论文标题
在线小额信贷平台中缓解偏见：kiva.org上的案例研究
Mitigating Bias in Online Microfinance Platforms: A Case Study on Kiva.org
论文作者
论文摘要
在贷款行业的过去几十年中，金融脱节是在全球范围内发生的。传统上，即使对于少量资金，银行也将充当资金和借款人之间的渠道。现在有可能通过像Kiva，Prosper，LendingClub这样的在线平台出现与此类资金相关的一些障碍。例如，KIVA与发展中国家的微型金融机构（MFI）合作，以简短的传记，要求的贷款，贷款期限和目的来建立借款人的互联网概况。特别是Kiva，允许贷方通过集团或个人资金在不同部门的项目中为项目提供资金。传统的研究研究纯粹是从贷款属性的角度研究了贷方偏好背后的各种因素，直到最近才研究了一些越野文化偏好。在本文中，我们调查了借款人国家对与不同部门相关的贷款的偏好的贷方看法。我们发现，经济因素和贷款属性的影响可能具有实质上不同的角色，可以在获得更快的资金方面发挥不同部门的作用。我们使用依赖贝叶斯变量选择方法的因果推理和回归模型中的最新工具正式研究和量化不同贷款领域中普遍存在的隐藏偏见。然后，我们扩展了这些模型，以根据我们的经验分析纳入公平性约束，发现此类模型仍然可以在基线回归模型方面取得几乎可比的结果。
Over the last couple of decades in the lending industry, financial disintermediation has occurred on a global scale. Traditionally, even for small supply of funds, banks would act as the conduit between the funds and the borrowers. It has now been possible to overcome some of the obstacles associated with such supply of funds with the advent of online platforms like Kiva, Prosper, LendingClub. Kiva for example, works with Micro Finance Institutions (MFIs) in developing countries to build Internet profiles of borrowers with a brief biography, loan requested, loan term, and purpose. Kiva, in particular, allows lenders to fund projects in different sectors through group or individual funding. Traditional research studies have investigated various factors behind lender preferences purely from the perspective of loan attributes and only until recently have some cross-country cultural preferences been investigated. In this paper, we investigate lender perceptions of economic factors of the borrower countries in relation to their preferences towards loans associated with different sectors. We find that the influence from economic factors and loan attributes can have substantially different roles to play for different sectors in achieving faster funding. We formally investigate and quantify the hidden biases prevalent in different loan sectors using recent tools from causal inference and regression models that rely on Bayesian variable selection methods. We then extend these models to incorporate fairness constraints based on our empirical analysis and find that such models can still achieve near comparable results with respect to baseline regression models.
