# Rideshare Transparency: Translating Gig Worker Insights on AI Platform
Design to Policy

Varun Nagaraj Rao  Center for Information Technology Policy, Princeton
UniversityUSA<EMAIL_ADDRESS>,  Samantha Dalal  University of Colorado
BoulderUSA<EMAIL_ADDRESS>,  Eesha Agarwal  Princeton
UniversityUSA<EMAIL_ADDRESS>,  Dana Calacci<EMAIL_ADDRESS>and  Andrés Monroy-Hernández<EMAIL_ADDRESS>Center for Information
Technology Policy, Princeton UniversityUSA

###### Abstract.

Rideshare platforms exert significant control over workers through algorithmic
systems that can result in financial, emotional, and physical harm. What steps
can platforms, designers, and practitioners take to mitigate these negative
impacts and meet worker needs? In this paper, through a novel mixed methods
study combining a LLM-based analysis of over 1 million comments posted to
online platform worker communities with semi-structured interviews of workers,
we thickly characterize transparency-related harms, mitigation strategies, and
worker needs while validating and contextualizing our findings within the
broader worker community. Our findings expose a transparency gap between
existing platform designs and the information drivers need, particularly
concerning promotions, fares, routes, and task allocation. Our analysis
suggests that rideshare workers need key pieces of information, which we refer
to as indicators, to make informed work decisions. These indicators include
details about rides, driver statistics, algorithmic implementation details,
and platform policy information. We argue that instead of relying on platforms
to include such information in their designs, new regulations that require
platforms to publish public transparency reports may be a more effective
solution to improve worker well-being. We offer recommendations for
implementing such a policy.

##  1\. Introduction

Rideshare platforms match people seeking transportation (riders) with
transportation providers (drivers). They do so by constructing algorithmically
managed “marketplaces” (Uber, 2023d) where they match riders’ demand with
drivers’ supply. While algorithmic management enables rideshare platforms to
operate at scale, it can cause workers financial, emotional, and physical harm
through gamification, omnipresent surveillance, and opaque algorithmic
decisions (Woodcock and Johnson, 2018; Dubal, 2023; Dzieza, 2021; Figueroa et
al., 2021; Rosenblat and Stark, 2016; Shapiro, 2018; Watkins, 2023). Workers,
in an attempt to regain control and develop infrastructural competency and
coping mechanisms, engage in resistance strategies (e.g., canceling rides),
switching apps (e.g., transitioning from Uber to Lyft), and gaming the system
(e.g., activating the app once inside surge regions) (Möhlmann and Zalmanson,
2017; Lee et al., 2015).

This dynamic also subjects drivers to information asymmetry, i.e., an
imbalance in the information accessible to rideshare platforms and drivers
(Rosenblat and Stark, 2016). As digital environment designers, rideshare
platforms create an asymmetric information environment and then exploit it
using mechanisms of “soft control” (Viljoen et al., 2021; Tomassetti, 2016;
Deleuze, 1995) that frequently disadvantage workers and prevent them from
making fully informed decisions (Shapiro, 2018; Rosenblat and Stark, 2016).
For instance, platforms manipulate driver supply using surge pricing bonuses
that increase driver pay during periods of high customer demand (Shapiro,
2018). Concretely, this asymmetry manifests to drivers through design choices
that impact the transparency—the understandability and accessibility—of
important information (Bommasani et al., 2023).

Although transparency in platform design is key to how drivers experience
information asymmetry and its harms, its role in shaping working conditions
and workers’ coping strategies is under-studied. Previous research on
algorithmic management has examined the harms and associated mitigation
strategies used by drivers around finances (Watkins, 2022), physical safety
(Qadri and D’Ignazio, 2022), and social connection (Yao et al., 2021), but has
not explored these same issues around transparency or how it manifests in
platform interface design. Other work that examines worker needs due to
algorithmic management uses speculative design scenarios and manufactured
exercises rather than evaluating existing platform design (Hsieh et al.,
2023b; Alvarez De La Vega et al., 2022; Zhang et al., 2022, 2023). These
limitations leave the causes of these harms, and the corresponding steps that
platform designers can take to mitigate them, unclear.

In this paper, we investigate how a lack of transparency in platform design,
particularly regarding AI and algorithmic decisions, impacts drivers’ working
conditions and coping strategies. Specifically, our research questions are
what are the transparency-related harms, mitigation strategies and needs of
rideshare workers?, and what policy solutions may mitigate worker harms and
address their needs? Our contributions are as follows:

  1. (1)

We comprehensively identify rideshare workers’ transparency concerns,
mitigation strategies and needs

  2. (2)

We obtain empirical evidence from workers through a unique mixed methods study
blending in-depth interviews and a LLM-based analysis of rideshare community
discussions on Reddit

  3. (3)

We offer a detailed roadmap for a rideshare transparency report, a policy
solution that we argue could address driver transparency needs and mitigate
some of the harms we identify.

To uncover transparency-related harms, mitigation strategies, and needs of
rideshare workers, we conducted in-person interviews with (N=9) Uber and Lyft
drivers in the Northeast United States. Drawing on recent methods in human-
centered explainable AI (Ehsan and Riedl, 2020; Kim et al., 2023; Suresh et
al., 2021), we developed an annotation-based activity. This activity involved
workers annotating screenshots of app UIs during interviews, providing
insights into worker-generated design improvements based on their real
interactions with the platform apps they work for. To validate findings on a
larger scale, we analyzed over 1 million submissions and comments from Uber
and Lyft driver discussions on Reddit. Using a novel LLM-based method, taking
a step further than traditional topic modeling (Gillies et al., 2022; Abdul et
al., 2018; Cambo and Gergle, 2022; Muller et al., 2016; Baumer et al., 2017),
we extracted transparency-related harms. The capabilities of LLMs, beyond
conventional topic modeling, allowed for more nuanced and contextually
relevant identification of these harms in the dataset. Our analysis confirmed
that transparency harms identified from Reddit data corroborate with interview
findings. establishing their persistence across time.

Our results highlight a transparency gap between the information offered by
existing platform designs and the information drivers believe they need to
make informed decisions and maintain their well-being. Specifically, we find
that drivers experience transparency-related harms when utilizing or impacted
by four key features of rideshare platforms: promotions, fares, routes, and
task allocation, prompting a desire for more information in these contexts.
Beyond the design recommendations made by drivers we document in our findings,
we argue for a policy solution to close this gap. We propose regularly-
released rideshare transparency reports, drawing inspiration from successful
precedents by social media platforms and AI model regulation (Heikkilä, 2023;
Diresta et al., 2022; Bommasani et al., 2023; Edelson et al., 2021; Silverman,
2023). These reports would encompass crucial indicators such as individual
ride statistics, driver history, and details about platform algorithms’ inputs
and outputs. Access to this information couldsignificantly improve worker
well-being and address major harms. We offer a detailed roadmap for what
content such reports should include and some strategies for how regulatory
agencies could implement them, contributing to the ongoing discourse on
rideshare platform regulation and design. We note here that this research is
situated within the U.S. context, drawing solely upon data from English-
language forums on Reddit and driver interviews in the North-East U.S. While
the findings provide structural insights into the rideshare industry that can
impact any driver, the policy contributions are primarily geared towards the
U.S. setting.

##  2\. Related Work

###  2.1. The Rideshare Economy and its Impacts on Workers

The rideshare economy is a prominent sector of the larger gig economy.
Rideshare platforms, like Uber and Lyft, connect people seeking transportation
(riders) with transportation providers (drivers). Drivers share their private
vehicles with riders and earn income from completing a ride. Rideshare
platforms and their impacts on workers have been well documented within the
CSCW literatures. While some CSCW research on rideshare platforms propose
design recommendations to improve outcomes for workers, few papers take into
account the feasibility of their recommendations given the existing legal
regimes platforms operate within and how they shape platforms’ operating
logics (Dalal et al., 2023). In this paper, we show how empirical studies of
rideshare platform features can inform targeted policy solutions that can
potentially alter the legal regimes platforms operate within. To situate our
solution, we first provide a brief summary of the existing state of CSCW and
HCI research on rideshare platforms, how these fields approach improving
outcomes for rideshare workers, and precedents for using policy to increase
transparency in other fields.

####  2.1.1. The Rideshare Economy

The rideshare economy, a prominent sector of the larger gig economy, has
become ingrained in the transportation and mobility landscape of the United
States. In the US alone, over 36% of adults have used rideshare platforms and
an estimated 5% of the American workforce has driven for a rideshare platform
for work (Anderson et al., 2021). Despite the rideshare economy’s established
presence in the transportation sector, there is a lack of detailed rideshare
driver workforce data because it is difficult to track these workers due to
their classification as independent contractors (Abraham et al., 2018). While
the Bureau of Labor Statistics made some initial strides in documenting key
information about the gig economy (of Labor Statistics, 2017), reliable
documentation of working conditions for rideshare workers, such as rate of
workplace injuries, average wages, and demographic make-up remains difficult
as there is no stable mechanism to systematically track independent
contractors in the gig economy (Zipperer et al., 2022; Donovan et al., 2016;
Abraham et al., 2018). A lack of data about labor conditions in the rideshare
industry contributes to regulatory shortcomings (Hawley, 2018). However,
researchers have chipped away at this lack of visibility into rideshare
working conditions through rich qualitative studies with drivers to understand
their experiences and the harms they face.

####  2.1.2. Known Harms and Mitigation Strategies in Rideshare Work

Prior research extensively documents that rideshare workers facing financial,
psychological, and physical harm due to opaque algorithmic decisions,
surveillance, and hazardous conditions. Rideshare platforms’ gamification
leads to long work hours and uncertain profits, pushing drivers to work faster
and harder which can result in physical harm (Dzieza, 2021; Figueroa et al.,
2021; Krzywdzinski and Gerber, 2021). Omnipresent surveillance causes
psychological harm (Rahman, 2021; Shapiro, 2018). Deactivation 111In gig work,
deactivation is the same as being fired. Workers lose access to their accounts
and cannot earn money on the platform until the account is reinstated. without
recourse results in financial harm (Watkins, 2023; Schwartz et al., 2023). Our
large-scale analysis of over one million Reddit comments, combined with
interview data, reveals the persistence of these harms at scale, surpassing
the scope of previous small-scale studies (Rosenblat and Stark, 2016; Sannon
et al., 2022; Yao et al., 2021; Watkins, 2022; Ma et al., 2018). While the
harms of the rideshare industry have been well-documented in qualitative work,
few studies have explored what specifically should be changed within platform
interfaces to mitigate harms and how policy can be used as a tool to enforce
changes to platform interfaces. Our study seeks to mitigate information
asymmetries in rideshare work by identifying features within the platforms
that need to change, and calling for policymakers to enforce public data
disclosures.

