Several scientific studies have reported the existence of the income gap
among rideshare drivers based on demographic factors such as gender, age, race,
etc. In this paper, we study the income inequality among rideshare drivers due
to discriminative cancellations from riders, and the tradeoff between the
income inequality (called fairness objective) with the system efficiency
(called profit objective). We proposed an online bipartite-matching model where
riders are assumed to arrive sequentially following a distribution known in
advance. The highlight of our model is the concept of acceptance rate between
any pair of driver-rider types, where types are defined based on demographic
factors. Specially, we assume each rider can accept or cancel the driver
assigned to her, each occurs with a certain probability which reflects the
acceptance degree from the rider type towards the driver type. We construct a
bi-objective linear program as a valid benchmark and propose two LP-based
parameterized online algorithms. Rigorous online competitive ratio analysis is
offered to demonstrate the flexibility and efficiency of our online algorithms
in balancing the two conflicting goals, promotions of fairness and profit.
Experimental results on a real-world dataset are provided as well, which
confirm our theoretical predictions.

研究了网约车司机收入不平等的原因，并通过在线二分图匹配模型提出促进公平和利润的 LP-based 参数化在线算法来解决这个问题。

在拼车服务中平衡司机收入平等与系统效率的交易

Trading the System Efficiency for the Income Equality of Drivers in  Rideshare

The prominent inequality of wealth and income is a huge concern especially in
the United States. The likelihood of diminishing poverty is one valid reason to
reduce the world's surging level of economic inequality. The principle of
universal moral equality ensures sustainable development and improve the
economic stability of a nation. Governments in different countries have been
trying their best to address this problem and provide an optimal solution. This
study aims to show the usage of machine learning and data mining techniques in
providing a solution to the income equality problem. The UCI Adult Dataset has
been used for the purpose. Classification has been done to predict whether a
person's yearly income in US falls in the income category of either greater
than 50K Dollars or less equal to 50K Dollars category based on a certain set
of attributes. The Gradient Boosting Classifier Model was deployed which
clocked the highest accuracy of 88.16%, eventually breaking the benchmark
accuracy of existing works.

本研究旨在探索使用机器学习和数据挖掘技术解决收入不平等问题。使用 UCI 成年人数据集进行分类，并采用 Gradient Boosting 分类器模型，最终达到了 88.16％的准确率，打破了现有工作的基准准确率。