision system.

Limitations

• —simulated environment
• —limited number of sources
• —PPO sensitivity to hyperparameters
• —no long-term memory modeling

Future Work

• —integration with real-world data pipelines
• —multimodal signals (text, logs, images)
• —transformer-based trust modeling
• —human-in-the-loop feedback
• —advanced adversarial scenario simulation
• —improved interpretability and explanations

Project Structure

trust_calibration/ ├── envs/ ├── agents/ ├── training/ ├── dashboard/ ├── models/ ├── outputs/ ├── tests/ ├── inference.py ├── Dockerfile ├── openenv.yaml ├── requirements.txt └── README.md

Installation

cd ~/trust_calibration pip install -r requirements.txt

Run Dashboard

PYTHONPATH=. streamlit run dashboard/streamlit_app.py

Open: http://localhost:8501

Final Takeaway

AI should not just predict.

AI should learn:

"Who to trust, when, and why."