# A Bottom-Up End-User Intelligent Assistant Approach to Empower Gig Workers
against AI Inequality

Toby Jia-Jun Li1, Yuwen Lu1, Jaylexia Clark1, Meng Chen1, Victor Cox1, Meng
Jiang1, Yang Yang2, Tamara Kay1, Danielle Wood1, Jay Brockman1 toby.j.li,
ylu23, jclark25, mchen24, vcox2, mjiang2, tkay, dwood5<EMAIL_ADDRESS><EMAIL_ADDRESS>1University of Notre Dame, 2Syracuse University

(2022)

###### Abstract.

The growing inequality in gig work between workers and platforms has become a
critical social issue as gig work plays an increasingly prominent role in the
future of work. The AI inequality is caused by (1) the technology divide in
who has access to AI technologies in gig work; and (2) the data divide in who
owns the data in gig work leads to unfair working conditions, growing pay gap,
neglect of workers’ diverse preferences, and workers’ lack of trust in the
platforms. In this position paper, we argue that a bottom-up approach that
empowers individual workers to access AI-enabled work planning support and
share data among a group of workers through a network of end-user-programmable
intelligent assistants is a practical way to bridge AI inequality in gig work
under the current paradigm of privately owned platforms. This position paper
articulates a set of research challenges, potential approaches, and community
engagement opportunities, seeking to start a dialogue on this important
research topic in the interdisciplinary CHIWORK community.

gig workers, AI inequality, intelligent assistants, human-AI collaboration

††journalyear: 2022††copyright: acmlicensed††conference: 2022 Symposium on
Human-Computer Interaction for Work; June 8–9, 2022; Durham, NH,
USA††booktitle: 2022 Symposium on Human-Computer Interaction for Work
(CHIWORK), June 8–9, 2022, Durham, NH, USA††price: 15.00††doi:
10.1145/3533406.3533418††isbn: 978-1-4503-9655-4/22/06††ccs: Human-centered
computing Human computer interaction (HCI)††ccs: Information systems Decision
support systems††ccs: Human-centered computing Computer supported cooperative
work Figure 1. Comparing the current platform-worker pattern with the proposed
approach

##  1\. Introduction

Digitally-mediated gig work, where individual workers provide on-demand work
(e.g., rideshare) through online platforms (usually on mobile apps),
constitutes a significant and growing portion of the workforce. Reports
estimated that about one-third of the U.S. workforce participate in the gig
economy (Mitic, 2021), while around one percent of the U.S. workforce (1.6
million people) directly work on app-based gig work platforms (of Labor
Statistics, 2017; Jackson et al., 2017; Farrell et al., 2018). The importance
of gig work is expected to continue to rise in the future of work (Gray,
2019). The labor participation in the gig economy skyrocketed during the
COVID-19 pandemic—prior research found a 25% increase in average labor supply
on gig economy platforms in response to the economic impact of COVID-19 (Cao
et al., 2020). The efforts to empower gig workers have drawn a lot of
attention due to recent high-profile events such as the pass of CA Prop. 22
that regulated the roles and benefits of app-based gig workers (General,
2021), the repeal of rules by the U.S. Dept. of Labor to prevent “employer
misclassification” of workers (Segal, 2021), and the strikes and protests of
gig workers around the nation (Rising, 2021).

Compared to conventional contingent work, a key unique characteristic of
digitally-mediated gig work is the ubiquity of artificial intelligence (AI)
involvement. For example, in rideshare, AI systems match drivers with
customers and determine drivers’ pay. However, the use of AI contributes to
the widening inequality in gig work between platforms and workers: these AI
systems are “owned” by platforms and designed to optimize for platforms’ best
interests. In contrast, individual gig workers’ unique goals, preferences, and
constraints are often neglected in the existing gig work platforms. The AI
systems in these platforms lack transparency and interpretability for users to
understand the algorithmic recommendations (Cameron et al., 2021; Dillahunt et
al., 2017b; Gray, 2019). Many platforms are also known to have systematic
gender, racial, and socioeconomic biases in their AI algorithms (Hannák et
al., 2017; Thebault-Spieker et al., 2017, 2015; Dillahunt et al., 2017a).
Meanwhile, high technical barriers prevent workers from accessing AI
technologies that work in their best interests (Lieberman et al., 2006a),
leading to a technology divide (Warschauer, 2004). It requires significant
expertise to “program” an AI system that can model complex gig work situations
and provide work planning support that accommodates the diverse needs of
workers.

Another key contributing factor to the AI inequality in gig work is the data
divide in who has access to and control over data (Delacroix and Lawrence,
2019; Andrejevic, 2014). Simply put, gig work platforms have access to data
from all drivers as well as the technologies to use those data while drivers
do not. Platforms, through their applications, collect and access data from
all drivers and customers, which are used to model worker behaviors, task
characteristics, and customer demands. Workers do not have access to such
collective data—each individual worker, at most, can only self-track the data
of themselves. Therefore, even if workers had access to the technology, their
lack of data access would prevent them from using AI systems for work planning
assistance in their best interest. This issue has recently been recognized as
a key challenge toward a more equitable future of gig work through initiatives
such as Digital Worker Inquiry (Edinburgh Centre for Data and Institute,
2022).

In this position paper, we argue that a practical approach to bridge the AI
inequality in gig work is through the design, development, and deployment of a
bottom-up network of end-user intelligent assistants. Each assistant is paired
with a worker, collects work-related data from them, and shares the data
within the network to close the data divide. With the available data, the
assistant can model and predict the market and empower workers to optimize
their work according to their personal goals, preferences, and contexts,
reducing the technology/AI divide.

The envisioned approach is translational with the potential to make immediate
impacts on gig worker communities within the current paradigm of privately-
owned gig work platforms. Compared with other directions e.g., developing more
equitable pricing and task allocation algorithms for the platform, developing
a more explainable and transparent worker’s app for the platform, the
technology required for this approach is worker-centric without requiring any
cooperation, access to additional data, or software access from the platform.
This is important to the practicality of the approach, as we do not expect
platforms to be completely cooperative in the effort to reduce AI inequality
in gig work because it might work against their financial interests.

This research agenda can also, through the deployment and data collection
process, contribute to fundamental understandings in characterizing and
measuring AI inequality in gig work and explore worker needs and strategies in
effective human-AI collaboration between gig workers and AI-enabled
assistants. For broader impacts, these findings can raise awareness of AI
inequality in gig work, providing empirical evidence and implications for
labor advocacy, worker movements, and legislative and policy efforts in market
regulation and worker rights protection in gig work. Beyond making immediate
direct impacts within the current gig work paradigm of privately-owned
platforms, findings from this approach (e.g., how workers collaborate with
each other through data sharing and AI-enabled facilitation in a decentralized
network) may also contribute to the foundation for a long-term direction in
designing new community-owned decentralized gig work platforms, an idea that
many in the research community are excited about.

##  2\. Background

###  2.1. The Role of Gig Work Platforms

An important factor to consider for AI inequality specifically in gig work is
the role of gig work platforms (Vallas and Schor, 2020). From a labor
economics point of view, the main purpose of these platforms is to reduce
“search frictions” by helping match those looking for workers (e.g., riders in
ridesharing) and those looking for work (e.g., rideshare drivers) (Pissarides,
2000; Prassl, 2018; Hoffmann, 2019). The platform plays an intermediary role
where it matches workers with customers and sets prices using data and
algorithms they have (Prassl, 2018; Hoffmann, 2019; Wood et al., 2019).

