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Is the current design of the gig economy fair to contractors?

Gianna Williams

American University

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Is the current design of the gig economy fair to contractors?

Introduction

The tech gig economy has seen a recent spike in employment over the past few years with

nearly 3.9 million drivers currently using the application. (Hellings 2022) (Helling, bond,

& Reliablerider, 2022). Most of those working within the ﬁeld of the tech gig economy

fall within the food delivery/ rideshare services. Uber and Lyft are the main suppliers

in this ride share economy. The beneﬁts of working within the gig economy have enticed

thousands to become drivers for the apps. Drivers are able to choose their own hours of

work, giving them the ﬂexibility of working whenever their schedules allow. Many choose

to do this work part-time apart from their regular jobs, others choose to work full time,

getting paid thousands a month. It is important to note that these workers are considered

independent contractors that work “with” the company rather than “for” the company,

and as a result, no taxes are deducted from their pay. While there are many beneﬁts for

drivers the organization, ‘Gig Workers Rising’, goes against the idea that these beneﬁts

outweigh the unethical working conditions. From this, is the current design of the gig

economy fair to contractors?

Fairness within the tech-gig economy would be impartial treatment for the drivers work-

ing for Uber regardless of their employment status along with a livable wage with those

relying on Uber for income. The ideal design would incorporate drivers and riders into

conversation with engineers. From my research I see that both companies do neither. Lyft

and Uber are popularly considered “ride-sharing” companies, that are software companies

which have designed and commandeered a mobile application to allow drivers to “share”

their personal vehicles to oﬀer rides to others. While some of the functions of these ap-

plications may be considered genius, workers complain that the applications make it very

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diﬃcult for the drivers to express their concerns and complaints to the company and do

not consider their points of view. These applications create unfair labor practices because

the features that are included don’t recognize their unpaid work, fail to incorporate them

in the development and improvement of the application and create an inauthentic space

of community. The current design of the gig economy is enormously proﬁtable for its

software developers; however some feel that the labor of the indispensable contractors

is grossly undervalued. Contractors working for Uber receive a minimum wage however,

the question remains if the amount received equates to the many ﬁnancial obligations

that come with their day to day lives. In contrast to the young professionals developing

the application, Uber’s drivers are much older and have families to provide for, making it

incredibly hard to provide for their families. With Uber’s mission of “building a culture

. . . that emphasizes doing the right thing” Khosrowshahi (n.d.) (Uber,2021) they fail

at their duty of designing a fair equitable application based on drivers’ complaints and

research I have gathered so far. Skeptics however argue against the unfair treatment of

Uber/Lyft Drivers. The incorporation of adding more concern and time for their works

takes away from the proﬁt margin Uber receives leading to less cars on the road and more

money being lossed by Uber.

History of Unions and Advocacy

The collective Gig Workers Rising aims to create a platform for exposing the labor con-

ditions for their workers through shared information and mobilization. The organization

“believes that everyone who works via an app deserves a real voice in their job and the

freedom that comes with good pay and real beneﬁts” Rising (n.d.) (Gig Workers Ris-

ing 2021). Their main goal is to protest for driver unions, livable wages, beneﬁts and

transparency. The importance of this platform dates back to 2018. Much of the organi-

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zation gained traction during the debate regarding California Proposition 22 which made

gig workers and delivery drivers exempt from being labeled as ‘employees.’ This subtle

change excluded these workers from receiving company beneﬁts such as bargaining rights,

unemployment insurance, healthcare and paid time oﬀ. The ﬁnancial impact of this work

distinction is clear, as the companies pay their software engineers over $100K a year in

contrast to the average of $40K paid to their drivers. Sonnemaker (2021) (Sonemaker,

2021). Uber simply prioritizes those working higher paying job rather than those who

generate most of its income.

Although the main beneﬁt for drivers is that they work on their own time, researchers

have found problems with this work model. Research suggests that Uber is losing support

based on their workers not being paid fairly, which has led to a negative perception of

the tech gig economy as unethical and irresponsible.