###  2.2. Calls for Transparency

####  2.2.1. Transparency Requirements in Rideshare Industry:

Researchers, journalists, and worker advocates highlight that the opaque
nature of algorithmic management in gig work causes financial, physical, and
psychological harm due to a lack of transparency, increasing the precarity of
such work (Dubal, 2023; Wingard, Forbes; Sainato, The Guardian). Despite this,
prior research lacks proposals for concrete solutions to enhance transparency.
Responding to this gap, rideshare driver organizations, backed by legislative
efforts like Colorado’s SB24-75 (Assembly, 2024) and Washington State’s House
Bill 2076 (House, 2022), demand clearer insights into task allocation,
earnings, and deactivation processes. The need for transparency is echoed in
robust discussions on driver forums like Reddit and UberPeople. While prior
research demonstrates that lack of transparency exacerbates the harms
rideshare drivers face (Rahman, 2021; Shaikh et al., [n. d.]; Woodcock, 2020;
Qadri and D’Ignazio, 2022; Zhang et al., 2023; Yao et al., 2021; Watkins,
2022), their specific transparency harms and needs are under-explored. Our
study, which analyzes over a million comments from rideshare driver subreddits
and conducts in-depth in-person interviews, not only confirms known rideshare
worker harms but also identifies new transparency specific ones which persist
over time, underscoring an urgent need for transparency solutions.

Policy solutions are essential to address the needs of rideshare workers
effectively, especially since platforms typically lack the incentive to
prioritize these needs over their profit-maximization objectives (Zuboff,
2015). Prior policy recommendations made in CSCW research has been broad in
its specifications and not implemented into policy practice (Hsieh et al.,
2023a; Van Doorn et al., 2023). We propose a novel and actionable policy
solution: advocating for a rideshare transparency report that provides
specific guidance on what data should be made transparent. This policy lever
draws inspiration from their impact in other digital technologies such as AI
models and social media (Heikkilä, 2023; Diresta et al., 2022; Bommasani et
al., 2023; Edelson et al., 2021; Silverman, 2023). We believe such specific
reports could lead to concrete legislative actions, similar to the EU’s
Digital Services Act and AI Act, and the US’s proposed Platform Accountability
and Transparency Act, thereby concretely addressing some needs of rideshare
workers.

####  2.2.2. Transparency Requirements in Other Industries:

Transparency data disclosures are mandated in several industries. For food and
drugs, the Nutrition Labeling and Education Act of 1990 mandates nutritional
labeling, while the FDA’s FAERS222https://catalog.data.gov/dataset/fda-
adverse-event-reporting-system-faers-latest-quartely-data-files requires
reporting of adverse drug events. Meanwhile, for AI models, datasheets(Gebru
et al., 2021), model cards(Mitchell et al., 2019), and transparency
indices(Bommasani et al., 2023) have been proposed, though no legal mandates
exist yet aside from draft provisions in regulations like the EU AI Act and US
AI Foundation Model Transparency Act. In the corporate realm, 154 Fortune 500
companies voluntarily release diversity
reports333https://purposebrand.com/blog/diversity-report-examples-
fortune-2023/, and Title VII requires affirmative action
plans444https://www.law.cornell.edu/cfr/text/29/1608.4. Financial reporting is
stringently regulated, with the Sarbanes-Oxley Act of 2002 mandating that
public companies file annual (10-K), quarterly (10-Q), and event-based (8-K)
reports with the U.S. Securities and Exchange Commission to provide insights
into a company’s financial health. Additionally, 88 telecommunications and
social media companies555https://www.accessnow.org/campaign/transparency-
reporting-index/ provide transparency reports on user data and content
moderation, driven by privacy and surveillance concerns from the FBI(ACLU,
2010) and NSA(Greenwald, 2013), while the EU’s Digital Services Act mandates
such reporting for very large online
platforms666https://transparency.dsa.ec.europa.eu/. However, binding U.S. laws
are still lacking in this domain and there could be tensions with the first
amendment due to concerns of compelled speech.

###  2.3. Design Research for Rideshare Work

Worker-centered design research has uncovered workers’ imaginaries for new
systems of work ( (Zhang et al., 2022; Bates et al., 2021), led to the
creation of tools to support worker self-reflection and well-being ( (You et
al., 2021; Zhang et al., 2023)), and enabled collective action through
information sharing ( (Irani and Silberman, 2013; Salehi et al., 2015; Calacci
and Pentland, 2022)). However, as Zhang et al. (2023) points out,
incorporating end-users into the re-design of algorithmic systems can be
fraught due to AI systems’ dynamic nature: these systems are often unstable
and opaque which complicates designers’ approaches to prototyping. As a
result, we observe that most prior gig work centered design research has
utilized speculative scenarios to elicit workers needs. For example, Zhang et
al. (2023) utilize data-probes to familiarize rideshare workers with how
algorithmic management systems function and enable them to make design
recommendations. And, Hsieh et al. (2023b) identified harms that gig workers
face and utilized a speed-dating approach to efficiently surface workers’
ideas for mitigating harms through speculative design.

Human-centered explainable AI (HCXAI) research (Ehsan and Riedl, 2020)
however, highlights the importance of basing AI harm analysis and design
recommendations on real user interactions, like those experienced by rideshare
drivers (a group of AI end-users), instead of hypothetical scenarios
(Tonekaboni et al., 2019; Cai et al., 2019b, a). This aligns with recent
trends in XAI that cater to specific user needs (Kim et al., 2023; Suresh et
al., 2021). Our study focuses on the tangible transparency and explainability
needs of rideshare workers, emphasizing the importance of real-world instances
of harm for effective, user-centric solutions. We utilize 10 screenshots
(detailed in Appendix A) comprehensively encompassing the AI and algorithmic
decisions on rideshare platforms to ground our in-person interviews with real-
world worker needs and surface contextualized design features. This approach
is unique in gig worker research for using such comprehensive, real-world
instances to identify and ground algorithmic harm.

##  3\. Background on Rideshare Platform Features

Figure 1. Algorithmic Landscape of Rideshare Platforms described using Uber’s
terminologies Images generated using ChatGPT-4

We provide a brief overview of the key AI and algorithmic aspects of rideshare
platforms to help the reader understand the vocabulary participants used in
our interviews, with a focus on Uber, the largest rideshare platform in the
world (see Figure 1 for a schematic representation). These insights also apply
broadly to similar platforms such as Lyft and Ola, among others, but may be
termed differently.

###  3.1. AI and Algorithmic Features

####  3.1.1. Driver-Rider Matching

Uber’s algorithm for matching drivers and riders goes beyond simple proximity.
It also integrates traffic data, geographical considerations, and advanced AI
techniques, aiming to streamline wait times, optimize driver earnings, and
enhance safety by preventing matches with a history of negative interactions
(Uber, [n. d.], 2017, 2018).

####  3.1.2. Driver Wages and Rider Prices

Uber employs two distinct pricing models. The “upfront pricing” model provides
both drivers and riders with predetermined earnings and costs for a trip,
including detailed route information and estimated time (Uber, 2023f) based on
several attributes like the time, distance, duration, location and vehicle
types of the ride. Alternatively, in “rate card” regions, driver earnings are
based on distance and time, revealed after the ride’s completion. Across both
pricing models the platform takes a variable percentage of the rider’s payment
(colloquially referred as the take rate). A variable service fee, or take
rate, is charged, differentiating the rider’s payment and the driver’s net
earnings. This pricing structure has been scrutinized for potential
discriminatory practices (Pandey and Caliskan, 2021; Chen et al., 2015; Yan et
al., 2020).

####  3.1.3. Routing

The platform’s AI-driven routing system calculates the most efficient paths to
destinations. It also allows drivers the flexibility to use external
navigation tools like Google Maps, Waze, or Garmin for guidance (Uber, 2018,
2017).

####  3.1.4. Gamified Promotions and Algorithmic Features

Uber implements various gamified incentives to regulate demand and motivate
drivers. Surge pricing temporarily increases earnings in high-demand areas to
attract more drivers (Uber, 2023e). Boosts offer additional pay for trips
originating from specific zones during designated times (Uber, 2022a). Quests
provide additional earnings for achieving certain rides or earnings
milestones, e.g., drive 10 trips and get an extra $25 within a set period
(Uber, 2023b). Trip Radar displays nearby trip requests, allowing drivers to
select multiple they’re interested in, with matching prioritizing proximity
and reduced wait times (Uber, 2022b).

###  3.2. Stakeholder Attributes

####  3.2.1. Driver Attributes

Drivers on the platform can be characterized by their location, tenure,
demographics, ratings, reviews, acceptance and cancellation rates, and their
tier status (e.g., Uber Blue, Gold, Platinum, Diamond). The influence of these
attributes on the platform’s algorithms is a subject of ongoing driver concern
and debate. These tiers, linked to performance metrics such as acceptance and
cancellation rate, offer various rewards and benefits (Uber, 2023a). Drivers’
ability to rate and review riders is also a key aspect of the platform’s
feedback system (Uber, 2023c).

####  3.2.2. Rider Attributes

Riders can be characterized by their location, tenure, demographics, ratings,
and reviews. Similar to driver attributes, the influence of these rider
attributes on the platform’s algorithms is a subject of ongoing debate.
Riders, like drivers, have the capability to rate and review drivers (Uber,
2023c).

