# Evaluating Fairness in Black-box Algorithmic Markets:
A Case Study of Ride Sharing in Chicago

Yuhan Liu  Yuhan Zheng  Siyuan Zhang  Lydia T. Liu

###### Abstract

This study examines fairness within the rideshare industry, focusing on both
drivers’ wages and riders’ trip fares. Through quantitative analysis, we found
that drivers’ hourly wages are significantly influenced by factors such as
race/ethnicity, health insurance status, tenure to the platform, and working
hours. Despite platforms’ policies not intentionally embedding biases,
disparities persist based on these characteristics. For ride fares, we propose
a method to audit the pricing policy of a proprietary algorithm by replicating
it; we conduct a hypothesis test to determine if the predicted rideshare fare
is greater than the taxi fare, taking into account the approximation error in
the replicated model. Challenges in accessing data and transparency hinder our
ability to isolate discrimination from other factors, underscoring the need
for collaboration with rideshare platforms and drivers to enhance fairness in
algorithmic wage determination and pricing.

Algorithm auditing, gig economy, ML fairness

##  1 Introduction

Rideshare platforms such as Uber and Lyft serve as algorithmic market
intermediaries, matching drivers and riders (Chan & Shaheen, 2012; Agatz et
al., 2010). In addition to providing a matchmaking service, they determine the
pricing for riders and the earnings for drivers for each trip, via a dynamic
pricing strategy to manage the balance between rider demand and driver
availability (Shapiro, 2018; Pandit et al., 2019; Dong & Leng, 2021). The
algorithms driving this process optimize complex objectives, operating as
black-box to both riders and drivers, which leads to large information
asymmetry. Moreover, the algorithmic decisions regarding pricing and wages
exacerbate the power imbalances (Rosenblat & Stark, 2016; Yao et al., 2021;
Jarrahi & Sutherland, 2019; Jarrahi et al., 2020). This power asymmetry
undermines the emotional, physical, and financial well-being of both riders
and drivers (Dubal, 2023; Woodcock & Johnson, 2018; Watkins, 2023).

Given significant information and power imbalance, stakeholders of gig
platforms have raised concerns regarding algorithm fairness (Zhang et al.,
2022; Hsieh et al., 2023). While prior research has explored the individual
drivers’ perceptions and expectations of fairness through qualitative
approaches, there remains a gap in quantitatively assessing these issues. This
paper aims to fill that gap by analyzing the fairness of AI algorithms used in
rideshare platforms, specifically focusing on their role in pricing and wage
decisions. In this study, we employ publicly available data to measure the
following: 1. Fairness in compensating drivers and 2. Fairness in charging
riders.

We use the Chicago Transportation Network Provider Dataset, including the
Public Passenger Vehicle Chauffeur Survey, Transportation Network Providers
Trips, and Taxi Trips to measure the aforementioned areas. The Public
Passenger Vehicle Chauffeur Survey Dataset is used to evaluate the
compensation bias of drivers in terms of different aspects e.g. age, race,
etc. The other two datasets are used to evaluate the fairness of the pricing
algorithms of the rideshare platforms compared to the taxi companies in
Chicago.

The main contribution of this work is a quantitative, empirical investigation
of algorithmic fairness in the two-sided market for ride-sharing, examining it
from both the drivers’ and the passengers’ perspectives. We tackle a key
challenge for performing a comparative audit of fares: given the AI algorithms
are not open-sourced in most commercial settings, we attempt to replicate the
pricing algorithm via machine learning in order to generate counterfactual
fares, and account for the prediction error of the replicated algorithm in our
hypothesis test. Finally, the study highlights the need for policy
interventions to increase transparency in AI dynamic pricing algorithms used
by rideshare platforms.