Invisible Labor

Many researchers studying the tech-gig economy have evaluated the lack of measurement

of emotional and non-physical labor attached to the work these drivers participate in.

that is not fairly compensated for these workers Lee, Kusbit, Metsky, and Dabbish (2015)

Toxtli, Suri, and Savage (2021a) (Dabbish 2015), (Saiph 2021). As mentioned previously,

drivers compared to software engineers don’t receive the same beneﬁts or protections

as those working within the corporate jobs within Uber/Lyft based on Proposition 22.

This pay divide and lack of beneﬁt creates a divide between software engineers and

drivers. Invisibilized labor is unpaid work Crain (n.d.) (Crain 2016) . In an op-ed by

Harvard student Alex Rosenblat, the ones who choose what is proﬁtable in Silicon Valley

are those on executive board seats and those creating algorithms that dictate workers’

(Rosenblat, 2016). Going further, the method for payment of rides is determined by

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the time and distance and not an hourly wage (Rosenblat, 2016). The amount of trips

drivers participate in determines the amount they make on that given day. Unpaid labor

in correspondence to Uber drivers is studied by MIT PhD student Rida Qadri in her

research she found that Gojek -an Indonesian software ridesharing company similar to

Uber- refuses to pay for parking, parking tickets, gas prices and makes drivers calculate

their own income. Skeptics argue that drivers involuntarily participated in this type of

exploitation based on them being recognized as independent contractors not employees.

However, as seen in the protests against Proposition 22 Uber drivers had no choice to

succumb to this type of mistreatment.

Drivers must put on a nice face to appease riders in order to secure tips and positive

ratings. Qadri (2020)(Qadri 2021) Qadri accurately describes this lack of compensation

for emotional labor in her ethnography on drivers. Based on the participants Qadri

studied, she found that their class positionality within society was of low income. This is

important to note as these companies are multi-billion dollar companies but have drivers

who are at or below the poverty line Rosenblat (2021) (Rosenblat 2021). In the article,

Qadri begins to explain the mental gymnastics drivers must go through to increase their

ratings while trying to be on time for the ride. It’s important to note that decision

making algorithms span beyond the United States and have heavily inﬂuenced software

development methodology globally.

Thankfully work is being done to support these tech gig economy workers. Saiph Savage’s

Civic A.I lab discovered that 97 % of the drivers and other contractors have participated

in some form of invisibilized labor (Savage 2021). Dr. Savage created a web plugin to

quantify the invisible labor of participants within the tech gig economy. The approach

used was to record the exact times the user accepted a ‘HIT’ (Human Intelligence Task).

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An HIT is simply something that software developers do not deem as a part of the driver’s

job. The technology created was able to record the time period in which the workers are

active on the HIT page tab through mouse movements, typing and keystrokes. It then

measured the daily income that each worker makes from these HITs recorded by grabbing

information from the crowdsourcing dashboard. Toxtli, Suri, and Savage (2021b). The

importance of this application showcases that invisibilized labor is real within the tech

gig economy and the interviews within this study along with that of Rida Qadri involves

participation from the drivers themselves.

Participatory Design

In addition to concerns regarding unpaid labor concerns I argue that participatory de-

sign should be used to incorporate a better care ethic. Participatory design is an ap-

proach that attempts to include all stakeholders in the design process Elizarova and

Dowd (2020)(Elizarova, 2017). To go further, for someone to have a participatory praxis,

one provides a way of negotiating diﬀerences between people from diﬀerent backgrounds

and creating something that is beneﬁcial to the party giving the information. In reference

to Uber or Lyft, incorporating drivers into brainstorming rooms with software developers

can mitigate the problems experienced by drivers. However Uber and Lyft fail to do this

and continue to use an empathetic approach, which is premised on the idea that they ac-

tually feel what the drivers feel. It is possible that this disconnect is not intentional, and

is brought on by current software development methodologies. An empathic approach

is similar to incorporating participatory design within development; however instead of

listening to the parties they are building for, they are trying to think like them Leonard

and Rayport (2014) (Leonard 1997). This approach can seem useful, however "thinking

like a driver” is not the same as actually being a driver. In addition, one must remember

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the existence of a class divide in which Uber engineers make $100k and drivers make less

than half of that amount (The Salary Project, 2019), meaning that their realities are

extremely diﬀerent. Rida Qadri holds that the inﬂuence of Silicon Valley’s optimization

algorithms heavily impacted the navigation system abroad, resulting in inaccuracy in

terms of driving navigation. The problem here that Qadri ﬁnds is that engineers lack the

knowledge of space and place when designing these navigational features for their drivers.