##  4\. Methods

Figure 2. Overview of our Methods

We used a mixed-methods approach to answer our research questions.identify the
transparency needs of rideshare drivers and elicit design recommendations that
address their transparency needs. We first browsed Reddit for a broad
understanding of discussions of rideshare workers relevant to the platforms’
non-transparent AI and algorithmic decisions. We then developed an interview
protocol and conducted interviews to surface drivers’ concerns, mitigation
strategies, and needs relevant to transparency. In parallel, to complement the
depth of interviews with the breadth of other drivers’ concerns 777This
analysis was primarily limited to surfacing harms, as most Reddit discussions
centered on voicing concerns (Yao et al., 2021; Watkins, 2022). across a
larger population, we used a Large Language Model (LLM) to analyze all
r/uberdrivers and r/lyftdrivers posts (65K) and comments (1.4MM) from
2019-2022 on Reddit, leveraging initial broad themes from our interviews and
prior work described in Section 2. Finally, we compiled repeating themes from
our interviews into a codebook, and incorporated relevant data from the Reddit
analysis, resulting in four key transparency needs: promotions, fares, routes,
and task allocation, to frame our findings sections. See Figure 2 for a visual
overview of our methods.

###  4.1. Method 1: Interviews with drivers

We conducted semi-structured interviews with nine Uber and Lyft drivers (see
Table 1

P | Age | Race | Gender | Platforms | # of Rides | Tenure | Education | Full Time
---|---|---|---|---|---|---|---|---
1 | 26 | White | Male | Lyft, Uber | 12,000 (Lyft) | 5 years | Some college, no degree | Yes
2 | 35 | Asian | Male | Uber | 500 (Uber) | 2 months | Bachelor’s degree | Yes
3 | 55 | Black | Female | Lyft, Uber | 600 (Uber) | 6 years | High school degree or equivalent | No
4 | 31 | Black | Male | Uber, Lyft | 4,300 (Uber) | 4 years | Master’s degree | No
5 | 31 | Black | Female | Uber | 3,200 (Uber) | 6 years | Some college, no degree | No
6 | - | Black | Male | Lyft, Uber | 2,900 (Lyft) | 6 years | Some college, no degree | No
7 | - | Black | Male | Uber | 23,000 (Uber) | 7 years | Associate degree | No
8 | 49 | White | Male | Uber, Lyft | 3,900 (Uber) | 8 years | Associate degree | No
9 | 36 | Hispanic | Male | Uber, Lyft | 11,200 (Uber) | 5 years | Some college, no degree | Yes

Table 1. Participant demographic and background information

for participant’s demographics). We used non-probablistic purposive sampling
(Patton, 2002; Crouch and McKenzie, 2006; O’reilly and Parker, 2013) to
recruit participants. The goal of the interviews is not to create
generalizable findings, but rather to investigate information-rich cases that
could provide a “thick description” (Geertz, 2008) of transparency in
rideshare work. The interviews involved participants annotating and discussing
screenshots of the ridesharing apps printed on paper. The screenshots centered
around the platform’s AI and algorithmic decisions across various stages of a
trip. We gathered the screenshots from discussions in the r/uberdrivers and
r/lyftdrivers subreddits, which we found searching for phrases such as “surge
pricing,” “take rate,” and “upfront price,” among others. For each screenshot,
participants identified their specific transparency concerns with the
information presented, described their current strategies for mitigating their
concerns, and suggested design interventions to address them. The interview
protocol and screenshots are available in the Appendix A. The study was
approved by our university IRB.

####  4.1.1. Participant Recruitment:

We recruited drivers who were active on Uber or Lyft in the last two years. We
confirmed their activity through a screening survey that included uploading a
driver profile screenshot. We recruited part-time and full-time drivers but
excluded those solely providing delivery services. We posted recruitment
messages on several NJ/NY Uber and Lyft driver groups on Facebook and
WhatsApp, as well as on university Slack groups. Also, we paid to post
recruitment messages on Craigslist and Reddit (as ads on on r/uberdrivers and
r/lyftdrivers).

####  4.1.2. Interview Process:

The interviews took place in-person between November to December 2023. The
interviews began with participants signing a consent form and completing a
demographic survey, followed by an ice-breaker activity. During the interview,
we examined drivers’ transparency concerns throughout the three stages of a
ride - at or before pickup (Screenshots 1-5), during the ride (Screenshot 6),
at or after dropoff (Screenshots 7-10), using 10 Reddit-sourced screenshots
(see Appendix A for the screenshots used). The screenshots included scenarios
about ride matching, wage calculations, routing, promotions (quests, boosts,
surge, trip radar), acceptance and cancellation rates, ratings and reviews,
and tiers. Participants identified their concerns in these screenshots,
discussed current mitigation strategies, and suggested additional desired
information.

The lead author conducted the interviews in person, which lasted between
90-100 minutes. We recorded interviews for transcription purposes. We
compensated participants with an $80 bank transfer or Amazon gift card. The
first and second authors coded the transcriptions using thematic analysis. We
conducted preliminary coding where the two researchers independently analyzed
the same batch of interviews. We then convened to compare and discuss our
findings. We also conducted card-sorting exercises to arrive at the final
codes. Later, we compiled repeating themes in a codebook, which we then used
to code the remainder of the interviews. We identified four key transparency
needs that we use to frame our findings: promotions, fares, routes and task
allocation.

###  4.2. Method 2: Analysis of Online Discussion Data

We analyzed large-scale text data from a large population of rideshare drivers
in online communities to complement our interviewsget a broad understanding of
their concerns and understand the extent to which the results converge with
our interview data.

####  4.2.1. Dataset Summary:

We downloaded posts and comments from r/uberdrivers and r/lyftdrivers for
2019-2022 from the Reddit Pushift Archives 888https://the-eye.eu/redarcs/.
After preprocessing to exclude the bottom 10% of submissions and comments by
word count for being too brief, our dataset included 65,377 submissions
($47,106$ from Uber, $18,271$ from Lyft) and $1,392,776$ associated comments
($1,054,030$ from Uber, $338,746$ from Lyft). Refer to Appendix B.2 for
detailed dataset summary statistics.

####  4.2.2. Multi-phase LLM Prompting for Analysis:

We used an LLM (GPT4-Turbo OpenAI model) to extract a quantified list, based
on frequency of occurrence of rideshare workers’ concerns, from Reddit
999Refer to the Appendix B.1 for more context on the use of LLMs for analysis
of public forum data. We used the following four prompts to guide our analysis
(see Appendix B.3 and B.5 for prompts details and associated costs).

  1. (1)

Generation Prompt: Identified posts and comments in our dataset that described
concerns surrounding platforms’ AI and algorithmic decisions.

  2. (2)

Classification Prompt: Using four commonly highlighted rideshare harms’ themes
(transparency, predictability, safety and fairness) from prior work (Rosenblat
and Stark, 2016; Zhang et al., 2022; Hsieh et al., 2023a; House, 2022; Hess,
2019) and our own interviews, we classified each output concern from the
generation prompt into one of four primary themes, with an additional “Other”
category.

  3. (3)

Aggregation Prompt: We aggregated and obtained the five most frequently
occurring concerns (secondary themes) within each primary theme.

  4. (4)

Prevalence Prompt: We classified concerns within each primary theme as
belonging to one of the secondary themes or an “Other” category.

Subsequently, the researchers selected all transparency-related concerns,
grouped them according to the four interview codes: promotions, fares, routes,
and task allocation, and quantified their prevalence through raw counts and a
percentage value across all the transparency-related concerns. We present the
quantitative analysis in Tables 3, 4, 5, 6, of the findings section.

####  4.2.3. LLM Performance Evaluation Metrics

We define metrics across all analysis phases to evaluate the LLM outputs’
performance. Table 2 presents the metrics, calculation methods, reference data
generation methods, and results. All metrics range from 0 to 1, with higher
values indicating better performance. Performance is generally very high,
mostly exceeding 0.74. The low factuality score (0.55) is less concerning
because the first analysis phase (Generation) uses broad instructions to allow
ambiguity and flexibility. As the completeness score is quite high, most
possible concerns move to the next stages, where more precise prompts, based
on our interview analysis and motivated by prior work, exclude non-factual
outputs. Additional details are available in Appendix B.4.

Metrics | Factuality | Completeness | Distinctness | Coverage (k) | Accuracy
---|---|---|---|---|---
Definition |  | Proportion of LLM-
---
generated concerns
also identified by
humans
| Proportion of human-
---
identified concerns
also generated by the
LLM
| Proportion of LLM-
---
generated themes
where the most similar
topic to each theme is
unique compared to the
most similar topics of
the other themes.
| Proportion of the ‘n’ LLM-
---
generated themes whose
most similar topic is
among the top ‘nk’ most
frequent topics in the
overall text.
| Proportion of LLM
---
labels which match
human labels
Analysis phase | Generation | Generation | Aggregation | Aggregation |  | Classification,
---
Prevalence
| Metric
---
calculation
method
| Manually compare
---
the LLM-generated
concerns with a
reference human-
generated list of
concerns
| Manually compare
---
the reference human
concerns with the
LLM-generated
list of concerns
| Automatically determine
---
whether most similar
topic to each theme is
unique using topic
models
| Automatically compare
---
the most similar topic to
each theme with the most
frequently occurring
topics across the entire
text using topic models.
| Automatically
---
compare the LLM
labels with
human labels
| Reference data
---
generation
method
| Theme extraction
---
by human
(2 researchers)
| Theme extraction
---
by human
(2 researchers)
| Topic extraction using
---
topic models
(BERTopic)
| Topic extraction using
---
topic models
(BERTopic)
| Labeling
---
by humans
(2 researchers)
| Comparison b/w
---
reference data and
LLM output
| Manually
---
(2 researchers)
| Manually
---
(2 researchers)
| Automatically
---
(Python Program)
| Automatically
---
(Python Program)
| Automatically
---
(Python Program)
| Limitation of
---
reference data
generation method
Expensive | Expensive |  | Need for interpretation
---
of topic model outputs
| Need for interpretation
---
of topic model outputs
Expensive
| Evaluation
---
sample size
| 125 posts
---
2,511 comments
| 125 posts
---
2,511 comments
47,873 concerns | 47,873 concerns | 100 concerns
Actual data size |  | 65,377 posts
---
1,392,776 comments
| 65,377 posts
---
1,392,776 comments
47,873 concerns | 47,873 concerns |  | 58,728 concerns
---
(classification)
47,873 concerns
(prevalence)
Results | 0.55* | 0.78* | 0.80 * |  | 0.95*(k=1)
---
1.00* (k=2)
| 0.74* (classification)
---
0.82* (prevalence)

Table 2. Metrics Chosen to Evaluate LLM-Powered Analysis Phases and Results.
Metric values belong to the closed interval [0,1], with higher values
indicative of better performance. * values are statistically significantly
different from chance measured via a binomial test (p-value <0.05).