While the platform provides values as an intermediary (and provides other
services e.g. mechanisms for quality control and infrastructures for handling
payments), it takes unfair advantages from the lack of transparency in the
matching and the pricing process through its monopoly on data and algorithms
(Van Doorn and Badger, 2020; Hoffmann, 2019; Rosenblat and Stark, 2016): the
platform has exclusive access to the data of labor supplies and demands and
the predictive model of prices that each party is willing to accept. When a
match (e.g., between a rider and a driver) is made, neither party knows the
price that the other party is paying/receiving, nor information of the other
possible riders and drivers in the market. This allows the platform to
optimize its task assignment and pricing algorithms in the best interests of
the platform (Rosenblat, 2018; Wood et al., 2019; Rosenblat and Stark, 2016).
The proposed bottom-up end-user intelligent assistant approach seeks to bridge
the AI inequality in gig work by breaking the platform’s monopoly on data and
algorithms, enabling workers to utilize data-driven AI assistance that works
in their best interests.

###  2.2. Prior Studies of Inequality in Gig Work

Previous work has demonstrated that gig work is different from work in the
formal economy in three major ways. First, gig work is precarious with
individuals unable to control how many “gigs” will be available and whether
they will be able to accept each incoming gig. Second, gig work is typically
low-wage labor (Prassl, 2018). Many sociologists argue that gig work depresses
wages in order to create exploitative conditions that make workers
increasingly reliant on gig work (Graham and Woodcock, 2018; Prassl, 2018).
Lastly, gig workers are not considered employees and thus are not protected by
federal employment law (Graham et al., 2020; Graham and Woodcock, 2018;
Prassl, 2018). Overall, previous research shows that gig work depresses wages,
is precarious, and is unprotected.

Inequalities in gig work are ubiquitous. For example, a 2017 study reported a
significant correlation between perceived gender and race of workers with
worker evaluations, which harms employment opportunities of workers (Hannák et
al., 2017). Aside from worker evaluations, which are more subjective,
systematic biases are still prevalent—Most physical gig work tasks are
geographic in nature. Geographic principles interact with platform designs to
create systematic biases in which services like Uber are more effective in
dense, high socioeconomic status (SES) areas than in low-SES areas (Thebault-
Spieker et al., 2017), similar to patterns previously found in peer-production
contents like Wikipedia and OpenStreetMap (Johnson et al., 2016). A 2019 study
identified unfair income distributions on a ride-hailing platform between
“successful“ and “unsuccessful drivers” (Sühr et al., 2019). Previous studies
reported strong tension between the two parties due to workers’ struggle
against power asymmetries maintained by the platform, since they are
marginalized in the decision-making process of the platform design (Ma et al.,
2018; Rosenblat and Stark, 2016, 2016). The lack of transparency and control
resulted in a feeling of dehumanization for workers (Möhlmann and Zalmanson,
2017). These tensions have led to worker movements in gig work ecosystems
(Kinder et al., 2019).

While toolkits and techniques have been developed to help AI system owners and
operators to make their algorithms fairer in a top-down fashion (Bellamy et
al., 2019; Holstein et al., 2019), it is unclear how much gig work platforms
have adopted them due to the power inequality between the platform and
workers. Therefore, our proposed approach seeks to address the inequality and
biases in gig work using a bottom-up approach instead, empowering each
individual worker with better AI-enabled decision-making and planning
assistance that works in their best interests towards their unique personal
goals.

###  2.3. Intelligent Assistants for Work Planning and Optimization

The end-user intelligent assistant we envision in this position paper will be
an instance of end-user programmable systems that allow users without
technical expertise to “program” them to help users with decision-making,
planning, and automation. Research on intelligent assistants that collaborate
with users in the planning and decision-making of complex problems has been
around for decades since early systems like Trips (Ferguson and Allen, 1998)
and Trains (Ferguson et al., 1996). A key approach for effective human-AI
interaction in such assistants is to support mixed-initiative interaction
(Horvitz, 1999; Allen et al., 1999), where an agent infers the goals and needs
of users with uncertainties, employs multi-turns of interactions with users to
resolve key uncertainties, and provides information that augments the user’s
capability to make decisions instead of making decisions for the users.
Several existing intelligent assistants such as (Li et al., 2019; Li et al.,
2017, 2020b, 2020a) have been designed and developed in the domain of general-
purpose task automation on smartphone apps without a particular focused work
domain. Two long-standing challenges in mixed-initiative assistants are
particularly relevant to the approach we discuss in this position paper: (1)
enabling workers without technical expertise to specify their goals and needs;
and (2) effectively explaining the assistant’s results and recommendations in
the context of use.

Prior studies (Trippas et al., 2019) identified the needs for work-related
task planning support by office information workers. Various intelligent
assistants have also been built to support the planning and optimization of
specific work tasks such as meeting scheduling (Cranshaw et al., 2017) and
writing (Greer et al., 2016). Recent initiatives such as Driver’s Seat
(Cooperative, 2022a) piloted a co-op approach where drivers voluntarily
contribute their data and an app help drivers analyze the data to answer
questions such as the most productive time to work, the best platform to work
on, and whether the driving strategy is working. However, Driver’s Seat uses a
centralized approach (that relies on selling worker-contributed data to third
parties) and provides limited AI-enabled planning and optimization support to
drivers. To achieve the vision in this position paper, as a research
community, we need to investigate the unique goals, needs, preferences, and
work contexts of gig workers and design new features, interfaces, and
interaction strategies for a personal intelligent assistant to address them.

##  3\. Formative Study Summary

We have conducted several prior studies on inequality in gig work that
informed the objectives of this research. Through a collaboration with New
York Worker Institute, we analyzed online reviews from digitally-mediated
platform gig workers addressing their work conditions. We found that the
negative impact of automated matching between workers and consumers was a
major source of complaints from gig workers. In another formative study, we
quantitatively analyzed the relationship between racial residential
segregation, income inequality, and labor participation in the platform
economy across all U.S. counties between 2010 and 2018 using a fixed effect
panel model (Halaby, 2004). The result, on a national level, confirms the
findings of previous studies on the negative impact of the designs of the gig
work platform algorithms on the inequality between high-SES and low-SES areas
and workers from different gender and racial groups (Hannák et al., 2017; Sühr
et al., 2019; Thebault-Spieker et al., 2015). The results of both studies
provide evidence for the negative impact of AI inequality on workers in
existing gig work platforms.

##### Worker perception of AI inequality

To inform the proposed approach, we have conducted six semi-structured
interviews with rideshare drivers and two brainstorm sessions with labor
organizers at Chicago Rideshare Advocates (Alliance, 2022; Advocates, 2022).
The findings of these studies confirmed workers’ interest in using an
intelligent assistant owned by workers to help them plan their work and
joining a bottom-up data-sharing network of drivers to bridge the “data
divide” in gig work. Workers reported anecdotes of how they suspected the
platform-owned task allocation and pricing algorithms were working “against
them” to maximize the profit of the platform by e.g., preventing them to hit
bonuses by stopping assigning them tasks when they are close to a milestone
(Platforms like Uber sometimes run promotions that award drivers a bonus when
they complete X tasks that fulfill certain criteria within a given time frame)
and charging high surge prices from customers without raising workers’ pay.
Some workers have also reported the use of some primitive methods to share
information between themselves, such as messaging each other on platforms such
as WhatsApp or Facebook Messenger to share work-related information e.g., a
high demand at the airport or anecdotes of high surge pricing near some areas.

##### Unmet worker needs

Through these engagements, we have identified challenges in lowering the
barrier of use, making the algorithmic results interpretable, and
accommodating the diverse needs and contexts of workers. Although most gig
workers, including rideshare drivers, are legally independent contractors, the
algorithmic management systems in existing gig work platforms provide workers
with little space in choosing tasks that they want to do according to their
preferences, constraints, and personal values. For example, workers are not
able to view all the available tasks on the market. Instead, the platform
pushes individual task recommendations to them for which workers need to
either accept or decline on the spot. In most markets (some states or cities
mandate worker access to task information when making decisions), workers
cannot view important characteristics of the task (e.g., ride destination, pay
amount) either prior to accepting and will even be “punished” by the algorithm
for decline too many tasks sometimes. This “recommendation” or “task
allocation” algorithm is completely opaque to the workers—workers have no
means to configure it according to their unique needs or goals.