Qadri also ﬁnds that the inﬂuence of Silicon Valley’s software methodologies inﬂuenced

Gojek to do similar harm by not incorporating their drivers into the brainstorming pro-

cess.

One of the main arguments made by organizers within the gig economy is that there is

a lack of community on these platforms due to the aggressive form of alienation while

working there. Rising (n.d.) (Gig Worker Rising, 2021). Found in Appropriated or

Inauthentic Care in Gig-Economy Platforms: A Psycholinguistic Analysis of Uber and

Lyft Austin Toomb’s research team conducted ﬁeldwork driving for Uber over a course

of 4 months. They documented their thoughts about the driving experience as well as

their communication with Uber’s platform via promotional emails and text messaging.

In the absence of a centralized hub for drivers to communicate, all communication was

one-sided with communication only coming from Uber’s marketing team. In conclusion

they found that although Uber and Lyft are similar, their tone in communication with

drivers is diﬀerent. When explaining ways for drivers to increase rating and income, Lyft

incentivized drivers by using language like this: “ . . . is one of Lyft’s busiest times of

the year, which means your hourly guarantees are supernaturally high this weekend. As

always, it’s a win-win: You’ll either hit the guaranteed amount on your own, or we’ll cover

the diﬀerence for your eligible hours.” /cite . Uber instead used more factual conversation

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such as “You’re eligible to earn up to $25/hour in gross fares. Are you ready to go?

Drive this weekend and meet the minimum requirements to earn a guaranteed hourly

rate ” Toombs, Gray, Zhou, and Light (2018). This is important because it shows the

considerable diﬀerence in communication styles that Uber and Lyft drivers receive. One

can see Lyft’s communication with their drivers is more “comfortable” to read than Uber.

Although it may seem more optimistic and personable it doesn’t mean it is authentic.

What we see here is Lyft capitalizing on more authentic personable language to entice

drivers to drive more, which then in turn increases proﬁt. After leaving the platform,

researchers found that Uber sent messages saying ‘your city needs you” Toombs et al.

(2018). Through the use of individualistic language, Lyft/Uber drivers attempt to connect

with their drivers. But in reality they are still alienating them due to them not having a

one on one relationship, as well as not allowing connection with other drivers. From this

it is shown that Uber/Lyft fails to use a participatory approach of seeing ways to entice

drivers by appreciating inauthentic care circles through their messaging.

In the paper Disentangling Power and Decision-making in Participatory Design, Tone

Bratteteig and Ina Wagner argue that two components of decreasing this power dynamic

between user and designer is through openness and stakeholder participation. Openness

in participatory design allows for a great number of perspectives which promotes free

ﬂowing of thoughts. Stakeholder participation is simply bringing together community

members to discuss ways to ﬁx the application. Uber and Lyft fail to bring openness

for their drivers through their rating system. They fail to tell riders how important high

ratings are for their success. According to Uber and Lyft a 4 out of 5 is considered a

failing grade due to an average rating being lower than 4.7 warranting dismissal from

driving. Stakeholder participation is proven as having failed in several ways. One of the

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most common lack of participation is through decision making in reference to pricing.

The lowest rate a driver can be paid is $5 after calculating the amount of commission

Uber takes out, leaving the driver with $3 proﬁt. Another example of failed stakeholder

participation is seen in the O’Connor v. Uber Technologies case which showcased Uber’s

lack of concern for drivers by labeling them as independent contractors.

Lastly, I would like to express how software development pedagogies inﬂuence the lack of

communication between developers and freelancers within the tech gig economy. Water-

fall, Prototyping, Sprints and Agile are the four main software development methodologies

used in product management. Although there are several diﬀerent methodologies, all of

them follow a similar iteration process: system architecture, implementation, integration,

requirement analysis and deployment. These methods are amazing in building tools how-

ever I raise the question if these software methods are the most suitable for the tech gig

economy.