### Related work

Recent research has revealed inequality in the gig economy across different
genders, races, and socioeconomic statuses (Hsieh et al., 2023). Like
traditional industries, the pay gap exists in crowdsourcing and freelancing
platforms as prior work has shown (Dubey et al., 2017; Dunn et al., 2021;
Foong & Gerber, 2021). In these platforms, compensation is often decided by
service requesters (Jarrahi & Sutherland, 2019; Jarrahi et al., 2020).
However, in the rideshare industry, drivers’ wages are determined by
algorithms instead of humans, which raised people’s attention to whether the
pay gap still exists. Prior work by Cook and colleagues revealed a significant
gender pay gap in Uber with the Chicago dataset (Cook et al., 2021). Our study
aims to evaluate the fairness of drivers’ wages by examining other demographic
factors like age and gender, socioeconomic factors like insurance coverage,
and working factors such as tenure to the platform and average working hours.

In addition to determining wages, algorithms also set the prices paid by
riders. Unlike taxis, which use a transparent and fixed pricing strategy,
ridesharing services have dynamic pricing influenced by trip distance, time,
and demand-supply status (Shapiro, 2018; Pandit et al., 2019; Chen et al.,
2015; Hall et al., 2021). Prior works (Ge et al., 2020; Brown, 2018) have
revealed discrimination in ridesharing, such as unequal wait times and
cancellation rates across different demographic groups using simulated
requests. Recent research also pointed out that price discrimination exists in
different neighborhoods (Pandey & Caliskan, 2021). According to recent
reports, customers showed dissatisfaction with rideshare services and
perceived them as overpriced (Roberts & Crane, 2023; Horowitz, 2021; Abraham,
2023). In this project, we aim to explore whether riders are fairly charged
for the services they receive, addressing potential unfairness from the
perspective of fair competition in the market.

##  2 Data & Method

### Dataset

We used the Public Passenger Vehicle Chauffeur Survey Dataset to evaluate
fairness in compensating drivers. The dataset contains 7021 self-reported data
points during 2021 in response to 73 questions. The survey asked about
drivers’ basic demographic information and also drivers’ working experience,
including their working schedule and average earnings. We filtered the dataset
to exclude empty datapoints for each feature we were interested in. For most
features, excluding education level and health insurance status, the filtered
dataset has nearly 3000 non-empty self-reported data points. Both education
level and health insurance status only have up to 1600 non-empty rows.

For fairness in charging riders, we selected the Transportation Network
Providers (TNP) Trips and Taxi Trips datasets. The TNP Trips monthly updates
all the trips reported by rideshare companies, starting from Jan 1, 2023, to
the current time, in Chicago. Each trip contains information such as pick-
up/drop-off location, trip miles, trip length, trip fare breakdown, etc. We
filtered out all carpool trips and all the trips after January 2024, resulting
in 81,274,469 rows in the dataset. The Taxi Trips dataset contains data from
2013 to 2023 reported by taxi companies in Chicago. Each trip contains the
same information as the TNP Trips Dataset. We filtered out all trips that
happened before 2023 to make the time consistent, as well as trips reported in
the invalid distance or incurring toll fees, resulting in 5,808,081 rows in
the dataset.

### Evaluating fairness in driver’s compensation

For fairness in drivers’ compensation, we measure the bias in the following
aspects regarding their self-reported earnings with statistical tests and
visualization: 1. age of the driver, 2. race/ethnicity of the driver, 3.
highest level of education of the driver, 4. health insurance status, 5. how
long the driver has been driving for the platform, 6. on average how long the
driver drives per day.

We hypothesize that certain demographic factors, such as age, race, and level
of education, may influence the compensation received by rideshare drivers on
the platform. Age-related bias may be introduced to prefer older drivers for
safety (McCartt et al., 2009; Guo et al., 2017). Similarly, biases related to
race may manifest through discriminatory user behavior, such as riders
canceling trips or giving lower ratings based on the driver’s race (Ge et al.,
2020). Regarding the level of education, discrimination may occur if the
platform perceives drivers with higher education levels as being more
competent or trustworthy, leading to preferential treatment and potentially
higher earnings. Access to benefits such as health insurance could also
contribute to disparities in earnings, as well as factors related to drivers’
working conditions, such as the number of hours driven per day and part-time
or full-time status. These hypotheses reflect concerns about potential biases
within the rideshare platform, such as the possibility of favoring certain
groups of drivers over others.