The workers reported a wide range of goals, constraints, and work contexts
currently not accommodated by the platform. For example, a worker had
significant caregiving duties at home, therefore she preferred to stay within
a certain distance when driving for the rideshare service so she could get
home quickly if needed. Goal-wise, most workers probably see to maximize their
monetary profit i.e., the expected pay from the task minus the expected cost
(e.g., gas for rideshare drivers), but some drivers might have different
personal values e.g., optimizing the carbon emission or devaluing tasks that
involve being stuck in traffic, even if they may pay well, for emotional
reasons. Lastly, some work contexts also contribute to personal value, but are
neglected by existing platform algorithms. For example, there are drivers who
drive hybrid cars that have better gas mileage on local streets than on
highways, which is the opposite of most other vehicles. but this factor was
likely completely neglected in the platform’s algorithm for task allocation.

##  4\. The Envisioned Approach

As shown in Figure 1, the key technical component in the approach that we
envision is a bottom-up network of intelligent assistants. Each gig worker
runs a Gig Worker Assistant app locally on the same phone used to run the gig
work platform. The worker specifies their unique personal values, constraints,
and work contexts in the assistant. The assistant can collect task information
(e.g., the time needed, distance, proposed pay) of each task recommended by
the gig work platform from the GUI of the gig work platform app. The worker
also (optionally) shares their real-time location with the server. In our
vision, this network of intelligent assistants should achieve the following
goals:

  1. (1)

Collect the worker’s work-related data and share it with the assistants of
other workers in the same network in real-time;

  2. (2)

Empower the gig worker to specify their unique goals, constraints, and
context;

  3. (3)

Model worker behaviors and customer demands using data shared in the network
and help the worker optimize work planning based on their own goals,
constraints, and context;

  4. (4)

Provide the worker with decision-making support in an explainable interface
that fits into the worker’s existing work context.

###  4.1. The Bottom-Up Approach for Bridging the Data Divide

The adoption of AI in existing gig work platforms is top-down, leading to the
data divide between platforms and workers. Currently, a centralized AI model,
owned by the platform, collects data from all workers and customers and then
uses the data to make algorithmic decisions, such as pricing and task
assignments. The lack of access to data for workers prevents them from having
AI that “works for them”, contributing to the AI inequality.

In response, our envisioned approach is bottom-up and worker-centric without
requiring any additional data or software access from the platform. As shown
in Figure 1, each worker has an intelligent assistant. A network of assistants
from a group of workers share data that each assistant collects of task
supply, task demand, and task characteristics within the network, allowing
predictive modeling of the market that enables work-planning support for gig
workers. This bottom-up network approach does not require the buy-in of the
platform, scales up easily, and allows flexible configurations and preferences
from workers.

Figure 2. The workflow of the envisioned Gig Work Assistant

####  4.1.1. Predictive Modeling User Behaviors and Task Characteristics

Computational models empowered by AI and machine learning can capture complex
patterns in human behavior and make accurate predictions in many applications
(Jiang et al., 2014; Jiang et al., 2015; Jiang et al., 2016). The objective of
predictive modeling in the envisioned approach is to create computational
models that use the collected (both current and past) data of a smaller group
of workers’ behaviors and task characteristics, such as job duration,
source/destination locations, price, and task acceptance/declining to predict
those of the entire market in real-time. Predictive models will be deployed to
provide recommendations to workers for planning optimization.

Previous work on representation learning models for user behavioral modeling
(e.g., CalendarGNN (Wang et al., 2020)) used graph neural networks for
automatically learning vector representations (or known as “embeddings”) of
users from their spatiotemporal behavior data. The architecture of such models
incorporates two networked structures. One is a tripartite network of items,
sessions, and locations. The other is a hierarchical calendar network of hour,
week, and weekday nodes. It first aggregates embeddings of location and items
into session embeddings via the tripartite network and then generates user
embeddings from the session embeddings via the calendar structure. The user
embeddings preserve spatial patterns and temporal patterns of a variety of
periodicities (e.g., hourly, weekly, and weekday patterns).

CalendarGNN was shown to be effective in identifying user preferences and
predicting their behavior on social media sites, online shopping websites, and
news reading mobile applications. A potential challenge of applying
CalendarGNN and many other machine learning models on task demand prediction
for gig workers is the sparsity, noise, and uncertainty of the collected data.
Interpretable and robust machine learning models are needed for reliable user
behavior modeling in this domain.

Figure 3. A screenshot of the data crawler Crepe

####  4.1.2. Techniques for Data Crawling

To show the feasibility of this approach, we have already developed and tested
a practical technique for crawling task information from the GUIs of existing
gig work platforms. A crawler (named Crepe) runs on each worker’s phone as an
Android background service that uses the GUI-based instrumentation method,
which extracts data directly from the GUIs of existing apps of gig work
platforms. Whenever a piece of information is displayed in the gig work
platform for the worker to see, the crawler can extract it. It also listens to
the worker’s input into the platform app (e.g., accepting a task). This method
provides compatibility with different gig work platforms and different makes
and models of devices (Android only) that workers use.

This GUI-based approach for data crawling in third-party apps uses a graph-
based model presented (Li et al., 2018) that grounds a semantic entity (e.g.,
the estimated pay of a gig task) to a relational query on a graph
representation of a third-party app GUI, allowing robust retrieval of the
entity value from the GUI in future contexts. It can also embed the semantics
of higher-level task intents (e.g., compare the pay of two gig work sessions)
of GUI-based tasks.

A limitation of the GUI-crawling approach is that it cannot collect the
customer payment needed for modeling worker-vs-platform biases, as the
customer-paid price is not visible to workers in most gig work platforms.
However, this limitation can be complemented by a simulated query method
(Thebault-Spieker et al., 2015). When a worker receives a task, a back-end
server can simulate a GPS location at the starting location of the task, run
the corresponding customer’s app (e.g., the customer version of the Uber app),
and obtain a price quote for an identical task. This method allows the
assistant’s model to estimate the user payment for a task using the actual
pricing algorithm (which is often kept a secret by the platform).

###  4.2. Empowering Workers with End-User-Programmable Assistants

As summarized earlier, in our envisioned approach, an end-user-programmable
assistant will be paired up with each gig worker and empower them with AI-
enabled task planning and decision-making support. This assistant is end-user-
focused in three ways: it is owned by an individual worker, is programmed by
an individual worker, and communicates its results directly to an individual
worker. The worker “teaches” the assistant their unique goals, constraints,
and contexts (which form the personal value of workers). The assistant then
utilizes the predictive model of the market, made with data shared by the
assistants of other drivers within the network, to provide workers with
suggestions on whether to accept a task, the areas to go to for the next task,
and the hours to work that optimize for the worker’s personal value. The
ownership gives the worker complete control over the assistant and ensures
that the assistant works in the worker’s best interest, unlike the task
recommendations provided by platform-owned algorithms. Designing such an
assistant presents the following research opportunities and challenges:

##### Specifying worker goals, constraints, and contexts

For the assistant to determine the personal value of a recommended task for a
worker, the worker needs to configure the assistant by specifying their unique
goals, constraints, and context. Because most of the workers do not have
significant technical expertise, it is likely not feasible for them to
directly define and configure these parameters for the assistant. A system
needs to guide them through the process. The main challenge is twofold: the
system has to first help the worker resolve their own uncertainty and figure
out what they want through a mixed-initiative (Horvitz, 1999) process. Then,
the system should enable the worker to express their intents in a formalized
way that the assistant can understand and execute. We expect that a
combination of multiple interaction modalities and strategies (Sarmah et al.,
2020; Oviatt, 1999), such as natural language programming (Lieberman and Liu,
2006; Lieberman et al., 2006a), direct manipulation (Hutchins et al., 1985),
and programming by example (Lieberman et al., 2006b; Cypher and Halbert, 1993)
techniques would be useful here.

