让公众参与设计隐私保护的AI，平衡数学安全与民主透明。
Democratizing Differential Privacy: A Participatory AI Framework for Public Decision-Making
- 用多准则决策法动态调整隐私参数，匹配公众偏好。
- 实时可视化误差并用AI分析噪声影响，提升可解释性。
- 自动响应法规变化，保障法律合规性，适合政策制定者。
本文提出一种对话式界面系统，支持在公共部门应用中参与式设计差分隐私人工智能系统。为解决数学隐私保障与民主问责之间的矛盾，我们提出三项核心贡献：(1) 基于TOPSIS多准则决策分析的自适应ε选择协议，将公民偏好与差分隐私（DP）参数对齐；(2) 可解释的噪声注入框架，包含实时均值绝对误差（MAE）可视化和GPT-4驱动的影响分析；(3) 集成的法律合规机制，根据不断演变的监管要求动态调节隐私预算。结果表明，对话接口能有效提升公众对算法隐私机制的参与度，确保公共治理中的隐私保护AI兼具数学稳健性与民主可问责性。
原文摘要 · Abstract (English)
This paper introduces a conversational interface system that enables participatory design of differentially private AI systems in public sector applications. Addressing the challenge of balancing mathematical privacy guarantees with democratic accountability, we propose three key contributions: (1) an adaptive $ε$-selection protocol leveraging TOPSIS multi-criteria decision analysis to align citizen preferences with differential privacy (DP) parameters, (2) an explainable noise-injection framework featuring real-time Mean Absolute Error (MAE) visualizations and GPT-4-powered impact analysis, and (3) an integrated legal-compliance mechanism that dynamically modulates privacy budgets based on evolving regulatory constraints. Our results advance participatory AI practices by demonstrating how conversational interfaces can enhance public engagement in algorithmic privacy mechanisms, ensuring that privacy-preserving AI in public sector governance remains both mathematically robust and democratically accountable.
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