### Evaluating fairness in customer’s payment

For fairness in charging riders, we make the assumption that the amount riders
pay is the sum of the trip fare and additional charges (such as taxes, tolls,
and service fees). Under this assumption, we evaluate only the pricing
algorithm in rideshare platforms, assessing whether the trip fare charged is
fair and reasonable compared to that of taxi companies. In other words, we do
not consider the impact of additional charges like taxes, tolls, and service
fees. We chose to compare with taxi companies because their pricing strategy
is more transparent and fixed compared to that of rideshare platforms. By
excluding the additional charges, we can also explore how dynamic or peak
pricing impacts trip fares. Based on recent reports about the “skyrocketed”
rideshare fares, we hypothesize that the rideshare platforms overcharged their
riders.

For fare charging, we use the distance of the trip (in miles), the time length
of the trip (in seconds), the trip starting timestamp, and the pick-up/drop-
off location as inputs, with the trip fare as the output, to replicate the
algorithms used by the platforms to calculate fares. Building a replicated
algorithm is necessary for this project because we do not (and no one does)
have access to the algorithms and training data used by rideshare platforms.
We then use the replicated algorithm to calculate how much each trip in the
Taxi Trips dataset would cost if the trip were fulfilled on rideshare
platforms. Subsequently, we conducted data analysis to interpret the results.

##  3 Results

### Fairness in driver’s compensation

Our analysis found no significant difference in rideshare drivers’ hourly
wages based on age or education level. However, race/ethnicity, health
insurance status, driver’s tenure, and weekly working hours significantly
impacted wages. This conclusion is supported by Chi-square test results for
each feature.

  * •

Race/ethnicity: Asian/Asian American drivers had the lowest median and mode
hourly wage ($12-13). The distribution of their wages was skewed towards lower
categories, confirmed by a pairwise Chi-square test. Figure 2 (displayed in
Appendix A) illustrates the distribution of wages in each reported race and
ethnicity group.

  * •

Health Insurance Status: Drivers with Medicaid and Medicare had the lowest
wages (median and mode: $12-13 and $<$$10, respectively). Their wage
distribution was also skewed towards lower categories, with statistical
significance confirmed by Chi-square tests (Figure 3 in Appendix A).

  * •

Tenure: Drivers with less than 1 year or more than 10 years of tenure earned
lower wages (mode: $<$$10, median: $12-13) as shown in Figure 4, in Appendix
A. This finding contradicted our hypothesis that longer tenure correlates with
higher wages as observed in other industries. The result of a pairwise Chi-
square test demonstrated that the hourly wage distribution for drivers who
stayed the shortest and the longest with the platform was statistically
significant compared to drivers who lay in the middle.

  * •

Working Hours: Hourly wage increased as their weekly working hours increased
(Figure 5, in Appendix A). Our initial hypothesis was that when drivers drive
longer, their earnings won’t increase due to gamification in the gig economy
mentioned in previous literature (Nagaraj Rao et al., 2024; Woodcock &
Johnson, 2018). However, our analysis shows proof against this hypothesis
since there was a positive correlation between working hours and wage, and a
pairwise Chi-square test showed statistical significance among different
groups.

Additionally, by examining the coefficients of the linear regression model
that took all features above as inputs, we observed that race/ethnicity had
the strongest effect on hourly wage. The effect magnitude of health insurance
status, driver’s tenure and weekly working hours were similar and ranked just
below the effect of race/ethnicity.