##### Displaying and explaining results

The assistant should display the result of work planning assistance in a way
that is appropriate for the background and the work context of gig workers.
Any recommendations for accepting or declining a task or going to a new area
etc. should be accompanied by explanations of why it contributes to the
worker’s personal value in an easy-to-understand format. The format should
also accommodate the worker’s unique context of use. For example, rideshare
drivers may need to be able to consume such information while driving where
their attention is occupied with limited cognitive capacity available.

##### Transparency in inequality

Another opportunity in the assistant is to help workers better understand the
inequality in existing gig work platforms. With the data collected from the
network, the assistant should be able to estimate the level of individual task
bias (i.e., whether the pay was equitably decided for an individual worker
according to the task allocation—When a worker receives a new task
recommendation, the model predicts how much other drivers would be paid for a
similar task), group bias (i.e., whether the pay was equitably decided for a
demographic group of gig workers according to the task allocation), and the
worker-platform bias (i.e., if there exists an alternative task allocation
that results in a higher total worker profit with a potentially lower platform
profit).

###  4.3. Worker Personal Values

The core of the end-user programming approach is to allow users to specify
their personal values in gig work. In any decision making, a value system
denotes the degree of importance of something for a person, referring to
desirable goals and transcending specific actions and situations (Schwartz,
2012). In a work setting, previous studies have found that different personal
values of workers lead to differences in their work values and the priorities
in the meaning of work (Ros et al., 1999) (e.g., good salary, work conditions,
authority to make decisions, interesting and varied work, social contact with
people). Therefore, in the design of a personal intelligent assistant for work
planning, it is important that the assistant not only optimizes for a single
value (usually the monetary gain), but also considers the weights of other
possible personal values (which can vary from one person to another). It is
worth noting that a previous study identified that gig workers such as
rideshare drivers not only perform body and temporal labor but also emotional
labor as a response to the expectation of “pleasing the passenger” due to the
intermediation of quantitative scores in the rating system (Raval and Dourish,
2016).

Two research opportunities are present here. First, more research is needed to
empirically understand the work values of gig workers. Many workers value the
flexibility in deciding when to work and when to stop work with little or no
notice. Some rideshare drivers also enjoy the social aspects of their job,
e.g., small talk with riders, while others do not (Lutz et al., 2018; Glöss et
al., 2016). Additional factors such as e.g., how much does a driver value not
being stuck in traffic emotionally beyond the value of time and gas, how
should an intelligent assistant trade-off between a high expected income vs.
high expected stability (i.e. low variance) in income may also play a role.
Results from such a need-finding study can decide the space of expressiveness
that an intelligent assistant should support for gig workers to specify their
personal values.

Another research opportunity is to design interfaces for workers to specify
their values. A direct manipulation interface is a possible option, but the
user’s uncertainty and lack of awareness about their values may pose potential
issues. An example-based interface where workers compare tasks of
characteristics and choose which ones they prefer can also be useful so the
system can infer worker values from their preferences on concrete example
tasks.

##  5\. Discussion

###  5.1. A “Fight Fire with Fire” Approach111We thank the anonymous Reviewer
3 for coming up with this analogy.

This position paper advocates for a “fight fire with fire” approach—to address
the inequality arising from platforms attempting to dictate data access in a
centralized top-down fashion and optimizing for platforms’ interests in
pricing and task allocation decisions using AI models, we seek to democratize
data access in a decentralized bottom-up fashion and empower individual works
to similarly optimize for their own interests in task planning using AI
models.

Compared with other data-driven efforts that seek to address the same problem,
such as Driver’s Seat (Cooperative, 2022a), Shipt (Calacci and Pentland,
2022), and Gigbox (Calacci, 2022), our envisioned intelligent agent approach
is complementary by providing information and recommendations with a finer
granularity and in a more timely fashion. Existing approaches mostly focus on
providing post-hoc worker support. They often first collect work-related data
from workers and later display aggregated statistics (e.g., net earnings,
expense per mile, average tips), often in a dashboard, to workers to help them
analyze their work records. By showing such data and helping users visualize
e.g., how earnings change over time and differ for tasks of different
characteristics, these systems can help workers reflect on their work practice
and adjust their higher-level work strategies to optimize their earnings.
Little support is provided to workers to translate such high-level strategies
into specific decisions they need to make in the field. In comparison, our
envisioned intelligent agent provides real-time recommendations to workers for
making lower-level specific decisions such as whether to accept the current
task and which area to go to when idling. In addition to relying on analytics
from aggregated past data like existing ones, our envisioned approach also
predicts the current market conditions using both historical data and the
sample of real-time current data collected from the agent network.

The complementary relationship between these two types of tools can be found
in studies of tooling practices by online gig workers (e.g., crowd workers on
platforms such as Amazon Mechanical Turk) (Williams et al., 2019). Workers use
both (1) real-time tools that “automate finding work and accepting work based
on specific criteria” and “augment the cognition of workers”; and (2)
administrative tools that “track and document work history.”

The design decision of providing task-level recommendations and utilizing
real-time market predictive models exemplifies our “fight fire with fire”
approach. The platform leverages large-scale real-time data sensing and
complicated predictive models to make specific fine-granular decisions on
pricing and task allocation. To “balance the playfield”, we believe that
workers ought to be equally equipped with a similar level of technological
support.

###  5.2. Balancing Worker Agency and Inclusion in Customer Access

So far we have been mostly discussing the AI inequality between platforms and
gig workers with the focus on empowering workers with access to data and AI
technologies. However, an important topic, the inclusion in customer access to
gig work services, has been left out of the scope of the discussion. When
workers have access to AI-enabled assistants that help them with work planning
so they are more likely to get the types of task that they desire, a possible
side effect is that customers whose tasks are considered “undesirable” by many
workers may be affected by longer wait times, higher prices, or in the worst
case, exclusions from the service.

Note that such biases in service availability for customers have already been
identified in existing platform-owned algorithmic systems in previous studies
such as (Thebault-Spieker et al., 2015, 2017), but the use of algorithmic
optimization by workers might amplify these biases. Even if the system does
not, for example, allow drivers to use protective classes in their
configuration (e.g., one does not want to serve customers of color),
demographic variables can still be proxied using e.g., geographic variables.
Therefore, any recommendations and planning assistance provided by the
assistant should be validated and balanced for access and inclusion using the
predictive task model (i.e., if the use of a specified personal value model
would result in a significant shift in the equality of accessing services for
customers). Persuasive and de-biasing techniques can also be developed and
deployed to reduce unconscious biases and conscious discriminatory practices
in gig workers’ use of AI assistance.

###  5.3. Power in Collective Worker Organizations

In the envisioned approach, a group of workers are connected by data sharing
facilitated through each worker’s intelligent assistant for a purpose in the
collective interest of the group—reducing the AI inequality in gig work. This
could be a form of labor organization. The entire group benefits from the
participation of each worker, as the contributed data enable more accurate
predictive modeling of the market, resulting in better performance for each
worker’s intelligent assistant in work planning optimization. However, to some
extent, gig workers in the same area are also “competing” with each other for
tasks. It would be interesting to, through community-engaged participatory
design processes and deployment studies, investigate worker behaviors in such
an organization and how different design decisions made in the system can
affect their behaviors.