####  4.2.4. Rationale for using LLMs:

We considered using statistical text analysis techniques like LDA, however,
they overlook linguistic nuances due to their “bag of words” model, failing to
capture crucial narrative elements like context in word order. Similarly, BERT
derivatives, while better at context capture, are limited to processing 512
tokens at a time. Furthermore, both of these topic modeling methods require
meticulous hyperparameter tuning, e.g., choosing number of topics apriori, and
significant human effort to translate output topics to human readable themes,
making them unsuitable for large datasets. Ultimately, using these established
methods will necessitate reliance on multiple disparate components (LDA, human
interpretation, text classifiers), leading to complexity, fragility, and
evaluation challenges.

LLMs offer a unified and scalable solution to circumvent issues when analyzing
relatively unstructured text data, like online forum posts. Recently published
works and preprints like TopicGPT (Pham et al., 2023) and LLooM (Lam et al.,
2024) demonstrated the benefits of LLMs over traditional topic models in terms
of performance and scalability 101010Although these works are similar to our
own prompting strategy, they surface topics, rather than themes, which is the
focus of our work. We consider “themes” to be different from “topics”. A topic
refers to the subject matter or main idea on a surface level described in a
word or few phrases, while a theme explores deeper insights and left open to
interpretation.. We believe scalability is crucial for several reasons: it
enables prioritizing intervention points based on the frequency of community
concerns, is necessary for tasks where breadth is preferred over depth, and
captures a diversity of opinions. However, recognizing the limitations of LLMs
in nuanced understanding, our novel approach blends the scalability of LLMs
with the depth of human expertise and interviews.

##  5\. Limitations

We recognize the following limitations of our methods:

(i) Sample Size: We conducted interviews with 9 participants, reflecting
recruitment challenges for in-person studies with this demographic. Despite
the small sample, we reached thematic saturation (Caine, 2016). Moreover, the
expansive Reddit community analysis complemented our insights into drivers’
concerns. Hence, although our analysis may not describe every driver’s
experience, the evidence we have collected nonetheless reveals structural
features of the Uber system that could potentially affect any driver using the
application.

(ii) Geographical Location: Most interviewees were located within a 100-mile
radius of the lead author’s university in the North-East U.S. Further, we draw
solely upon data from English-language forums on Reddit and drivers
interviews. However, the themes identified are applicable across regions to
any driver, since they are concerned with platform features not restricted to
specific geographical locations.

(iii) Screenshots related to Uber: Our study exclusively used screenshots from
the Uber app for annotation during interviews. However, we emphasize that the
underlying algorithmic features are also present in the Lyft app, and most
drivers are familiar with both platforms.

(iv) Reproducibility Challenges in LLM outputs: The results obtained LLMs like
GPT-4 are not guaranteed to be consistent or reproducible, as the model’s
outputs can vary across different runs, even with the same input data, due to
the stochastic nature of the underlying algorithms.

(v) Black Box Performance of LLMs: The performance of LLMs may be poor,
necessitating rigorous evaluation methodologies as we have defined to ensure
the validity of results. Additionally, LLMs struggle to identify unexpected
findings, resulting in a form of collective wisdom. By complementing LLMs with
interviews, we can also achieve depth rather than mere breadth.

##  6\. Positionality Statement

Our team’s expertise spans Human-Computer Interaction (HCI), AI & Machine
Learning, Technology Ethics and Policy. As academics, we advocate for
increased transparency in technology platforms. All the authors have engaged
with gig economy workers, policymakers, union organizers and a subset of them
are involved in designing decentralized platforms to mitigate AI’s negative
labor impacts. One of the authors is also a part-time driver with Uber. Our
positionality influenced our data interpretation from Reddit and interviews,
as well as participants’ willingness to engage with us. Acknowledging our
unconscious biases and academic position, we exercised reflexivity and
adherence to ethical research practices, though some biases inevitably
impacted the study.

##  7\. Ethics Statement on Reddit Data Use

Reddit users are not representative of the broader population (Gaffney and
Matias, 2018; Jamnik and Lane, 2019). Using Reddit data for research purposes
introduces risks such as privacy concerns due to exposing sensitive or
personally identifiable information(Fiesler et al., 2024). Specific to our
work, Reddit participants in rideshare forums may exhibit stronger opinions,
be more tech-savvy, or male-dominated (Ferrer et al., 2021), differing from
the broader rideshare driver population. Moreover, the English-specific
discussions on the most popular rideshare subreddits could exclude
perspectives from non-English speaking immigrant drivers, a significant
rideshare workforce segment111111In 2022, nearly half of all immigrants in the
U.S. reported being independent workershttps://www.mckinsey.com/featured-
insights/sustainable-inclusive-growth/future-of-america/freelance-side-
hustles-and-gigs-many-more-americans-have-become-independent-workers. While
this could affect the generalizability of driver experiences, our analysis
still reveals structural insights relevant to any rideshare drivers.
Additionally, use of Pushshift data for research purposes may be in violation
of Reddit’s current terms of services
121212https://www.redditinc.com/policies/data-api-terms. Finally, we note that
the application of AI tools LLMs to Reddit data can amplify existing biases
through their outputs or by propagating them when used as training data.

##  8\. Findings

We find that drivers experience a lack of transparency around four key
features of rideshare platforms: promotions, fares, routes, and task
allocation. For each feature, we detail the harms drivers experience due to
the lack of transparency, the mitigation strategies they use, and the design
solutions they developed to address transparency issues. A lack of
transparency is characterized by missing, misleading, or difficult to access
information on the platform interface.

We complement our rich qualitative interview data concerning harms with
quantitative insights from over 400,000 r/uberdrivers and r/lyftdrivers Reddit
community members, across more than 1 million submissions and comments,
highlighting the widespread nature of these harms. Specifically, we include a
table that presents the most representative transparency harms along with
relevant quotes from Reddit, ranked according to their frequency and
quantified as a percentage of occurrence across all the 31,340 transparency-
related harms: promotions (Table 3), fares (Table 4), routes (Table 5), and
task allocation (Table 6). We find that the transparency-related harms across
all four codes strongly reinforce the harms surfaced by interview
participants.

###  8.1. Promotions are presented misleadingly and drivers want accurate and
interpretable information about offers

Figure 3. Annotations of Driver Concerns for Surge Pricing (left) and Ride
Offer (right)

As we detail in Section 3, rideshare platforms offer a variety of gamified
promotions, including surges, quests, and boosts. These promotions allow
drivers to increase their earnings by meeting a set of criteria. Our
participants told us that while these promotions seemed attractive when they
initially started driving, over time, they became frustrated with what they
characterized as misleading information about promotion offers.

####  8.1.1. Harms caused by lack of transparency in promotions

Harm | Quote | % (Count)
---|---|---
| Issues with understanding and qualifying for
---
incentive programs, bonuses, and quests.
| Are they really expecting a thumbnail of a map
---
with zero details of where I’d need to be to have
a ride qualify for the Quests to be useful?
| 21%
---
(6,679)
| Drivers face uncertain and fluctuating income
---
due to changes in surge and quest pricing.
| Uber has always done that. When they offer a
---
higher promo, they manipulate and lower surges
| 9.5%
---
(2,953)
| Confusion persists over surge pricing
---
determination and boundaries.
| I get a ton of riders telling me they are paying
---
$40-$60 for the ride (surging rate), but when the
ride ends, I get around $6 (non-surge rate pay)
| 6%
---
(1,858)
Table 3. Most representative transparency harms on Reddit relevant to
promotions (Total = 11,490). The harms agree with those surfaced by interview
participants.

Drivers experienced financial and psychological harm due to the lack of
transparency in promotions. Surge pricing is presented as a heatmap within the
app’s interface (see Figure 3), where the dark red areas correspond to higher
surge multipliers and, thus, higher potential earnings for drivers completing
rides within that area. However, there is a lack of clarity on boundaries in
the surge map which reduces the utility of the surge map interface as P8
points out: “So the whole surge map is just terrible because you don’t know
where you need to be on the map to actually get the money.” P8. Uncertainty
about information presented on the surge pricing map makes it difficult for
drivers to form efficient work strategies and impacts their earnings.

Drivers face similar issues with misleading information about quests that
financially impact them. Drivers told us that the way information about quests
is presented in the app is misleading because it fails to clearly explain how
much additional money drivers can earn by completing the bonus. P3 says:

> “So [Uber] can make that more transparent and stop being deceptive…Because
> [Uber will] tell you, ’We’ll give you a bonus if you do 40 rides like for
> $200.00.’ But if you already made your $200.00 in your 40 rides, you don’t
> get anything.” P3

The way quest amount is currently presented is misleading because drivers do
not get the amount on top of their earnings, but rather used to top off
earnings if they earn less than that amount for the required amount of rides.
However, drivers say that this caveat is unclear on the quests offerings page.

Drivers share a common belief that gamified promotions were intended to get
them to work during times outside of their preferred hours by offering them
attractive offers when they were offline. This perception is in line with how
algorithmic nudging works: using soft control as a way to manipulate the
supply of drivers (Rosenblat and Stark, 2016). Our participants re-iterated a
common finding in the gig work literature–namely that constant nudging and
gamification impart a psychological toll on workers. P1 characterized these
gamifying techniques as creating a work environment that was like a casino
saying: “The house always wins kind of thing. It goes back to the gambling
ones, like they’re going to give you something that you know is impossible.”
P1. A lack of clear information about how gamified promotions work and their
implications for driver earnings leads to financial and psychological harm.