### Fairness in customer’s payment

Consider the hypothesis that a customer is charged systematically differently
for a ride taken by taxi vis-a-vis rideshare. A challenge to testing this
hypothesis is that each ride is either taken via taxi or rideshare, but not
both. Since each ride is different and may be more or less expensive due to
its length and duration (even in the case of taxi rides), such differences
must be taken into account. We take the following strategy: For each ride $X$,
we model $f(X)$ as the rideshare fare and $g(X)$ as the taxi fare. We train a
model to predict the rideshare fare function, resulting in an approximation
$\hat{f}(X)$. Our best linear regression model had an RMSE of 5.35 and a
$R^{2}$ of 0.70 when tested with the holdout test data from the TNP dataset.
We take the taxi rides in our dataset $\\{X_{1},X_{2},\ldots,X_{n}\\}$, making
the assumption that it is a representative sample of rides, and compute the
_counterfactual_ rideshare fares $\hat{f}(X_{i})$’s (see Figure 1). An initial
Mann–Whitney U test showed that there was a statistically significant
difference between the distribution of predicted ride share trip fares and
actual taxi fares (rejected the $H_{0}$ hypothesis with $p\text{
value}=0.000$). Further analysis revealed that 14.83% of the 95% confidence
intervals for the counterfactual rideshare fares were below than actual taxi
fares predicted, while 0.89% were higher.

We test the hypothesis that the expected rideshare fare is greater than the
taxi fare, with a one-tailed $t$-test. Specifically, the null hypothesis
($H_{0}$) is that $\mathbb{E}[f(X)-g(X)]\geq 0$. We calculate
$D_{i}=\hat{f}(X_{i})-g(X_{i})$, and its variance, taking into account the
known variance of the error in $\hat{f}$ in the calculation of the _effective
variance_ of the t-statistic. Comparing the p-value to a significance level of
0.05, we failed to reject the null hypothesis that the difference between
predicted rideshare fare and the actual fare is less than or equal to zero,
suggesting insufficient evidence to conclude that rideshare platforms charged
higher than taxi platforms. Our initial investigation shows that the
differences in taxi and rideshare pricing policies, while significant, are
nuanced; we may require more data than as well as further analysis to draw any
stronger conclusions. Our approach of replicating an opaque algorithm from
public data in order to perform hypothesis tests that are _valid for the
opaque algorithm_ may be of independent interest.

Figure 1:  Comparison between the actual taxi fares and the predicted fares if
trips were fulfilled by rideshare platforms. Each data point represents one
trip. Points above the red dotted line indicate predicted fares are higher
than actual fares, while points below indicate predicted fares are lower than
actual fares.

##  4 Discussion and Conclusion

### Disparities in drivers’ compensation calls for policy intervention and
more transparency

We explore fairness issues regarding drivers’ compensation from three
perspectives: demographic features like age and race, other personal
information like education level and health insurance, and work-related
features like platform tenure and working time. Our results show that there is
no significant evidence that age discrimination exists.

However, our data analyses show a strong association between wage and
race/ethnicity. Indirect human input like customer ratings can track racial
bias and influence drivers’ earnings, even if race/ethnicity is not directly
used by pricing algorithms; more investigation is needed to understand if this
constitutes wage discrimination. Previous work in traditional workplace
discrimination discussed the disadvantaged situation faced by African
Americans and Hispanics (Hirsh & Lyons, 2010). In the labor market with
emerging technologies, the old problems are not eliminated but hidden in other
forms. Given that there is no effective technical solution to mitigate hidden
bias embedded in user input, negative impact could be mitigated through the
platform setting up a compensation policy that balances bias in customer input
and its influence on drivers’ earnings to ensure the service quality while
avoiding discrimination.

