Manual approvals are still used by banks and other NGOs to approve loans. It
takes time and is prone to mistakes because it is controlled by a bank
employee. Several fields of machine learning mining technologies have been
utilized to enhance various areas of credit rating forecast. A major goal of
this research is to look at current sentiment analysis techniques that are
being used to generate creditworthiness.

这篇研究主要考察当前使用的情感分析技术以提高信用评级预测的准确性，以解决银行及其他非政府组织人工审核贷款的低效与容易出错的问题。

信用情感评分预测调查

A Survey: Credit Sentiment Score Prediction

The utility of machine learning in evaluating the creditworthiness of loan
applicants has been proofed since decades ago. However, automatic decisions may
lead to different treatments over groups or individuals, potentially causing
discrimination. This paper benchmarks 12 top bias mitigation methods discussing
their performance based on 5 different fairness metrics, accuracy achieved, and
potential profits for the financial institutions. Our findings show the
difficulties in achieving fairness while preserving accuracy and profits.
Additionally, it highlights some of the best and worst performers and helps
bridging the gap between experimental machine learning and its industrial
application.

本研究探讨了 12 种顶级的偏差缓解方法，讨论其性能，基于 5 种不同的公平度量标准、实现的准确性和金融机构的潜在利润。我们的研究结果表明，在保留准确性和利润的前提下实现公平存在困难，同时突出了一些最佳和最差的表现，并有助于实验机器学习和其工业应用之间的联系。

公平信貸評分的算法決策方法

Algorithmic decision making methods for fair credit scoring

Settings such as lending and policing can be modeled by a centralized agent
allocating a resource (loans or police officers) amongst several groups, in
order to maximize some objective (loans given that are repaid or criminals that
are apprehended). Often in such problems fairness is also a concern. A natural
notion of fairness, based on general principles of equality of opportunity,
asks that conditional on an individual being a candidate for the resource, the
probability of actually receiving it is approximately independent of the
individual's group. In lending this means that equally creditworthy individuals
in different racial groups have roughly equal chances of receiving a loan. In
policing it means that two individuals committing the same crime in different
districts would have roughly equal chances of being arrested.
We formalize this fairness notion for allocation problems and investigate its
algorithmic consequences. Our main technical results include an efficient
learning algorithm that converges to an optimal fair allocation even when the
frequency of candidates (creditworthy individuals or criminals) in each group
is unknown. The algorithm operates in a censored feedback model in which only
the number of candidates who received the resource in a given allocation can be
observed, rather than the true number of candidates. This models the fact that
we do not learn the creditworthiness of individuals we do not give loans to nor
learn about crimes committed if the police presence in a district is low.
As an application of our framework, we consider the predictive policing
problem. The learning algorithm is trained on arrest data gathered from its own
deployments on previous days, resulting in a potential feedback loop that our
algorithm provably overcomes. We empirically investigate the performance of our
algorithm on the Philadelphia Crime Incidents dataset.

该研究探讨基于机会平等的公平分配问题，并介绍了一种有效的学习算法来克服不完整反馈数据，进而应用于预测犯罪问题。