消除尼日利亚金融科技领域的结构性不平等
Neutralizing Structural Inequality in the Nigerian FinTech Sector
AI总结：
针对尼日利亚金融科技领域销售点欺诈检测中算法决策系统存在的结构性不平等问题，提出分层人机人工智能分类模型及三层路由策略，实验证明该模型能缩小区域性能差距，消除结构性偏见，实现机会平等。
AI中文摘要：
金融服务中的算法决策系统常依赖无意中编码结构性不平等的数据代理。本文为尼日利亚金融科技领域的销售点欺诈检测引入分层人机人工智能分类模型。采用“人人平等”世界观，应对歧视性清洗挑战，即系统将农村网络超时等与基础设施相关的偶然噪声误判为欺诈意图。实施三层路由策略，利用校准集成模型作为主要过滤器。实验结果表明，与自主基线相比，互补差距有统计学意义的1.88%，欺诈召回率提高24.79个百分点，模型将区域性能差距从19.43个百分点降至2.88个百分点，消除了结构性偏见。分层协作提供了实质性机会平等的有力机制。
英文摘要：
Algorithmic decision systems in financial services often rely on data proxies that inadvertently encode structural inequalities. This paper introduces a hierarchical human-AI triage model for Point of Sale fraud detection in the Nigerian FinTech sector. Adopting a We Are All Equal worldview, we address the challenge of discrimination laundering, wherein the system misinterprets infrastructure related aleatoric noise such as rural network timeouts as fraudulent intent. We implement a three-tier routing policy utilizing a calibrated ensemble model as a primary filter. The policy routes transactions characterized by epistemic uncertainty such as cold start new accounts to specialist analysts while reserving high stakes cases for a senior supervisor. To manage finite human capacity, we utilize a dynamic shadow price to ration human attention and implement a random audit mechanism to prevent human skill atrophy. Our experimental results demonstrate a statistically significant 1.88\% complementarity gap and a 24.79\% percentage point gain in fraud recall over an autonomous baseline. Crucially, the model reduces the regional performance gap from 19.43 to 2.88 percentage points, neutralizing structural bias. Hierarchical collaboration provides a robust mechanism for substantive equality of opportunity, ensuring that rural accounts are not excluded from the digital economy due to environmental brute luck.