Recent investigations into gig platforms have uncovered drivers’ perceptions
of unfairness, such as algorithms showing favoritism towards newer drivers
(Nagaraj Rao et al., 2024) and potentially manipulating task allocations to
create challenges in achieving certain Quests (Nagaraj Rao et al., 2024;
Krzywdzinski & Gerber, 2021). Building upon previous work, we aligned our
hypotheses with these findings. While our results indeed demonstrated a degree
of algorithmic favoritism towards new drivers with some experience, we also
observed a positive correlation between working time and wages. Interestingly,
we found a misalignment between drivers’ perceptions shown in previous
literature and our quantitative findings, suggesting potential discrepancies
in compensation fairness perceptions or limitations in our quantitative data’s
ability to capture crucial insights. Notably, our analysis relied on publicly
available data, and further investigations are hindered by the lack of
specific data. This underscores the critical need for enhanced algorithm
transparency—ensuring not only comprehensibility for drivers from non-tech
backgrounds but also facilitating data access for algorithm auditing
researchers with technical expertise.

### Transparency is needed to increase riders’ perception of fairness towards
dynamic pricing system

The rideshare industry is not the first one, nor is it the only one that
introduced a dynamic pricing mechanism. Other transportation industries like
airplanes have been adopting similar pricing strategies for decades
(Proussaloglou & Koppelman, 1999). However, while price differentiation exists
across various transportation modes, the issue of price discrimination within
rideshare platforms has garnered particular attention (Pandey & Caliskan,
2021). Riders hold a biased impression that the rideshare service is
overpriced (Horowitz, 2021; Roberts & Crane, 2023; Abraham, 2023). Our
findings indicate that when compared to taxi service — the most analogous
transportation alternative to rideshare—around 15% of rides result in lower
expenditures for riders while the overcharging percentage is less than 1%. We
attribute the variance in perception between rideshare and the airline
industry to two factors: on-demand nature and lack of transparency.

In contrast to rideshare, the airline industry implements a comparable pricing
model, adjusting fares based on supply, demand, and fuel costs. The primary
distinction lies in transparency: flight ticket prices for various dates and
times are readily available on airline websites, facilitating advance planning
and coordination. Moreover, the abundance of data in the airline sector has
spurred advancements in price prediction technologies (Tziridis et al., 2017;
Boruah et al., 2019). The on-demand nature of rideshare services makes advance
scheduling difficult and complicates price prediction. While AI algorithms are
essential to these operations, their proprietary nature prevents full
disclosure, leading to decreased rider trust. Our analysis highlights the
importance of transparency in building both driver confidence and rider trust.

### Conclusion and Future Work

We investigated algorithmic fairness in the rideshare industry from the
perspective of both drivers and riders and consequently raised concerns about
discrimination in gig worker’s labor market and transparency in these
platforms’ dynamic pricing systems, employing various statistical tests on
public datasets. We discovered that race/ethnicity, health insurance status,
tenure, and working hours significantly impacted hourly wage. On the other
hand, we revealed the difficulty of accessing fairness in customer’s payment
by replicating rideshare platform’s pricing model with features that were
traditionally used to calculate trip fares. Given the limited amount of public
datasets on worker’s wages and limited studies on how drivers interact with
rideshare platforms, we faced obstacles in isolating workforce discrimination
from confounding factors. The lack of transparency in their pricing algorithm
also posed challenges to justify variations in trip fares.