In Prioritizing the Software Development Methodologies in Online Gig Economy Project

Using Analytic Hierarchy Process, researchers Yoga Aﬁf Nurrahman, Yudho Giri Suc-

ahyo and Arﬁve Gandhiargue argue that “The biggest challenge in gig economy projects

is the separated environment between clients and gig workers. . . It signiﬁcantly reduces

the chance for communication, including for software development. ” Aﬁf Nurrahman,

Giri Sucahyo, and Gandhi (2021). This statement shows the need to change software

development methodologies especially when the software is used to manage people and

their labor. In the study they interviewed software engineers working within the tech gig

economy and asked them if their development methodology was helpful in creating fea-

tures for the platform. They found that requirement clarity from clients and gig workers

was a high priority in development; however, they saw that there was a barrier in making

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this a priority based on proximity to the clients and gig workers. Although prototyping

involves customer engagement as a step in producing features, it continues to fail to bring

participation

Rebuttals

Multi-billion dollar companies such as Uber/Lyft annually spend on $330 million on con-

sulting ﬁrms to create union busting campaigns Wave of union victories suggests union-

busting consultants may have lost their sway (2022). Unionization can be minimized if the

working conditions are improved. Going back to our conversation on participatory design,

one would argue that if engineers were to use this approach many of the problems can be

minimized. The reasoning behind Uber’s lack of community platforms for their drivers is

based on the threat of creating unions. Skeptics however choose to alienate workers and

reduce unionizing through promise making. Promising keeping is a strong tactic and culti-

vates a false sense of community as it will make the worker feel they are in communication

with the company executives https://www.laborlab.us/5

ommon

nion

usting

actics (2022)(5

Common Union-Busting Tactics, 2022). This is represented within the paper mentioned

previously regarding the lack authenticity Uber and Lyft marketing creates with it’s

drivers. To counteract such problems companies try to refute these issues by making

software engineers deliver the food themselves acting as the drivers. The purpose of this

is to make them think as a driver "Kukura" ("2021") (Kukura 2021). However in this

article many Doordash engineers complain about having to participate in the system.

Also skeptics in support of the unfair issues at Uber would be hesitant to the claims

mentioned in this paper based on them not needing to pay taxes. With labeling drivers

as contractors they don’t have to document parts of their income. This lead to a question

if Uber is even contributing to the economy?

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The reasoning for explaining the alternative idea of this exploitation is to show the clear

point: that those working in Silicon Valley want to simply proﬁt oﬀ their drivers and

not improve their quality of working conditions. From the current scholarship found,

researchers show us the need for change in these companies and ways to mitigate this

exploitative harm. Currently workers are creating a community and organizing in appli-

cation via WhatsApp and other social media messaging platforms.

Future Work

Since many workers are aware of this divide, many form communities on other applications

to promote protests and bring awareness. I argue that the best ways for these organizers

to achieve at least one of the demands is through technology that is democratized and

decentralized from apps like Uber, Lyft and Facebook as it will give the workers the power

to bring community if the company is not being participatory.

Conclusion

In conclusion companies like Uber and Lyft fail to use participatory approaches when

engineering their decision making platforms. This failed decision making creates an un-

happy experience for the users they engineer for. If Uber and Lyft’s mission is “Trans-

portation as reliable as running water, everywhere for everyone” they fail to make the

accessibility they seek due to failures toward their drivers. Problems with Uber/Lyft and

other companies will continue to have labor concerns, unions and negative press until

they bring positive participatory design within their software development cycles. To

further explore this concept I believe work in this space can be done in two ways - either

through a decentralized messaging system that allows Uber drivers to organize free of

the fear of surveillance from the developers working on the platform or a participatory

design approach to be implemented within the tech gig economy software development

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methodology. Much of this work is incredibly possible if those in power within Silicon

Valley spend less time on trying to source more proﬁt and productivity and more time

investing in the needs of those whose labor is necessary in order to secure their proﬁt

margin.

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