A Human-LLM Teaming Framework for Privacy Risk Analysis: An Illustration with CBDC-Based Welfare Schemes
2026-08-17 • Emerging Technologies
Emerging TechnologiesArtificial IntelligenceComputational Engineering, Finance, and ScienceComputers and Society
AI summaryⓘ
The authors looked at how digital money systems used for welfare can risk people's privacy because they handle lots of personal data. They suggest a teamwork method where humans and large language models (LLMs) work together to better spot privacy risks. The LLM quickly organizes information while humans check, improve, and finalize the findings. Their example shows how this cooperation helps tell apart facts from guesses and fills in missing information. This work helps create better ways for humans and AI to analyze privacy concerns.
Central Bank Digital Currencyprivacy risk assessmentwelfare schemeslarge language modelshuman-AI teamingdata characterizationsurveillancestigmatizationcontextual evaluationPRIAM methodology
Authors
Sourya Joyee De, Abdessamad Imine
Abstract
Central Bank Digital Currency (CBDC)-based welfare schemes may be potentially privacy invasive as they process significant volumes of beneficiary personal data and lead to privacy harms such as surveillance, discrimination and stigmatization. Such welfare delivery schemes involve complex digital ecosystems and large number of stakeholders. Consequently, to examine their privacy risks, privacy risk assessments require extensive information gathering and synthesis, complex reasoning, scenario explorations, contextual evaluation and human judgement. Thus, they present ideal scenarios for human-LLM teaming, where effective integration of complementary human and LLM capabilities can yield an outcome far superior to either human-only or LLM-only assessments. In this paper, we propose a first human-LLM teaming framework for the systematic privacy risk analysis methodology called PRIAM. The framework specifies an iterative collaborative process in which the LLM processes large-scale documentary evidence to produce initial outputs, which are then interpreted and evaluated by human experts who direct their further refinement by the LLM and exercise their judgement to finalize the output. We illustrate the framework on the data characterization activity of PRIAM using a CBDC-based welfare scheme use case. The illustration demonstrates that while LLMs generate the initial data categories and assign initial values to data attributes, human experts evaluate and provide feedback to refine them, distinguishing documented evidence from inferences, identifying information gaps, and flagging unsupported or ambiguous outputs. This framework serves as a foundational contribution towards human-AI teaming for privacy risk assessments.