## References

  * Abraham (2023) Abraham, R.  Uber and lyft fare hike revenue study.  2023\.  URL https://www.vice.com/en/article/qjk7n3/uber-lyft-fare-hike-revenue-study.
  * Agatz et al. (2010) Agatz, N., Erera, A., Savelsbergh, M., and Wang, X.  Sustainable passenger transportation: Dynamic ride-sharing.  2010\.
  * Boruah et al. (2019) Boruah, A., Baruah, K., Das, B., Das, M. J., and Gohain, N. B.  A bayesian approach for flight fare prediction based on kalman filter.  In _Progress in Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2017, Volume 2_ , pp. 191–203. Springer, 2019.
  * Brown (2018) Brown, A. E.  _Ridehail revolution: Ridehail travel and equity in Los Angeles_.  University of California, Los Angeles, 2018.
  * Chan & Shaheen (2012) Chan, N. D. and Shaheen, S. A.  Ridesharing in north america: Past, present, and future.  _Transport reviews_ , 32(1):93–112, 2012.
  * Chen et al. (2015) Chen, L., Mislove, A., and Wilson, C.  Peeking beneath the hood of uber.  In _Proceedings of the 2015 internet measurement conference_ , pp. 495–508, 2015.
  * Cook et al. (2021) Cook, C., Diamond, R., Hall, J. V., List, J. A., and Oyer, P.  The gender earnings gap in the gig economy: Evidence from over a million rideshare drivers.  _The Review of Economic Studies_ , 88(5):2210–2238, 2021.
  * Dong & Leng (2021) Dong, Z. and Leng, M.  Managing on-demand ridesharing operations: Optimal pricing decisions for a ridesharing platform.  _International Journal of Production Economics_ , 232:107958, 2021.
  * Dubal (2023) Dubal, V.  On algorithmic wage discrimination.  _Available at SSRN 4331080_ , 2023.
  * Dubey et al. (2017) Dubey, A., Abhinav, K., Hamilton, M., and Kass, A.  Analyzing gender pay gap in freelancing marketplace.  In _Proceedings of the 2017 ACM SIGMIS conference on computers and people research_ , pp. 13–19, 2017.
  * Dunn et al. (2021) Dunn, M., Munoz, I., and Sawyer, S.  Gender differences and lost flexibility in online freelancing during the covid-19 pandemic.  _Frontiers in sociology_ , 6:738024, 2021.
  * Foong & Gerber (2021) Foong, E. and Gerber, E.  Understanding gender differences in pricing strategies in online labor marketplaces.  In _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_ , pp. 1–16, 2021.
  * Ge et al. (2020) Ge, Y., Knittel, C. R., MacKenzie, D., and Zoepf, S.  Racial discrimination in transportation network companies.  _Journal of Public Economics_ , 190:104205, 2020.
  * Guo et al. (2017) Guo, F., Klauer, S. G., Fang, Y., Hankey, J. M., Antin, J. F., Perez, M. A., Lee, S. E., and Dingus, T. A.  The effects of age on crash risk associated with driver distraction.  _International journal of epidemiology_ , 46(1):258–265, 2017.
  * Hall et al. (2021) Hall, J. V., Horton, J. J., and Knoepfle, D. T.  Pricing in designed markets: The case of ride-sharing, 2021.
  * Hirsh & Lyons (2010) Hirsh, E. and Lyons, C. J.  Perceiving discrimination on the job: Legal consciousness, workplace context, and the construction of race discrimination.  _Law & society review_, 44(2):269–298, 2010\.
  * Horowitz (2021) Horowitz, D. M.  As rideshare prices skyrocket, uber and lyft take a bigger piece of riders’ payments.  2021\.  URL https://missionlocal.org/2021/07/as-rideshare-prices-skyrocket-uber-and-lyft-take-a-bigger-bite-of-the-pie/.
  * Hsieh et al. (2023) Hsieh, J., Adisa, O., Bafna, S., and Zhu, H.  Designing individualized policy and technology interventions to improve gig work conditions.  _arXiv preprint arXiv:2306.12972_ , 2023.
  * Jarrahi & Sutherland (2019) Jarrahi, M. H. and Sutherland, W.  Algorithmic management and algorithmic competencies: Understanding and appropriating algorithms in gig work.  In _Information in Contemporary Society: 14th International Conference, iConference 2019, Washington, DC, USA, March 31–April 3, 2019, Proceedings 14_ , pp. 578–589. Springer, 2019.