####  8.1.2. Mitigation strategies used to address lack of transparency in
promotions

To cope with unpredictable earnings as a result of non-transparent gamified
promotions, drivers initially experimented with different strategies to
identify profitable trip offers. Experienced drivers, like P9, learned to
avoid chasing surges due to its unpredictability and prefer to accept surges
that align with their routine, explaining, “[I] absolutely [do] not [chase the
surge]…because I know it changes with supply and demand.” P9 Similarly,
drivers like P5 initially tried to maximize earnings by strategically entering
surge areas, saying, “I may go offline and try to get to the $19 area.” P5
However, they soon realized the inefficiency of this approach, with P5 noting
the frustration of surges diminishing upon arrival: “I expect that by the time
I get there [$19 surge] will probably be $8 and then you know you have wasted
time.” P5. This experience led drivers to avoid promotions, as P2 shares, “I
don’t even try to shoot for any higher [value] quest.” P2. Similarly, P5
started optimizing for what they perceived to be more reliable trips: “once
I’ve got a…trip from the airport, I turn on my destination filter from there,
and work back towards the airport. And because the airport is a popular
destination, it usually works.” P5. In general, drivers responded to opaque
and uncertain payoffs from gamified promotions by developing a preference for
predictable and transparent trip offers.

####  8.1.3. Design solutions to address lack of transparency in promotions

Drivers expressed a desire for design solutions that addressed the lack of
transparency in promotions by asking for more explanations about how
promotions worked. P3 illustrates this through their experience with the quest
feature: “So they can make [the bonus amount] more transparent and stop
being…I mean, they do tell you on the second page that, but that’s not right,
though.” P3. Here, P3 calls out how information on how much drivers can expect
to earn from a bonus is hidden from drivers when it should be caveated upfront
that the bonus amount is supplemental, not additional to driver earnings from
completed tasks. As a result, P3 asked that this caveat be shown upfront with
the offer. P6 similarly calls for information about “how many rides make up a
boost?” P6 so drivers better understand how they can increase their earnings
through participating in these incentive offers. Drivers also suggest that the
platform could message them how much they can make from a surge to get a
better picture of their potential earnings: “Before the surge they can send
SMS to drivers [saying] ’This is what potentially you can make.’ I think
that…will motivate a lot of drivers as well. Because people always want to
gain information, they can make extra money.” P7. In summary, the platform
interface could more clearly show that amounts earned from quests and boosts
are supplemental, not additional, to driver earnings, and they could provide
more granular information about the requirements to qualify for a promotion.

###  8.2. Fare information is difficult to decipher and drivers want more
accurate and explicit metrics

Drivers earn a portion of a trip fare for each trip they complete. However,
the total trip fare is often unclear to drivers, as is the amount they can
expect to earn. The trip fare information that drivers are shown depends on
whether they work in a region that uses “upfront pricing” or “rate card
pricing”. Drivers who worked in regions with upfront pricing are shown
estimated total earnings as well as some information about the duration of a
trip and destination. Drivers who worked in rate card regions are shown
information about trip duration and distance and they have to mentally
calculate their earnings based on the rate per minute and rate per mile for
their region. Regardless of the pricing model, drivers told us that the fare
breakdowns they saw were inaccurate and made it difficult for them to
accurately predict their earnings. This causes financial harm and also raises
questions among drivers about fairness.

####  8.2.1. Harms caused by lack of transparency in fare information

Harm | Quote | % (Count)
---|---|---
Drivers are unsure about opaque fare calculation methods. | $30 to the middle of nowhere and had to eat dead miles to drive back | 23 (7202)
| Drivers express discontent with the percentage of fares they
---
receive compared to what the platform charges customers.
| Yeah it’s always high ride demand and they charge the passengers high
---
prices and drivers earnings are like 25%
| 3
---
(1025)
| Drivers are not fairly paid for extensive wait times during
---
stops or added tasks
| The stop system is bullshit. You make less money, 100%. I just checked.
---
To order two rides, one there, one back, $17. To order the same ride, but
using stops it’s $13.
| 3
---
(856)

Table 4. Most representative transparency harms on Reddit relevant to fare
information (Total = 9,083). The harms agree with those surfaced by interview
participants.

Lack of transparency in fare information makes it difficult for drivers to
predict their earnings and work towards financial security. P8, who works in a
rate card region, told us that it is difficult to decipher exactly how much
they could expect to earn based on the fare breakdowns they are given:

> “If you can do the math in your head by looking at the miles and the time
> you can possibly figure [the fare] out, but you only have a few seconds
> really to choose whether you want to accept the ride…A lot of people, myself
> included, can’t do those calculations.” P8

P8 describes a situation echoed by other drivers: the information presented
combined with the available time for processing was insufficient to make an
informed decision about potential earnings from a trip offer. Drivers working
in upfront pricing regions do not have to do these mental calculations as they
are provided with an estimate of how much they will earn from a trip. However,
the upfront price often doesn’t account for real-time ride conditions such as
wait times, stops and traffic, as P5 told us: “I only got paid like $13, but I
spent a whole hour in traffic.” P5. P5 summarizes the frustration that even
though upfront pricing tells drivers their estimated earnings in the trip
offer, these estimations do not necessarily result in financial security or
greater predictability with earnings because they don’t take into account
real-time driving conditions and thus can be misleading. Drivers are also
frustrated with high and unpredictable service fees, typically not visible to
them, P7: “You’re supposed to take 10%. But because of all additional fees,
they take more than 30%. Which is a problem for a lot of drivers.” P7.

####  8.2.2. Mitigation strategies used to address lack of transparency in
fares

The lack of transparency in fares makes it difficult for drivers to calculate
and predict their earnings accurately. Drivers confront and try to mitigate
the effects of unpredictable earnings with a few strategies that, while
resourceful, remain significantly limited without substantive platform-led
changes. Full-time drivers are forced to work extended hours, as P4 says “But
you know what…you gotta force yourself to go and do” P4, to offset pay
disparities. P9 echoes this sentiment by stating that, “the algorithm is kind
of like in your nose and it keeps track of how much money you’re making. At
the end of the day you notice that I’m not making more than 20 bucks an
hour…as a full-time driver, you better put in those hours, a lot of hours. So
the flexibility right, that is gone.” P9. Drivers employ strategies such as
switching apps or relocating to find better fares. P9 describes using both
Uber and Lyft: “I have my other app, Lyft, you know. I turn [it] on and let it
give me a ride. You turn off Uber.” P9. P4 mentions moving to a different area
for better offers: “OK, let me drive, like, 10 miles away from there and then
turn back on the app.” P4. These strategies, while showcasing drivers’
adaptability, underscore the need for platform-level reforms to address
unpredictable earnings genuinely.

####  8.2.3. Design solutions to address lack of transparency in fares

Drivers want to be able to predict their earnings from a trip offer. Rate card
pricing put a cognitive load on drivers as they had to do quick mental
calculations to estimate how much they would make. These drivers emphasized
the importance of being shown your earnings upfront. The upfront model
provides drivers with the transparency they desire and reduces the cognitive
load on drivers to calculate fares mentally. However, this model is not used
consistently across the US, and drivers point out that the estimated earnings
can be misleading. As a result, drivers proposed that rideshare companies
could show estimated earnings upfront and trip metrics such as distance, time,
rate per mile, and rate per minute after a ride has occurred so they could
assess if their earnings felt fair, “I think you can have upfront pricing and
have a mileage rate at the same time. I mean, they kind of already do it with
the reservations.” P5. P4 suggested a model like the one taxi cab companies
use where: “they started off with the base pay. And then there was a there was
a certain per mile you went, and then there was also a charge for per minute
that you were inside that vehicle.” P4. These fare breakdowns would make it
easier for drivers to forecast their earnings and understand how fares were
being calculated.

However, showing rate breakdowns in more detail does not necessarily address
the core issue of financial precarity that drivers face, which is one of their
key concerns. Instead, drivers want easily interpretable, straightforward
pricing models where trip metrics, like mileage and time, correspond closely
to their earnings. P4 summarizes this sentiment, saying: “If it was fair pay,
honestly, then, a lot of these issues wouldn’t be a thing. You know, if the
mileage and the time equated to the pay that you deserve without any
additional bumps or anything like that, then I don’t think there will really
be much of an issue.” P4. These statements underline a shared desire for a
straightforward and fair pay structure. Ultimately, the inadequate pay coupled
with the lack of trust in the platforms underscore the critical role of
transparency for drivers, considering their challenging labor conditions.

###  8.3. Route information is imprecise and drivers want accurate,
contextualized, and reliable geographic information

When considering trip offers, drivers typically have fewer than 10 seconds to
accept or decline a ride. In that limited time, they have to parse and decide
based on the trip metrics presented in the platform interface (see Figure 3
for an example of a trip offer). Drivers point to issues with the accuracy and
completeness of geographic information presented and the amount of time given
to accept or decline an offer. Drivers told us that the lack of transparency
in trip offer information leads them to engage in unsafe driving behaviors and
negatively impacts their financial well-being.

####  8.3.1. Harms caused by lack of transparency in route information

Harm | Quote | % (Count)
---|---|---
| Concerns over inadequate payment for time and
---
distance invested in long pickups and waits.
| I don’t understand what the hell is Uber thinking when giving
---
us long pickups for short trips. NOBODY sane accepts a
$4 ride for someone half an hour away.
| 5
---
(1429)
| Drivers experience technical difficulties with app
---
navigation and route mapping, leading to disruptions.
| I started using the Lyft nav, then Google maps, then Waze only
---
to go back to the Lyft nav. I went through that cycle because the
Lyft nav sucks but at least you see everything going on with the app.
With the others, I’ve lost out on streaks and pings because of glitches, etc.
| 4
---
(1208)
| Drivers express risks due to dangerous multitasking
---
and unsafe areas suggested by algorithms
| If a Pax wants to be taken to a part of town I don’t feel safe going,
---
I feel like I should know, for my own safety and peace of mind.
| 3
---
(909)

Table 5. Most representative transparency harms on Reddit relevant routing
information (Total = 3,546). The harms agree with those surfaced by interview
participants.

Drivers tell us that getting trip offers is distracting, but they need to pay
attention to their phones while they drive if they want to earn money. P2
describes this tension, saying: “I’m literally on the bridge, like going 60 or
whatever…and they literally send me a request, like what do I do?” P2. Drivers
utilize indicators such as trip distance, estimated duration, and end
pickup/dropoff location to decide whether to accept a trip offer. However,
drivers claim that these indicators are often inaccurate, not present, or
incomplete. For example, multiple drivers told us that they are unable to see
the full address for the pickup and dropoff location, which makes it difficult
for them to assess if a trip is worthwhile, “it’s hard to really know if this
is the right town that you’re actually going in, because in New Jersey it’s so
many towns, it could be two towns with the same name” P3. Drivers voiced
concern for their physical safety when driving without knowing beforehand
where they were going and said they felt compelled to decide between safety
and making a living: “If you want to stay out of a bad neighborhood…you will
never make any money doing this job.” P1. P2 explained their frustration about
the lack of transparency with trip destinations, saying: “If you’re not a high
enough tier, you get less information [about the trip]. So you don’t even know
exactly, like, where the trip is going.” P2. Making access to precise
geographical features of a trip contingent on a driver’s tier status,
exacerbates issues of transparency because a driver’s access to information is
contingent on a convoluted algorithmic process that determines their tier
status.