On the optimization aspect, we have so far discussed the assistant’s role in
helping individual workers optimize for their personal values in their work
planning. However, thanks to the formation of the worker organization, there
is also the opportunity for all assistants in the network to coordinate
together for the collective interest of the group (Vandaele, 2018). Such
data/AI-facilitated and “loosely connected” (compared with a traditional labor
union) groups might still have the potential to increase the bargaining power
of workers. For example, if tasks of a specific category (e.g., originated
from some regions) are systematically underpaid, the broad use of assistants
may significantly cut the labor supply, and subsequently, affect the service
availability for these tasks. As a result, the platform, either as a result of
automated algorithmic adjustments or manual intervention, may raise the pay
for these tasks. The use of intelligent assistant networks can also be used as
an infrastructure to facilitate the organization of more formal labor protests
and strikes (Flanagan and Walker, 2021; Nissim and Simon, 2021; Maffie, 2020),
especially at a finer granularity (e.g., assistants that are configured to
recommend declining all tasks that fit specific criteria as part of a labor
movement).

All these interdisciplinary challenges would be a valuable opportunity for the
HCI community to engage with sociologists, labor economists, behavioral
economists, and researchers from other adjacent disciplines in designing and
studying such complex socio-technical systems.

###  5.4. Integration with Existing Systems for Immediate Real-World Impacts

As discussed, the design of the envisioned technical approach prioritizes the
integration with existing gig work platforms for immediate real-world impacts.
The technology should be compatible with major gig work platforms (e.g., Uber,
Lyft) and should incorporate inputs from the community in the design process
for real-world use in the actual work context. Specifically, the technical
approach assumes only having access to the data from a smaller group of gig
workers without requiring any data access provided by the platform operator.

As a result, there is a lower barrier to making the technology publicly
available and planning a field study to (1) evaluate the feasibility,
robustness, and ecological validity of the system features; (2) measure the
usefulness of our system in real-life scenarios; and (3) study the
characteristics of how users use the system. The real-world adoption of the
approach is also more feasible in the near term. In comparison, any approach
that requires the cooperation of the platform operator (e.g., developing more
equitable pricing and task allocation algorithms for the platform (Chakraborty
et al., 2017; Patro et al., 2020) and developing an explainable and
transparent worker’s app for the platform) is more difficult because the goal
of reducing AI inequality in gig work contradicts directly their interests.

###  5.5. Racial Biases in Gig Work

The rise of the gig economy has particularly affected racially minoritized
workers. Empirical research found that “of survey respondents who earn more
than 40% of their income from on-demand work, a whopping 67% identify as
racial minorities” (Prassl, 2018). This is alarming given the fact that when
the survey was administrated, racial minorities only made up 36% of the US
population. In other words, in 2018, racial minorities were over-represented
in the gig economy by nearly double their actual size in the US population. A
source of racial inequality in gig work is racialized surveillance, where
workers in racial minority groups, for example, are rated lower, receive more
complaints, and earn less tips (Rosenblat et al., 2016, 2017; Ge et al.,
2020). Lower ratings and higher customer complaints can affect the tasks
assigned to workers and, in some cases, lead to workers being deactivated from
the platform. The geographic inequality in rideshare, as identified in
(Thebault-Spieker et al., 2015, 2017), also contributes to the racial
inequality for both workers and customers.

An emphasis of the envisioned research approach should be investigating how to
reduce racial inequality as a part of its effort to bridge the technology
divide and the data divide in gig work. In the design process, researchers
must ensure the participation of workers from diverse racial, gender,
cultural, and SES backgrounds, as well as the representation of their values
in design decisions (Dillahunt and Malone, 2015; Harrington et al., 2019).

###  5.6. Implications for Future Community-Owned Cooperative Platforms

Another proposed approach to address inequality in gig work from the research
community is to design community-owned, worker-owned, or publicly-owned gig
work platforms that deviate from the prevailing paradigm where gig work
platforms are owned and operated by for-profit corporations (e.g., efforts
like The Drivers Cooperative (Cooperative, 2022b), RideFair (RideFair, 2022),
and research in platform cooperativism (Scholz and Schneider, 2017)). In fact,
in the space of shared economy in digital products, worker-owned platforms
such as Stocksy (United, 2022) (a platform cooperative of photographers) have
been shown to be feasible and effective in ensuring equality and fair pay for
its members. The open-source software movement and the success of Wikipedia
are also examples of success in the decentralized community-owned approach to
coordinating, moderating, and managing large-scale group work. However, the
adoption of this approach in physical (i.e., offline tasks such as food
delivery and rideshare) gig work remains challenging for various reasons, such
as the high initial investment needed for setting up the infrastructure, the
significant overhead in regulatory compliance, worker background checking, and
the challenge in work quality control. Although there have been small-scale
successful examples such as Arcade City Austin (O’Halloran, 2022; Austin,
2022) (an online “forum” that matches independent drivers with riders through
Facebook Page posts), scaling up these efforts seems challenging.

Our envisioned approach complements the efforts for future community-owned
cooperative platforms. Through the deployment of a worker-owned network of
intelligent assistants within the ecosystem of corporate-owned gig work
platforms, researchers can study worker behaviors, experiment with design
strategies, and gain valuable understanding of the current platforms and their
inequality in a much shorter timeframe without the barrier of large initial
efforts in developing the infrastructure of an entirely new gig work platform
and promoting it to reach the critical mass required for study and for the
platform to be self-sustainable. The empirical findings, design implications,
and interaction strategies learned from our envisioned approach can then be
applied to the design and development of future community-owned cooperative
platforms.

###  5.7. Broader Impacts in Community Engagement and Labor Advocacy

This research agenda presents ample opportunities for community engagement and
labor advocacy. For example, in the formative stage, we partnered with Chicago
Rideshare Advocates (Alliance, 2022; Advocates, 2022), a Chicago-based labor
advocacy organization with around 3,000 rideshare drivers from a wide range of
racial, cultural, and socioeconomic backgrounds. The goal of bridging the AI
inequality in gig work aligns with the group’s aim of advocating for fairness
in ridesharing. Organizations such as Chicago Rideshare Advocates can leverage
their deep community roots to play an important role in potential
participatory design workshops, data collections, and field deployments. Their
access to real gig workers on platforms such as Uber and Lyft would allow
researchers to better understand the work contexts on those platforms and
ensure the practicality of technological innovations. The data and findings
resulting from the research can also support labor advocacy efforts for
workers’ rights by holding platforms more accountable for their AI algorithms,
advocating for policy development, and persuading the companies to make their
platforms fairer for the workers.

##  6\. Conclusion

This position paper discusses a bottom-up end-user intelligent assistant
approach to empower gig workers against AI inequality. We argue that the
discussed approach would be a practical way to empower gig workers to bridge
the “data divide” and “technology divide” in gig work that can result in
immediate real-world impacts while contributing significant findings and
implications for characterizing AI inequality in gig work and designing future
cooperative gig work platforms owned by communities.

The envisioned research direction addresses a complex socio-technical
challenge that requires methods, techniques, and theories across multiple
disciplines both within computing (e.g., Machine Learning, Data Mining, Human-
Computer Interaction) and beyond (e.g., Design, Economics, Psychology, and
Sociology). We hope this position paper can spark dialogues spanning multiple
disciplines on this increasingly important research topic for the future of
work.

###### Acknowledgements.

This research was supported in part by a Google Cloud Research Credit Grant, a
Google Research Scholar Award, and a Lucy Scholar Award. We would like to
thank our anonymous reviewers for their feedback. We are grateful to Nitesh
Chawla, Daniel Graff, Lori Simmons, Zheng Zhang, Zheng Ning, and Simret
Gebreegziabher for useful discussions.