  * Jarrahi et al. (2020) Jarrahi, M. H., Sutherland, W., Nelson, S. B., and Sawyer, S.  Platformic management, boundary resources for gig work, and worker autonomy.  _Computer supported cooperative work (CSCW)_ , 29:153–189, 2020.
  * Krzywdzinski & Gerber (2021) Krzywdzinski, M. and Gerber, C.  Between automation and gamification: forms of labour control on crowdwork platforms.  _Work in the Global Economy_ , 1(1-2):161–184, 2021.
  * McCartt et al. (2009) McCartt, A. T., Mayhew, D. R., Braitman, K. A., Ferguson, S. A., and Simpson, H. M.  Effects of age and experience on young driver crashes: review of recent literature.  _Traffic injury prevention_ , 10(3):209–219, 2009\.
  * Nagaraj Rao et al. (2024) Nagaraj Rao, V., Dalal, S., Agarwal, E., Calacci, D., and Monroy-Hernández, A.  Navigating rideshare transparency: Worker insights on ai platform design, 2024.  Manuscript in submission.
  * Pandey & Caliskan (2021) Pandey, A. and Caliskan, A.  Disparate impact of artificial intelligence bias in ridehailing economy’s price discrimination algorithms.  In _Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society_ , pp. 822–833, 2021.
  * Pandit et al. (2019) Pandit, V. N., Mandar, D., Hanawal, M. K., and Moharir, S.  Pricing in ride sharing platforms: static vs dynamic strategies.  In _2019 11th International Conference on Communication Systems & Networks (COMSNETS)_, pp. 208–215. IEEE, 2019.
  * Proussaloglou & Koppelman (1999) Proussaloglou, K. and Koppelman, F. S.  The choice of air carrier, flight, and fare class.  _Journal of Air Transport Management_ , 5(4):193–201, 1999.
  * Roberts & Crane (2023) Roberts, G. and Crane, E.  New yorkers rage about hefty uber prices after ceo stunned by $52 fare.  2023\.  URL https://nypost.com/2023/08/02/new-yorkers-rage-about-hefty-uber-prices-after-ceo-stunned-by-52-fare/.
  * Rosenblat & Stark (2016) Rosenblat, A. and Stark, L.  Algorithmic labor and information asymmetries: A case study of uber’s drivers.  _International journal of communication_ , 10:27, 2016.
  * Shapiro (2018) Shapiro, A.  Between autonomy and control: Strategies of arbitrage in the “on-demand” economy.  _New Media & Society_, 20(8):2954–2971, 2018\.
  * Tziridis et al. (2017) Tziridis, K., Kalampokas, T., Papakostas, G. A., and Diamantaras, K. I.  Airfare prices prediction using machine learning techniques.  In _2017 25th European Signal Processing Conference (EUSIPCO)_ , pp. 1036–1039. IEEE, 2017.
  * Watkins (2023) Watkins, E. A.  Face work: A human-centered investigation into facial verification in gig work.  _Proceedings of the ACM on Human-Computer Interaction_ , 7(CSCW1):1–24, 2023.
  * Woodcock & Johnson (2018) Woodcock, J. and Johnson, M. R.  Gamification: What it is, and how to fight it.  _The Sociological Review_ , 66(3):542–558, 2018\.
  * Yao et al. (2021) Yao, Z., Weden, S., Emerlyn, L., Zhu, H., and Kraut, R. E.  Together but alone: Atomization and peer support among gig workers.  _Proceedings of the ACM on Human-Computer Interaction_ , 5(CSCW2):1–29, 2021.
  * Zhang et al. (2022) Zhang, A., Boltz, A., Wang, C. W., and Lee, M. K.  Algorithmic management reimagined for workers and by workers: Centering worker well-being in gig work.  In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_ , pp. 1–20, 2022.

##  Appendix A Figures

Figure 2:  Distribution of race groups in each hourly wage category.  Figure
3:  Distribution of insurance groups in each hourly wage category.  Figure 4:
Distribution of hourly wage category for each tenure group.  Figure 5:
Distribution of hourly wage category for each working hour group.