Even though rideshare platforms have features such as “destination filters”
that purport to help drivers by enabling them to filter for trip offers around
a destination the driver selects, drivers told us that these features rarely
work as intended. P4, P5, and P6 criticize the malfunctioning destination
filter, as P6 states: “Every time I use it, it sends me away from my house. I
live close to Philadelphia, it may try to send me to New York.” P6.

####  8.3.2. Mitigation strategies used to address lack of transparency in
route information

Drivers utilize a variety of strategies, including auto-accepting all ride
offers and compromising on physical safety to cope with inadequate information
processing time and incomplete geographic details. P9 explains their approach
to dealing with incoming trip requests and inadequate information processing
time by saying: “What happens if you lose a [trip] request? Your acceptance
rate gets hit. Because of that, I set up the app for automatically accepting
the ride.” P9. This strategy enables them to stay focused on the road and not
worry about impacts on their acceptance rate if they miss a trip offer while
driving. However, this meant that sometimes P9 accepted trips that paid very
little for the amount they had to drive. Several drivers mentioned they just
ask the passenger where they’re going and follow their suggestion rather than
relying on the app, P6: “I cater to the people, to be honest with you…I depend
on the actual passengers a lot of times, so I ask them [where they’re going]”
P6.

Ultimately, drivers navigate a precarious balance between ensuring their
safety and optimizing for earnings. Some, like P6, will not wait long in
unsafe neighborhoods: “If I go to a bad neighborhood. You know, I don’t get a
response, you know, within a couple of minutes, then I’m definitely trying to
pull off.” P6 And P7 discusses the importance of understanding passenger
psychology for self-protection. Yet, drivers like P1 often feel compelled to
accept rides in risky areas, as declining can affect their earnings, and the
algorithm may offer the same ride again: “I usually roll the dice and take the
person… like 3:00am in the morning at the bottom, which is in [anonymized for
review], which is like a really bad neighborhood, you gotta decide if it’s
worth it because you’re gambling … that person might try to rob you.” P1
However, P1 is quick to add that “You don’t have a choice; otherwise you will
make no money. So the safety is like you just have to get over it.” P1.

####  8.3.3. Design solutions to address lack of transparency in route
information

Figure 4. Hold button (left, P3) and Ride Available Nearby feature (right,
P6). P3 and P6 propose these interventions to prioritize safety over
algorithmic management during a ride.

Drivers express a need for more time to make informed decisions about ride
offers. P4, P6, P7, and P9 also stress that their focus should be on the road
during a ride, not on making calculations or decisions. P4 specifically notes,
“really my focus needs to be on the road, not trying to do math in my head and
be looking at my phone!” P4. As a result, P3 and P6 propose the implementation
of a Pause/Hold Button and a complementary Ride Available feature, as seen in
Figure 4. P6 describes a system where a ride offer pops up only when the
driver has stopped, enhancing safety: “If it pops up a ride available, when
you stop, for safety reasons, you can look at the ride and say I can do it or
I can’t do it.” P6 P3 further elaborates on this idea, suggesting a hold or
pause button that allows drivers to pause offers during a ride or a break
(e.g., coffee / restroom breaks) and review those offers once they have safely
stopped: “Maybe we have like a hold button or a pause button or something that
you can click on… Put in the hold until you safely stop … I think this is
what’s causing a lot of the accidents. So many people are looking at the
phone. Let me put it on hold, and when I stop, if it’s still available, I
could take a look at it or what they have available in the area.” P3.

Drivers call for platforms to add more contextualized geographic information,
create features that let drivers safely review and accept ride offers, and
consistently display a set of basic information for all trip offers. P3 draws
on their experience asking passengers for landmarks near their destination:
“But what I try to do is I ask the customer, I said, which school are you
going to or, you know which town, you know, do, you know, give me, like, an
idea.” P3 to inform their design solution to include contextual geographic
like the name of the town or landmarks in the proximity of the dropoff
location, helps the driver better understand where they’re going.

###  8.4. Task allocation is black-boxed and drivers want explanations about
how they work

Due to the opaque nature of rideshare platforms’ AI and algorithmic decisions,
drivers lack insight into the factors influencing the quality and quantity of
trip offers. While drivers believe acceptance/cancellation rates, ratings, and
reviews impact trip quality, the lack of transparency in task allocation
prevents definitive proof. This opacity results in psychological and financial
harm, hindering drivers’ ability to establish a predictable work routine. It
also raises fairness concerns, as drivers are uncertain about the criteria
determining task quality. To address this, drivers seek design features
displaying how their in-app metrics contribute to task allocation, aiming for
transparency and improved understanding.

####  8.4.1. Harms caused by a lack of transparency in task allocation

Harm | Quote | % (Count)
---|---|---
| Drivers express concerns over clarity in ride
---
assignments and earnings determination by algorithms.
| A driver who keeps a high AR, low cancel rate, gets mostly 5 Stars,
---
and gives out mostly 5 Stars to pax will get better (more lucrative) rides
than a driver the company’s algorithm doesn’t like.
| 9
---
(2765)
| Challenges with understanding and adapting to algorithm
---
changes that affect ride assignments and income.
| I multi-app, but the algorithm has changed. If you log off
---
from the app, to ride for another app, the algorithm puts you
on the bottom of the queue after you log on again for the next ride.
| 8
---
(2408)
| Concerns over lack of clarity in cancellation criteria
---
and the associated fare impact.
| Then I ask about the cancellation fee and they said they
---
can’t provide one as no option came up.
| 4
---
(1197)
| Uncertainty about how ratings are determined and their
---
direct impact on drivers’ work opportunities.
| I read that Lyft will try to match a pax with a driver that both had
---
5 starred each other in the past.
| 1
---
(384)
| Drivers feel platform algorithms unfairly influence ride
---
distribution, surge pricing, and overall income.
| A lady was standing next to my car and ordered a lyft and it gave her
---
a driver that was outside of the Bonus zone and 20 minutes away.
| 1
---
(316)
| Concern over algorithms distributing rides unfairly
---
or prioritizing certain drivers
| Perceived favoritism by the app’s algorithm towards newer drivers
---
can limit earnings for long-term drivers.
| 0.5
---
(151)

Table 6. Most representative transparency harms on Reddit relevant to task
allocation (Total = 7,221). The harms agree with those surfaced by interview
participants.

Drivers accepted and completed trips they would have otherwise deemed unsafe
because they were afraid of impacting their acceptance/cancellation rates and
being punished by the platform’s algorithm. For example, P5 told us that, “as
a woman,” she was hesitant to accept trips in certain neighborhoods late at
night, but she was “not going to cancel the trip for the sake of not trying to
damage my rating” P5. Drivers worry that their performance ratings are
affected by their demographic characteristics. For example, P7, a Black man,
said: “Some people… they act certain kind of way based on their race, based on
their background, based on where they live… some drivers get a lot of bad
ratings.” P7. He further mused about whether these ratings and reviews may
indirectly influence the algorithm’s matching and compensation outcomes.

Figure 5. Evidence of algorithmic wage and price discrimination shared by P8.
The earnings are redacted to preserve participant privacy.

Without insight into how task allocation works, drivers worry about potential
discrimination and unfairness. P2 told us how they believed algorithmic wage
discrimination–different pay for similar work–was taking place, saying: “if
you had two different phones and you got two different drivers, one person was
getting one thing and somebody else was getting another … and I was like, that
can’t be right …it was the same distance, just different people … why is one
person offered a higher rate and why is another person offered a lower rate?”
P2 P8 shared screenshots demonstrating evidence of algorithmic wage
discrimination. Figure 5 shows different rate cards for similar rides across
the Garden State Parkway in NJ. The rate card showed $1.23/mile and
$0.21/minute for pickups from the east, but only $0.70/mile and $0.18/minute
from the west, even though the pickup spots may be less than a mile apart. P8
noted, “From Lakewood MVC West of the Parkway to Costco [on the] East, Uber
charges $9.92 one way, and $11.94 the other.” P8 This evidence aligns with
other drivers’ experiences and also highlights the algorithm’s role in wage
(and price) discrimination.

####  8.4.2. Mitigation strategies used to address lack of transparency in
task allocation

Drivers choose to optimize their in-app metrics (e.g., acceptance rates and
performance rating) in hopes of remaining in good standing with the platform
and thus getting more and better tasks. Some drivers, like P9 use the auto-
accept feature: “What happens if you lose a request? Your acceptance rate gets
hit. Because of that I set up the app for automatically accepting the ride.”
P9. Others, like P3, go to lengths to be polite with unruly passengers out of
fear that the passenger could give them a low rating and impact their
performance rating. Some drivers, like P2 and P8, attempt to scrutinize the
payouts for tasks by comparing trip offers with each other. In summary,
drivers attempt to mitigate opacity in task allocation by maximizing the two
in-app metrics most visible to them, acceptance rate and performance rating,
and through systematic collective examination of trip offers.

####  8.4.3. Design solutions to address lack of transparency in task
allocation

Drivers suggest ways that rideshare platforms could use in-app metrics for
task allocation in a way that allows drivers to exert agency without fear of
reprisal. Drivers emphasize that their cancellation rate should not be
impacted by cancellations made for safety reasons during a ride. P7 insists
that such cancellations, made while driving, should not affect their
cancellation metrics: “Once you’re driving already, if you make cancellations,
it should not affect your cancellation.” P7. P5 extends this argument to
cancellations due to low passenger ratings, advocating that safety
considerations should override concerns about metrics: “I don’t think we
should be docked or penalized or should go towards our cancellation rate for
cancellations due to passengers that have low ratings and cancellations that
we do while we’re driving.” P5.