## References

  * (1)
  * Advocates (2022) Chicago Rideshare Advocates. 2022\.  https://www.facebook.com/chicagorideshareadvocates/
  * Allen et al. (1999) James F. Allen, Curry I Guinn, and Eric Horvtz. 1999\.  Mixed-initiative interaction.  _IEEE Intelligent Systems and their Applications_ 14, 5 (1999), 14–23.
  * Alliance (2022) Chicago Gig Alliance. 2022\.  https://chicagogigalliance.org/
  * Andrejevic (2014) Mark Andrejevic. 2014\.  Big data, big questions— the big data divide.  _International Journal of Communication_ 8 (2014), 17.
  * Austin (2022) Arcade City Austin. 2022\.  Arcade City Austin.  https://www.facebook.com/groups/ArcadeCityAustin/about/
  * Bellamy et al. (2019) R. K. E. Bellamy, K. Dey, M. Hind, S. C. Hoffman, S. Houde, K. Kannan, P. Lohia, J. Martino, S. Mehta, A. Mojsilović, S. Nagar, K. Natesan Ramamurthy, J. Richards, D. Saha, P. Sattigeri, M. Singh, K. R. Varshney, and Y. Zhang. 2019.  AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias.  _IBM Journal of Research and Development_ 63, 4/5 (2019), 4:1–4:15.  https://doi.org/10.1147/JRD.2019.2942287
  * Calacci (2022) Dan Calacci. 2022\.  Gigbox.  https://gigbox.media.mit.edu/app/
  * Calacci and Pentland (2022) Dan Calacci and Alex (Sandy) Pentland. 2022.  Bargaining With The Black-Box Designing and Deploying Worker-Centric Tools to Audit Algorithmic Management. In _Preprint_.
  * Cameron et al. (2021) Lindsey Cameron, Angele Christin, Michael Ann DeVito, Tawanna R. Dillahunt, Madeleine Elish, Mary Gray, Rida Qadri, Noopur Raval, Melissa Valentine, and Elizabeth Anne Watkins. 2021.  _“This Seems to Work”: Designing Technological Systems with The Algorithmic Imaginations of Those Who Labor_.  ACM, New York, NY, USA.  https://doi.org/10.1145/3411763.3441331
  * Cao et al. (2020) Xinyu Cao, Dennis Zhang, and Lei Huang. 2020.  The impact of COVID-19 pandemic on gig economy labor supply.  _NYU Stern School of Business_ (2020).
  * Chakraborty et al. (2017) Abhijnan Chakraborty, Aniko Hannak, Asia J Biega, and Krishna Gummadi. 2017.  Fair sharing for sharing economy platforms. In _Fairness, Accountability and Transparency in Recommender Systems-Workshop on Responsible Recommendation_.
  * Cooperative (2022a) Driver’s Seat Cooperative. 2022a.  Driver’s Seat Cooperative.  https://driversseat.co/
  * Cooperative (2022b) The Drivers Cooperative. 2022b.  The Drivers Cooperative.  https://drivers.coop
  * Cranshaw et al. (2017) Justin Cranshaw, Emad Elwany, Todd Newman, Rafal Kocielnik, Bowen Yu, Sandeep Soni, Jaime Teevan, and Andrés Monroy-Hernández. 2017.  Calendar.Help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop. In _Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems_ (Denver, Colorado, USA) _(CHI ’17)_. ACM, New York, NY, USA, 2382–2393.  https://doi.org/10.1145/3025453.3025780
  * Cypher and Halbert (1993) Allen Cypher and Daniel Conrad Halbert. 1993.  _Watch what I do: programming by demonstration_.  MIT press.
  * Delacroix and Lawrence (2019) Sylvie Delacroix and Neil D Lawrence. 2019.  Bottom-up data Trusts: disturbing the ‘one size fits all’approach to data governance.  _International data privacy law_ 9, 4 (2019), 236–252.
  * Dillahunt et al. (2017a) Tawanna R. Dillahunt, Sheena Erete, Roxana Galusca, Aarti Israni, Denise Nacu, and Phoebe Sengers. 2017a.  Reflections on Design Methods for Underserved Communities. In _Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing_ (Portland, Oregon, USA) _(CSCW ’17 Companion)_. ACM, New York, NY, USA, 409–413.  https://doi.org/10.1145/3022198.3022664
  * Dillahunt and Malone (2015) Tawanna R. Dillahunt and Amelia R. Malone. 2015.  The Promise of the Sharing Economy among Disadvantaged Communities. In _Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems_ (Seoul, Republic of Korea) _(CHI ’15)_. ACM, New York, NY, USA, 2285–2294.  https://doi.org/10.1145/2702123.2702189
  * Dillahunt et al. (2017b) Tawanna R. Dillahunt, Xinyi Wang, Earnest Wheeler, Hao Fei Cheng, Brent Hecht, and Haiyi Zhu. 2017b.  The Sharing Economy in Computing: A Systematic Literature Review.  _Proc. ACM Hum.-Comput. Interact._ 1, CSCW, Article 38 (Dec. 2017), 26 pages.  https://doi.org/10.1145/3134673
  * Edinburgh Centre for Data and Institute (2022) Culture & Society Edinburgh Centre for Data and Edinburgh Futures Institute. 2022\.  Digital Worker Inquiry: Data, Solidarity, Leverage.  https://digitalworkerinquiry.com/
  * Farrell et al. (2018) Diana Farrell, Fiona Greig, and Amar Hamoudi. 2018\.  The online platform economy in 2018: Drivers, workers, sellers, and lessors.  _JPMorgan Chase Institute_ (2018).
  * Ferguson and Allen (1998) George Ferguson and James F Allen. 1998.  TRIPS: An integrated intelligent problem-solving assistant. In _Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98)_. 567–572.
  * Ferguson et al. (1996) George Ferguson, James F Allen, and Bradford W Miller. 1996\.  TRAINS-95: Towards a Mixed-Initiative Planning Assistant.. In _Proceedings of The Third International Conference on artificial Intelligence Planning Systems (AIPS-96)_. 70–77.
  * Flanagan and Walker (2021) Frances Flanagan and Michael Walker. 2021.  How can unions use Artificial Intelligence to build power? The use of AI chatbots for labour organising in the US and Australia.  _New Technology, Work and Employment_ 36, 2 (2021), 159–176.
  * Ge et al. (2020) Yanbo Ge, Christopher R Knittel, Don MacKenzie, and Stephen Zoepf. 2020.  Racial discrimination in transportation network companies.  _Journal of Public Economics_ 190 (2020), 104205.
  * General (2021) California Attorney General. 2021\.  Proposition 22 Official Title and Summary.  https://voterguide.sos.ca.gov/propositions/22/title-summary.htm
  * Glöss et al. (2016) Mareike Glöss, Moira McGregor, and Barry Brown. 2016\.  Designing for Labour: Uber and the On-Demand Mobile Workforce. In _Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems_ (San Jose, California, USA) _(CHI ’16)_. ACM, New York, NY, USA, 1632–1643.  https://doi.org/10.1145/2858036.2858476
  * Graham and Woodcock (2018) Mark Graham and Jamie Woodcock. 2018.  Towards a fairer platform economy: introducing the Fairwork Foundation.  _Alternate Routes_ 29 (2018), 242–253.
  * Graham et al. (2020) Mark Graham, Jamie Woodcock, Richard Heeks, Paul Mungai, Jean-Paul Van Belle, Darcy du Toit, Sandra Fredman, Abigail Osiki, Anri van der Spuy, and Six M Silberman. 2020.  The Fairwork Foundation: Strategies for improving platform work in a global context.  _Geoforum_ 112 (2020), 100–103.
  * Gray (2019) Mary L. Gray. 2019\.  _Ghost work : how to stop Silicon Valley from building a new global underclass_.  Houghton Mifflin Harcourt, Boston.
  * Greer et al. (2016) Nick Greer, Jaime Teevan, and Shamsi T Iqbal. 2016.  An introduction to technological support for writing. In _ACM Conference on Human Factors in Computing Systems_ , Vol. 12.
