Responsible LLM Deployment for High-Stake Decisions by Decentralized Technologies and Human-AI Interactions

ArXiv ID: 2512.04108 “View on arXiv”

Authors: Swati Sachan, Theo Miller, Mai Phuong Nguyen

Abstract

High-stakes decision domains are increasingly exploring the potential of Large Language Models (LLMs) for complex decision-making tasks. However, LLM deployment in real-world settings presents challenges in data security, evaluation of its capabilities outside controlled environments, and accountability attribution in the event of adversarial decisions. This paper proposes a framework for responsible deployment of LLM-based decision-support systems through active human involvement. It integrates interactive collaboration between human experts and developers through multiple iterations at the pre-deployment stage to assess the uncertain samples and judge the stability of the explanation provided by post-hoc XAI techniques. Local LLM deployment within organizations and decentralized technologies, such as Blockchain and IPFS, are proposed to create immutable records of LLM activities for automated auditing to enhance security and trace back accountability. It was tested on Bert-large-uncased, Mistral, and LLaMA 2 and 3 models to assess the capability to support responsible financial decisions on business lending.

Keywords: Large Language Models (LLMs), Human-in-the-loop, Blockchain Auditing, Decision Support Systems, Fintech

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Street Traders
• Why: The paper introduces a conceptual framework integrating human-AI collaboration and decentralized tech for responsible LLM deployment, with limited mathematical formalization (primarily a standard optimization equation and metric definitions). Empirical rigor is moderate, as it tests specific LLMs (BERT, Mistral, LLaMA) on a financial use case (business lending) and discusses quantifiable metrics like perplexity and agreement scores, though it lacks detailed backtesting or code.

  flowchart TD
    A["Research Goal:<br/>Framework for Responsible LLM Deployment<br/>in High-Stakes Decisions"] --> B["Methodology:<br/>Human-AI Collaboration &<br/>Decentralized Auditing"]
    
    B --> C["Input Data:<br/>Financial Lending Decisions<br/>(Bert-large, Mistral, LLaMA)"]
    
    C --> D["Process 1:<br/>Local LLM Deployment<br/>w/ Human-in-the-Loop Verification"]
    C --> E["Process 2:<br/>Blockchain/IPFS Integration<br/>for Immutable Audit Trails"]
    
    D --> F["Outcome:<br/>Stability Assessment of<br/>Post-hoc XAI Explanations"]
    E --> F
    
    F --> G["Key Outcome:<br/>Enhanced Security, Traceability,<br/>& Accountability in Decisions"]