Drivers also asked for explanations about how the acceptance and cancellation
rates impacted the trips they were offered as they felt that having lower
acceptance/cancellation rates would lead to less desirable trip offers, but
they struggled to gather empirical evidence that would disprove this
hypothesis: “like what [is Uber] basing that off of? I feel like since I
started driving a little bit more full time, I haven’t gotten [a quest bonus]
in quite some time.” P4. P4 illustrates the uncertainty they faced when trying
to understand what led to promotions. P2 also told us that they wanted
explanations about how reward amounts for promotions are calculated so they
could make decisions about how often they needed to drive: “I mean how the
rewards are being calculated? But these numbers change a lot, so I have no
idea how these rewards are calculated.” P2. In summary, drivers want
explanations of how task allocation decisions are made so they can make
informed decisions about their work strategies. They want explanations to be
provided post-hoc to enable adequate time for reflection.

##  9\. Lessons for Regulating Rideshare Platforms: Rideshare Platforms Must
Publish Transparency Reports

Figure 6. Thematic analysis map: Overview of transparency-related findings
from driver interviews and Reddit data analysis.

We present an overview of our findings in Figure 6 which highlight a
transparency gap between existing platform designs and the information drivers
need, particularly concerning promotions, fares, routes, and task allocations.

###  9.1. Design Solutions Require Organizational Consideration and Policy
Intervention

Drivers need transparency into algorithmic decisions, such as promotions,
fares, routing, and task allocation. While rideshare drivers propose design
changes for transparency, rideshare platforms, driven by a focus on scale and
maximizing profits, may resist implementing them without proper incentives.
Moreover, information asymmetries are beneficial to rideshare platforms as
they enable platforms to extract surplus value from each transaction by having
access to information unavailable to other parties due to the platforms’
position as marketplace designers (Viljoen et al., 2021). This disincentives
platforms to balance information asymmetry through increased transparency.
While our participants identified innovative design features that could
increase transparency in their work, it is important to consider how rideshare
platforms’ operational logics constrain these design solutions. We build on
recent work that argues that researchers must consider organizational
contexts, actively incorporate policy, and conduct the necessary translation
work to firmly ground policy recommendations in empirical research findings
and put forth a transparency report geared toward policymakers exploring the
impact of AI on labor (Yang et al., 2024; Dalal et al., 2023). This report
demonstrates how technology researchers can engage in translation work between
empirical studies and policymaking to address the misalignment between
platform interests and drivers’ transparency needs.

###  9.2. Rideshare Transparency Reports as a Solution to Drive Regulatory
Changes

Transparency has long been essential for clarity and social accountability
(Kalderon, 2015; Florini, 2007; Acemoglu and Robinson, 2013), with its
importance recognized in various fields (Johnston, 2006; Schudson, 2015). Its
growing relevance in digital technologies, especially in social media and AI
(Heikkilä, 2023; Diresta et al., 2022; Bommasani et al., 2023; Edelson et al.,
2021; Silverman, 2023), is leading to calls for transparency through
legislative actions like the EU’s Digital Services Act and AI Act, and the
US’s proposed Platform Accountability and Transparency Act.

Transparency is a powerful catalyst for change. Transparency initiatives in
social media catalyzed legal actions and ad practice reforms. Facebook’s Ad
Library131313https://www.facebook.com/ads/library/ enabled research (Nagaraj
Rao and Korolova, 2023; Le Pochat et al., 2022; Papakyriakopoulos et al.,
2022) and lawsuits(Romer-Friedman and Ebadolahi, 2022), resulting in
settlements which have changed targeted job and housing ad systems(Department
of Justice, 2022). Pay transparency narrowed gender pay gaps(Werber, 2022).
These precedents show transparency drives material change.

For rideshare platforms, enhanced transparency can also boost predictability,
fairness, and safety, driving regulatory changes. Mandating platform data
transparency enables research, fair wage advocacy, treating platforms as
utilities, and policies addressing low gig pay. However, currently, rideshare
platforms currently lack incentives to voluntarily publish the granular data
necessary to enhance transparency, absent legislation requiring disclosure
141414Chicago’s rideshare regulations mandate platform data disclosures,
establishing a policy precedent; see
https://www.chicago.gov/city/en/depts/bacp/provdrs/vehic/svcs/tnp.html, and
https://chicago.github.io/tnp-reporting-manual/.

Building on existing policy precedents in AI, social media regulations, and
nascent rideshare regulations, we argue researchers must pursue diligent
translation between technical design interventions and policy domains. The
transparency report would help realize a subset of the technical interventions
we have proposed by providing access to data that only platforms currently
control. We propose a concrete outline and establish baseline reporting
guidelines for a robust rideshare transparency report. These findings
highlight critical implications for policymakers. We call for rideshare
platforms to publish comprehensive transparency reports detailing indicators
for workers, advocates, and regulators to better understand the complex
rideshare landscape and make more informed decisions that benefit drivers.

###  9.3. Platform Inclusion Criteria

We propose 5 criteria for the inclusion of rideshare platforms that must
publish rideshare transparency reports. We loosely define rideshare platforms
as a digital service that uses AI-based and algorithmic systems to pay drivers
and match passengers for transportation. We argue that a rideshare platform
that meets any one of the following criteria must be included in the reporting
requirements. A rideshare platform should publish transparency reports if it
151515We use the term algorithmically to mean the use of an AI-based or other
algorithmic system):

  1. (1)

Algorithmically determines and varies the wages of drivers for similar rides
defined by distance and/or duration

  2. (2)

Algorithmically matches drivers and riders

  3. (3)

Algorithmically varies the platform fee for each ride

  4. (4)

Algorithmically incentivizes drivers through gamification features (e.g.,
quests, challenges, bonuses), which vary across drivers according to, but not
limited to, location, tenure, and demand.

  5. (5)

Enables rideshare access to more than 10 million people across the U.S. and
employs more than 1 million drivers.

Uber and Lyft are examples of platforms that meet all of the above-mentioned
criteria.

###  9.4. Transparency Report Indicators

We define 51 indicators that characterize transparency in rideshare platforms
inspired by our analysis of Reddit and interview data. We divide the
indicators into 4 broad categories: indicators about ride statistics,
indicators about driver statistics, indicators about algorithmic management,
and finally indicators about platform policies. We provide an overview of the
indicators here and a comprehensive list in the Appendix Figure 17.

  1. (1)

Ride Statistics (24 indicators): These indicators include timestamps of
pickup, dropoff, waits and stops, overall ride duration and distance, and a
breakdown of fees and wages. These ride statistics indicators are vital for
enabling algorithmic audits and enhancing overall transparency, specifically
regarding the breakdown of wages, to investigate questions of algorithmic wage
discrimination.

  2. (2)

Driver Statistics (11 indicators): These indicators include drivers’ earnings,
ratings, reviews, acceptance and cancellation rates, and demographics. This
should be available for all service locations. These indicators are crucial
for enhancing predictability, bolstering driver agency, and promoting wage
parity across the platform.

  3. (3)

Algorithmic Management (9 indicators): These indicators include inputs,
outputs, and data used in the platform’s algorithms, privacy mitigations,
incentives (quests, surges), and impact assessments on drivers, enhancing and
realizing driver needs of explainability and predictability.

  4. (4)

Platform Policies (7 indicators): These indicators include details about
safety incidents, customer service inquiries, new platform features, maximum
and minimum earnings, rate cards, as well as deactivations and appeals,
providing essential transparency into the rideshare ecosystem, extending
beyond direct AI and algorithmic decisions.

###  9.5. Data Access and Retention

For effective regulation and transparency, the FTC and local transportation
authorities (e.g. see data on Transportation Network Providers in the Chicago
Data Portal
161616https://data.cityofchicago.org/Transportation/Transportation-Network-
Providers-Trips-2018-2022-/m6dm-c72p/about_data), should either obtain data
directly from rideshare platforms or mandate these platforms to publish and
maintain it in a centralized data warehouse. This warehouse should regularly
update, providing daily, weekly, monthly, and yearly data snapshots and
summaries. It must offer a publicly accessible API, enabling requests by date
range, platform, and location, and a web interface for basic data searches.
Platforms are advised to retain data for one year, while government agencies
should maintain it for seven years, balancing the need for error correction
and data integrity against storage costs.

###  9.6. Recommendations for Policymakers

As policymakers shape the future of rideshare platforms, a focused approach to
transparency, enforcement, and realistic policy goals is crucial. We provide 3
recommendations to policymakers below. Ultimately, these recommendations call
for a more accountable and sustainable transportation sector.

  1. (1)

Prioritizing Transparency in New Rideshare Regulation: Transparency should be
a foremost consideration in rideshare regulation and should mandate the
publishing of rideshare transparency reports. This prioritization will enhance
accountability and public trust in these services.

  2. (2)

Enforcing Existing Transparency Policies: Policymakers should actively enforce
current legislation through data protection authorities and the FTC (Zanfir-
Fortuna, 2023; FTC, 2022; Hess, 2019) to increase transparency in rideshare
platforms. This enforcement can lead to more informed decisions by platforms,
drivers, riders, and regulators and foster better practices within the
industry.

  3. (3)

Setting Realistic Expectations for Transparency While transparency remains
vital, policymakers must acknowledge its constraints. Worker data collectives,
third-party applications, and application design changes represent potential
complementary solutions to improve driver well-being. Therefore, policies
should strive for a reasonable transparency level, recognizing it as one facet
of a broader strategy addressing rideshare challenges.

###  9.7. Transparency is Not a Panacea: Limitations and Unintended
Consequences

Researchers have highlighted significant limitations of transparency
initiatives across domains; we acknowledge them here. Keller (2021); Urman and
Makhortykh (2023); Husovec (2024) note the lack of standardization in
transparency data disclosures, allowing companies discretion over measurement
approaches without adhering to established principles (e.g. Santa Clara
Principles). Further, companies lack incentives to devote resources for
dedicated transparency infrastructure, leading to insufficient disclosure
(Dubois et al., 2021; Keller, 2021). This can result in “transparency
washing”(Zalnieriute, 2021), where initiatives obfuscate substantive questions
about corporate power. Encapsulating all these points, Ananny and Crawford
(2018) develop a 10-point typology examining transparency’s limitations in
creating accountable algorithmic systems. Their typology argues revealing
system parts and data alone is insufficient for understanding or governance.
Engaging with transparency’s limits allows using them as conceptual tools to
hold sociotechnical systems accountable by understanding their relational
nature, not just through visibility.