  * Halaby (2004) Charles N Halaby. 2004\.  Panel models in sociological research: Theory into practice.  _Annu. Rev. Sociol._ 30 (2004), 507–544.
  * Hannák et al. (2017) Anikó Hannák, Claudia Wagner, David Garcia, Alan Mislove, Markus Strohmaier, and Christo Wilson. 2017.  Bias in Online Freelance Marketplaces: Evidence from TaskRabbit and Fiverr. In _Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing_ (Portland, Oregon, USA) _(CSCW ’17)_. ACM, New York, NY, USA, 1914–1933.  https://doi.org/10.1145/2998181.2998327
  * Harrington et al. (2019) Christina Harrington, Sheena Erete, and Anne Marie Piper. 2019\.  Deconstructing Community-Based Collaborative Design: Towards More Equitable Participatory Design Engagements.  _Proc. ACM Hum.-Comput. Interact._ 3, CSCW, Article 216 (nov 2019), 25 pages.  https://doi.org/10.1145/3359318
  * Hoffmann (2019) Anna Lauren Hoffmann. 2019\.  Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse.  _Information, Communication & Society_ 22, 7 (2019), 900–915.
  * Holstein et al. (2019) Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé, Miro Dudik, and Hanna Wallach. 2019\.  _Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?_ ACM, New York, NY, USA, 1–16.  https://doi.org/10.1145/3290605.3300830
  * Horvitz (1999) Eric Horvitz. 1999\.  Principles of Mixed-Initiative User Interfaces. In _Proceedings of the SIGCHI Conference on Human Factors in Computing Systems_ (Pittsburgh, Pennsylvania, USA) _(CHI ’99)_. ACM, New York, NY, USA, 159–166.  https://doi.org/10.1145/302979.303030
  * Hutchins et al. (1985) Edwin L Hutchins, James D Hollan, and Donald A Norman. 1985\.  Direct manipulation interfaces.  _Human–computer interaction_ 1, 4 (1985), 311–338.
  * Jackson et al. (2017) Emilie Jackson, Adam Looney, and Shanthi Ramnath. 2017\.  The rise of alternative work arrangements: Evidence and implications for tax filing and benefit coverage.  _Office of Tax Analysis Working Paper_ 114 (2017).
  * Jiang et al. (2015) Meng Jiang, Alex Beutel, Peng Cui, Bryan Hooi, Shiqiang Yang, and Christos Faloutsos. 2015\.  A general suspiciousness metric for dense blocks in multimodal data. In _2015 IEEE International Conference on Data Mining_. IEEE, 781–786.
  * Jiang et al. (2014) Meng Jiang, Peng Cui, Fei Wang, Xinran Xu, Wenwu Zhu, and Shiqiang Yang. 2014\.  Fema: flexible evolutionary multi-faceted analysis for dynamic behavioral pattern discovery. In _Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining_. 1186–1195.
  * Jiang et al. (2016) Meng Jiang, Peng Cui, Nicholas Jing Yuan, Xing Xie, and Shiqiang Yang. 2016.  Little is much: Bridging cross-platform behaviors through overlapped crowds. In _Thirtieth AAAI Conference on Artificial Intelligence_.
  * Johnson et al. (2016) Isaac Johnson, Yilun Lin, Toby Jia-Jun Li, Andrew Hall, Aaron Halfaker, Johannes Schöning, and Brent Hecht. 2016.  Not at Home on the Range: Peer Production and the Urban/Rural Divide. In _In Submission to CHI 2016_.
  * Kinder et al. (2019) Eliscia Kinder, Mohammad Hossein Jarrahi, and Will Sutherland. 2019.  Gig Platforms, Tensions, Alliances and Ecosystems: An Actor-Network Perspective.  _Proc. ACM Hum.-Comput. Interact._ 3, CSCW, Article 212 (Nov. 2019), 26 pages.  https://doi.org/10.1145/3359314
  * Li et al. (2017) Toby Jia-Jun Li, Amos Azaria, and Brad A. Myers. 2017\.  SUGILITE: Creating Multimodal Smartphone Automation by Demonstration. In _Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems_ _(CHI ’17)_. ACM, New York, NY, USA, 6038–6049.  https://doi.org/10.1145/3025453.3025483
  * Li et al. (2020a) Toby Jia-Jun Li, Jingya Chen, Haijun Xia, Tom M. Mitchell, and Brad A. Myers. 2020a.  Multi-Modal Repairs of Conversational Breakdowns in Task-Oriented Dialogs. In _Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology_ _(UIST 2020)_. ACM.  https://doi.org/10.1145/3379337.3415820
  * Li et al. (2018) Toby Jia-Jun Li, Igor Labutov, Xiaohan Nancy Li, Xiaoyi Zhang, Wenze Shi, Tom M. Mitchell, and Brad A. Myers. 2018.  APPINITE: A Multi-Modal Interface for Specifying Data Descriptions in Programming by Demonstration Using Verbal Instructions. In _Proceedings of the 2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2018)_.
  * Li et al. (2020b) Toby Jia-Jun Li, Tom Mitchell, and Brad Myers. 2020b.  Interactive Task Learning from GUI-Grounded Natural Language Instructions and Demonstrations. In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations_. ACL, Online, 215–223.  https://doi.org/10.18653/v1/2020.acl-demos.25
  * Li et al. (2019) Toby Jia-Jun Li, Marissa Radensky, Justin Jia, Kirielle Singarajah, Tom M. Mitchell, and Brad A. Myers. 2019.  PUMICE: A Multi-Modal Agent that Learns Concepts and Conditionals from Natural Language and Demonstrations. In _Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology_ _(UIST 2019)_. ACM.  https://doi.org/10.1145/3332165.3347899
  * Lieberman and Liu (2006) Henry Lieberman and Hugo Liu. 2006.  Feasibility studies for programming in natural language.  In _End user development_. Springer, 459–473.
  * Lieberman et al. (2006a) Henry Lieberman, Fabio Paternò, Markus Klann, and Volker Wulf. 2006a.  End-user development: An emerging paradigm.  In _End user development_. Springer, 1–8.
  * Lieberman et al. (2006b) Henry Lieberman, Fabio Paternò, Markus Klann, and Volker Wulf. 2006b.  End-User Development: An Emerging Paradigm.  In _End User Development_. Springer, Dordrecht, 1–8.  https://doi.org/10.1007/1-4020-5386-X_1
  * Lutz et al. (2018) Christoph Lutz, Gemma Newlands, and Christian Fieseler. 2018\.  Emotional labor in the sharing economy. In _Proceedings of the 51st Hawaii international conference on system sciences_.
  * Ma et al. (2018) Ning F. Ma, Chien Wen Yuan, Moojan Ghafurian, and Benjamin V. Hanrahan. 2018.  _Using Stakeholder Theory to Examine Drivers’ Stake in Uber_.  ACM, New York, NY, USA, 1–12.  https://doi.org/10.1145/3173574.3173657
  * Maffie (2020) Michael David Maffie. 2020\.  The role of digital communities in organizing gig workers.  _Industrial Relations: A Journal of Economy and Society_ 59, 1 (2020), 123–149.
  * Mitic (2021) Igor Mitic. 2021\.  24+ Crucial Gig Economy Statistics and Facts | Fortunly.com.  https://fortunly.com/statistics/gig-economy-statistics/
  * Möhlmann and Zalmanson (2017) Marieke Möhlmann and Lior Zalmanson. 2017.  Hands on the wheel: Navigating algorithmic management and Uber drivers’. In _Autonomy’, in proceedings of the international conference on information systems (ICIS), Seoul South Korea_. 10–13.
  * Nissim and Simon (2021) Gadi Nissim and Tomer Simon. 2021.  The future of labor unions in the age of automation and at the dawn of AI.  _Technology in Society_ 67 (2021), 101732.
  * of Labor Statistics (2017) US Bureau of Labor Statistics. 2017\.  _Contingent and Alternative Employment Arrangements_.
  * O’Halloran (2022) Kelly O’Halloran. 2022\.  How Arcade City has continued to grow, despite a few run-ins with the law | Built In Austin.  https://www.builtinaustin.com/2017/12/06/arcade-city-where-are-they-now
  * Oviatt (1999) Sharon Oviatt. 1999\.  Ten Myths of Multimodal Interaction.  _Commun. ACM_ 42, 11 (Nov. 1999), 74–81.  https://doi.org/10.1145/319382.319398
  * Patro et al. (2020) Gourab K Patro, Abhijnan Chakraborty, Niloy Ganguly, and Krishna Gummadi. 2020.  Incremental fairness in two-sided market platforms: On smoothly updating recommendations. In _Proceedings of the AAAI Conference on Artificial Intelligence_ , Vol. 34. 181–188.
  * Pissarides (2000) Christopher A Pissarides. 2000\.  _Equilibrium unemployment theory_.  MIT press.
  * Prassl (2018) Jeremias Prassl. 2018\.  _Humans as a service: The promise and perils of work in the gig economy_.  Oxford University Press.
  * Raval and Dourish (2016) Noopur Raval and Paul Dourish. 2016.  Standing Out from the Crowd: Emotional Labor, Body Labor, and Temporal Labor in Ridesharing. In _Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing_ (San Francisco, California, USA) _(CSCW ’16)_. ACM, New York, NY, USA, 97–107.  https://doi.org/10.1145/2818048.2820026
  * RideFair (2022) RideFair. 2022.  RideFair - Rideshare platform for driver owned cooperatives.  https://ridefair.io
  * Rising (2021) Gig Worker Rising. 2021\.  Gig Workers Rising - News Coverage.  https://gigworkersrising.org/get-informed/news/
  * Ros et al. (1999) Maria Ros, Shalom H Schwartz, and Shoshana Surkiss. 1999\.  Basic individual values, work values, and the meaning of work.  _Applied psychology_ 48, 1 (1999), 49–71.
  * Rosenblat (2018) Alex Rosenblat. 2018\.  _Uberland: How Algorithms Are Rewriting the Rules of Work_.  University of California Press.
  * Rosenblat et al. (2016) Alex Rosenblat, Karen EC Levy, Solon Barocas, and Tim Hwang. 2016\.  Discriminating tastes: Customer ratings as vehicles for bias.  _Data & Society_ (2016), 1–21.
  * Rosenblat et al. (2017) Alex Rosenblat, Karen EC Levy, Solon Barocas, and Tim Hwang. 2017\.  Discriminating tastes: Uber’s customer ratings as vehicles for workplace discrimination.  _Policy & Internet_ 9, 3 (2017), 256–279.
  * Rosenblat and Stark (2016) Alex Rosenblat and Luke Stark. 2016.  Algorithmic labor and information asymmetries: A case study of Uber’s drivers.  _International Journal of Communication_ 10 (2016), 27.
  * Sarmah et al. (2020) Ritam Sarmah, Yunpeng Ding, Di Wang, Cheuk Yin Phipson Lee, Toby Jia-Jun Li, and Xiang ’Anthony’ Chen. 2020.  Geno: a Developer Tool for Authoring Multimodal Interaction on Existing Web Applications. In _Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology_ _(UIST 2020)_.
  * Scholz and Schneider (2017) Trebor Scholz and Nathan Schneider. 2017.  _Ours to hack and to own: The rise of platform cooperativism, a new vision for the future of work and a fairer internet_.  OR books.
  * Schwartz (2012) Shalom H Schwartz. 2012\.  An overview of the Schwartz theory of basic values.  _Online readings in Psychology and Culture_ 2, 1 (2012), 2307–0919.
  * Segal (2021) Edward Segal. 2021\.  Labor Department Rescinds Trump Administration’s Policy On Gig Workers.  https://www.forbes.com/sites/edwardsegal/2021/05/05/labor-department-rescinds-trump-administrations-policy-on-gig-workers/ Section: Leadership Strategy.
  * Sühr et al. (2019) Tom Sühr, Asia J. Biega, Meike Zehlike, Krishna P. Gummadi, and Abhijnan Chakraborty. 2019\.  Two-Sided Fairness for Repeated Matchings in Two-Sided Markets: A Case Study of a Ride-Hailing Platform. In _Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining_ (Anchorage, AK, USA) _(KDD ’19)_. ACM, New York, NY, USA, 3082–3092.  https://doi.org/10.1145/3292500.3330793
  * Thebault-Spieker et al. (2017) Jacob Thebault-Spieker, Loren Terveen, and Brent Hecht. 2017\.  Toward a Geographic Understanding of the Sharing Economy: Systemic Biases in UberX and TaskRabbit.  _ACM Trans. Comput.-Hum. Interact._ 24, 3, Article 21 (April 2017), 40 pages.  https://doi.org/10.1145/3058499
  * Thebault-Spieker et al. (2015) Jacob Thebault-Spieker, Loren G. Terveen, and Brent Hecht. 2015\.  Avoiding the South Side and the Suburbs: The Geography of Mobile Crowdsourcing Markets. In _Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing_ (Vancouver, BC, Canada) _(CSCW ’15)_. ACM, New York, NY, USA, 265–275.  https://doi.org/10.1145/2675133.2675278
  * Trippas et al. (2019) Johanne R. Trippas, Damiano Spina, Falk Scholer, Ahmed Hassan Awadallah, Peter Bailey, Paul N. Bennett, Ryen W. White, Jonathan Liono, Yongli Ren, Flora D. Salim, and Mark Sanderson. 2019\.  Learning About Work Tasks to Inform Intelligent Assistant Design. In _Proceedings of the 2019 Conference on Human Information Interaction and Retrieval_ (Glasgow, Scotland UK) _(CHIIR ’19)_. ACM, New York, NY, USA, 5–14.  https://doi.org/10.1145/3295750.3298934
  * United (2022) Stocksy United. 2022\.  Authentic Stock Photography.  https://www.stocksy.com//,https://www.stocksy.com//
  * Vallas and Schor (2020) Steven Vallas and Juliet B Schor. 2020.  What do platforms do? Understanding the gig economy.  _Annual Review of Sociology_ 46 (2020), 273–294.
  * Van Doorn and Badger (2020) Niels Van Doorn and Adam Badger. 2020.  Platform capitalism’s hidden abode: producing data assets in the gig economy.  _Antipode_ 52, 5 (2020), 1475–1495.
  * Vandaele (2018) Kurt Vandaele. 2018\.  Will trade unions survive in the platform economy? Emerging patterns of platform workers’ collective voice and representation in Europe.  _Emerging Patterns of Platform Workers’ Collective Voice and Representation in Europe (June 19, 2018). ETUI Research Paper-Working Paper_ (2018).
  * Wang et al. (2020) Daheng Wang, Meng Jiang, Munira Syed, Oliver Conway, Vishal Juneja, Sriram Subramanian, and Nitesh V Chawla. 2020.  Calendar graph neural networks for modeling time structures in spatiotemporal user behaviors. In _Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining_. 2581–2589.
  * Warschauer (2004) Mark Warschauer. 2004\.  _Technology and social inclusion: Rethinking the digital divide_.  MIT press.
  * Williams et al. (2019) Alex C. Williams, Gloria Mark, Kristy Milland, Edward Lank, and Edith Law. 2019.  The Perpetual Work Life of Crowdworkers: How Tooling Practices Increase Fragmentation in Crowdwork.  _Proc. ACM Hum.-Comput. Interact._ 3, CSCW, Article 24 (nov 2019), 28 pages.  https://doi.org/10.1145/3359126
  * Wood et al. (2019) Alex J Wood, Mark Graham, Vili Lehdonvirta, and Isis Hjorth. 2019\.  Good gig, bad gig: autonomy and algorithmic control in the global gig economy.  _Work, Employment and Society_ 33, 1 (2019), 56–75.