Mandating transparency, could also inadvertently undermine core business
interests and result in unintended consequences(Kroman, 2024). Disclosing
competitive algorithms like surge pricing models could diminish platforms of
their competitive advantages. Furthermore, transparency requirements may force
pricing models to converge, reducing competition and consumer choice. Publicly
sharing driver incentives could open avenues for collusion and market
distortions. Revealing surge zones may result in oversupply in those areas,
diminishing earning potential elsewhere. Privacy risks can also emerge,
without adequate safeguards, from disclosing driver and rider location data.
Taken together, transparency initiatives face inherent trade-offs between
accountability and adverse effects, and policymakers need to balance the needs
of stakeholders carefully.

##  10\. Conclusion and Future Work

Our study exposes a lack of transparency in rideshare platform features,
leading to financial, emotional, and physical harm to drivers. Through a
unique mixed-methods study that combines in-person interviews with nine
drivers and an LLM-based analysis of over one million Reddit posts, our
findings highlight the lack of transparency in key rideshare platform
features, including promotions, fares, routes, and task allocation.
Importantly, the coherence between the interview and Reddit data underscores
the widespread and persistent nature of transparency-related harms within the
rideshare community. The significance of our study lies in not only uncovering
transparency harms, mitigation strategies and needs of rideshare workers, but
also proposing concrete solutions. Specifically, we advocate for policymakers
to call on rideshare platforms to regularly publish rideshare transparency
reports, echoing successful precedents in social media and AI regulation.
These reports, informed by worker’s transparency needs surfaced through our
study, detailing crucial indicators such as ride statistics and algorithmic
insights, can empower drivers and address their transparency needs. Taken
together, this study contributes to the ongoing discourse on platform
regulation and design, moving beyond discussions of information asymmetry to
envisioning platform design where transparency is prioritized, empowering
drivers to address power imbalances and resulting in improved rideshare worker
well-being.

Future work can investigate jurisdictions with established rideshare platform
regulation (e.g., Germany, Spain, UK (Espinoza, 2023)) or active resistance
(e.g., Colombia(Rueda, 2020), Brazil(Dickerson and Magalhaes, 2015)) to
understand workers’ transparency needs. Do transparency demands persist when
drivers are classified as employees receiving wages and benefits? Comparative
studies across regulatory environments could reveal valuable global insights
into the evolving rideshare platform-worker-policymaker dynamics.

## Acknowledgements

We acknowledge the Azure Cloud Computing Grant from CSML at Princeton which
enabled access to Microsoft Azure compute resources. We are grateful to Prof.
Lindsey Cameron, Prof. Amy X. Zhang, Prof. Min Kyung Lee, Prof. Toby Jia-Jun
Li, Prof. Christian Sandvig, Angie Zhang and Hope Schroeder for their broad
discussions. We thank Prof. Arvind Narayanan and Mihir Kshirsagar for their
insights on the generalization of this work and its policy implications. We
thank Prof. Brian Keegan for pointing us to the Reddit data source. We thank
Sayash Kapoor for insightful discussions on the conceptual and normative
implications of LLMs and prompting strategies. We are grateful to Vinayshekhar
Bannihatti Kumar for his valuable inputs on defining evaluation metrics. We
thank Sunnie Kim for her contributions on connections to Explainable AI. We
appreciate Elizabeth Anne Watkins for providing valuable critiques from an
interpretivist perspective, which helped reframe our work. We thank Yuhan Liu,
Beza Desta, and Kiyosu Maeda from the Princeton HCI group for their
annotations. We thank the participants and organizers of the CHI LLM as
Research Tools 2024 Workshop for their feedback. Finally, we thank the
anonymous CSCW and FAccT reviewers for their valuable feedback.

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##  Appendix A Interview Protocol and Screenshots

###  A.1. Interview Protocol

1) Introduction

Hi everyone, and thank you for coming. Today, we’re studying how clear or
“transparent” rideshare platforms’ behind-the-scenes rules, called
“algorithms,” are to you. These algorithms use AI and Machine Learning
techniques, which are basically computer programs that learn from yours and
other drivers driving patterns. We want to understand what you need to know to
do your job and any challenges you face when these rules aren’t clear. Our
ultimate goal is to advocate for greater transparency of these algorithms for
your benefit. Today’s activity will be structured based on the 3 stages of a
ride – at or before pickup, during the ride, at or after drop off.

2) Ice Breaker

  * •

Take 1 minute to draw a doodle of your self portrait. Write your name,
preferred pronouns, how long you’ve been driving for, and what app(s) you use.

  * •

We’ll then have you introduce yourself with your self portrait and describe
the most interesting ride you’ve ever done, in about 2-3 minutes.

3) Interview and Design Activity: Annotate Screenshots and Ask Probing
Questions

Here are 10 screenshots we found on public forums about various aspects of a
ride. We would like you to annotate these images. We’ll progress through the
screenshots sequentially.

This screenshot is about [insert topic e.g., surge].

  * •

Use the marker to circle information that you felt uncertain about. For
example, you might feel uncertain about something because you don’t know WHY
it’s being presented or HOW it was calculated.

  * •

Use the pen to write/draw what additional information you would have liked in
that context

[For each screenshot]

  * •

This screenshot is about [insert algorithmic decision / factor e.g. surge
pricing, acceptance rate, etc]. Reflecting on this screenshot and the
information you circled in red, could you share why you were uncertain about
that piece of information within the context of [algorithmic decision/factor]?

  * •

Can you describe a ride where you directly experienced or observed [insert
algorithmic decision] on your app? How did it guide or influence your actions?
How did it make you feel? (OR) Reflect on a time when you noticed [insert
algorithmic factor] on your app screen. How did this factor influence or shape
your decisions?

  * •

Reflecting on the information you’ve written using the pen, could you share
what additional information or context about [insert algorithmic decision /
algorithmic factor] on the screenshot would you have liked?

4) Conclusion

Alright, before we conclude our interview, I’d like to touch on a few more
points based on your annotated screenshots and the discussions we’ve had
regarding the three stages of a ride

  * •

What do you do when you feel like you don’t have enough information about a
ride? Do you turn to third-party apps (Mystro, Maximo, Gridwise, SurgeApp),
WhatsApp message groups, or Reddit forums, for example?

  * •

As you gained more experience with rideshare work, has your emotional response
to not getting adequate information about rides changed? Tell me about the
biggest differences between you as a driver when you started vs. where you are
today.

###  A.2. Screenshots and Associated Probing Questions

Figure 7. Screenshot 1

This screenshot is about the algorithms which determine the upfront price,
providing you with an offer and the route to a destination or drop off.

Probing questions

  * •

When you start your work day, how do you decide which app you want to use?
What additional information would be useful for you to have when choosing
which app(s) to turn on?

  * •

Tell me about how you decide if a trip is worthwhile. What additional
information do you wish you had?

  * •

Can you tell me about a trip you were offered recently that you thought was
really good? What made it “really good”?

  * •

Tell me about how you decide on what route to take for a ride. Can you tell me
more about what concerns you have when you need to make adjustments to the
suggested route, route closures, traffic etc?

  * •

What factors do you consider when you choose the location where you wait for
your first ride to come in? How does this compare to the information provided
by the app?

  * •

Can you tell me about what it’s like to wait for a passenger to show up for a
ride? What additional information do you wish you had? How long do you usually
wait for?

Figure 8. Screenshot 2

This screenshot is about the surge pricing algorithm.

Probing Questions

  * •

Do you ever consider surge pricing when picking a spot to wait for more
requests?

  * •

Can you tell me a little more about how you understand and work with surge?

  * •

Where and when do surges usually occur?

Figure 9. Screenshot 3

This screenshot is about the boost algorithm.

Probing Questions

  * •

Do you ever consider boost pricing when picking a spot to wait for more
requests?

  * •

Can you tell me a little more about how you understand and work with boost?

Figure 10. Screenshot 4

This screenshot is about the cancellation and acceptance rate.

Probing Questions

  * •

How do you think about the acceptance and cancellation rate?

  * •

How do the acceptance and cancellation rates influence your driving habits?

Figure 11. Screenshot 5

This screenshot is about quests.

Probing Questions

Can you tell me a little more about how you understand and work with quests?
How does it influence your selection of trips?

Figure 12. Screenshot 6

This screenshot if about ride offers you receive while you are on a ride

Probing Questions

What is your decision-making process like when you are offered a new ride
while driving?

Figure 13. Screenshot 7

This screenshot is about fare breakdown at the end of a ride.

Probing Questions

  * •

Can you tell me about how you think about your pay when you’re (1) waiting for
a rider before a pickup? (2) stopped for a rider in between a trip?

  * •

Have you noticed insurance deductions in the fee paid to Uber? Tell me a
little more about it.

  * •

Tell me what it’s like to do airport rides. How is it different compared to
city rides? What incentivizes you to consider airport rides? How do you think
the take rate varies for airport vs non airport rides?

Figure 14. Screenshot 8

This screenshot is about driver and rider, ratings and reviews

Probing Questions

  * •

How do you think about driver and rider ratings and reviews?

  * •

How does the driver and rider ratings and reviews influence your driving
habits?

Figure 15. Screenshot 9

This screenshot is about the trip radar. Trip radar is a feature that allows
drivers to see and respond to all nearby product requests. When a driver
declines a ride request, it gets relisted on the Trip Radar. The first driver
to tap on the Trip Radar screen gets the ride.

Probing Questions

  * •

Can you tell me a little more about how you understand and work with trip
radar?

  * •

What kind of ride requests do you think you get on trip radar?

  * •

Trip Radar is a relatively new feature. How are you made aware of new features
on the app?

Figure 16. Screenshot 10

This screenshot is about Uber Pro Rewards.

Probing Questions

  * •

Can you tell me a little more about how you understand and work with Uber Pro
rewards?

  * •

How does Uber Pro Rewards influence your driving habits?

##  Appendix B LLM for Analysis of Public Forum Data, Prompt Engineering,
Evaluation Methodology and Costs

###  B.1. Harnessing LLMs for Analysis of Online Public Forums’ Data to
Surface Harms and Concerns of a Community

In recent years, data from online public forums, have been used for research