Angie: Our paper is called Algorithmic Management
Reimagined for Workers and By Workers: Centering Worker
Well-Being In Gig Work. This is the work of Angie Zhang,
Alexander Boltz, Chun-Wei Wang, and Min Kyung Lee.
Algorithmic management refers to the practice where rather than
human managers. workers are managed by software algorithms
or automated platforms. The gig economy in particular ---
encompassing short term, on-demand work, such as
rideshare drivers, freelance contractors, and so on --- has
been subject to algorithmic management that manages,
organizes, coordinates, and even evaluates workers.
Given the pervasiveness of gig work with as many as 55 million
Americans participating in some form in 2017, research has
focused on understanding how algorithmic management impacts
workers, as well as how they react to it. A specific focus
though on how algorithmic management impacts worker
well-being has been less explored. Thus two questions
driving our study were 1) how does algorithmic management
affect the well-being of gig workers? And 2) what designs or
solutions do gig workers have around how to improve
algorithmic management for their well-being?
We held focus groups, interviews, and workshops with
24 unique driver participants. We held initial focus groups and
interviews with drivers to understand how algorithmic
management impacts their well-being, defining it as the
following: physical, psychological and financial
well-being based on the Partnership on AI's Framework
for Promoting Workforce Well-Being in the AI-Integrated
Workplace.
In focus groups and interviews, we incorporated the use of
pairwise comparisons in order to discuss why drivers preferred
certain rides over others, and understand how these features of
the rides impact their well-being. We also asked them
to share their perceptions of platform fairness.
From focus groups and interviews, we constructed
participatory design workshop with workers to design solutions
that they envisioned for improving their own well-being.
In these workshops, we created a structure to support workers in
designing solutions. First, we displayed one of five prompts
about a driving scenario, which was created and informed by
focus groups and interviews. Then we asked workers questions
to probe how they believe platform algorithms to work as
well as their algorithmic imaginaries --- which is how
they envisioned the algorithm should work to support them ---
to help them start thinking about and sharing their
experiences.
Next, we assigned gig workers to an intervention so they could
consider the prompt through this lens and imagine how a solution
could be designed to fulfill their needs. The five prompts
covered the five principles for fair labor platform design, and
the three intervention types reflected the types of apps and
social media forums that drivers currently use for their work.
The final outcome wasn't to design the three interventions
in depth, but to use all of this to generate solutions and
analyze the types of solutions drivers came up with.
This is an example of one of the prompts --- driver's assessment
of work performance and well-being --- that they viewed
as a group and discussed before moving to breakout rooms to
consider the prompt through the lens of an intervention.
We structured our findings into four problems that we identified
drivers discussed and the solutions they came up with to
address these, through thematic analysis. And we'll discuss a
subset of these.
We heard early on during focus groups that drivers didn't feel
their well-being was supported by platforms. For example, "Uber
discourages you from taking that long view of the past month or
the past few previous months. It does remain very weekly
focused." Here, P11 indicates that financial well-being was
impacted by platforms structuring work statistics in a
way that prevents drivers from understanding their performance
in a meaningful way to support long term financial planning.
Drivers also felt they had to forego physical and
psychological well-being while working. They often felt
pressure to accept all rides that came their way to maintain
high ratings on the platform. But it meant things like risking
their physical safety and exhaustion. Drivers talked about
concerns about carjackings, and female drivers in particular
talked about a heightened awareness when driving at night.
One response drivers had for this problem revolved around a
strong inclination towards having more data driven insights
within the app. They suggested how data analytics could improve
well-being, such as making possible the creation of
balanced work schedules that take into account the three
well-beings.
Here P23 and 21 are discussing creating individualized plans
that allow them to know optimal times to work and rest to not be
mentally stressed about meeting their "bottom line" or financial
goals, suggesting how data analytics can allow drivers to
plan more, avoid driving while sleep-deprived, and encourage a
work life balance.
Gamification was brought up frequently as a source of
unfairness drivers faced on platforms. Those refers to
methods platforms use to encourage drivers to drive more.
"Quest" is a terminology specifically used by Uber where
the platform will offer different drivers different,
unequal driving bonuses. In the quote here, P11 explains that
Quests are presented to drivers inconsistently and based on
factors outside of their control. We also heard from
ride-, drivers, that gamification was noted by some
as detrimental to their physical or psychological well-being such
as when they're locked into a Quest they're struggling to
finish because rewards are "all or nothing", increasing health
hazards of workers overworking.
One of the solutions that drivers suggested for addressing
unfair differentials on the apps revolved around flexible
incentive configurations. Drivers imagined configuring
incentive offers they received, whether by switching between
Quest offers or implementing incremental payouts for ride
achievements, in order to support a healthier well-being.
One driver also suggested being able to customize incentives,
alluding to a pizza metaphor, where he could choose rewards
criteria like pizza toppings that apps like Uber could then
weight and rank against their own demand forecasting to
determine compensation.
The third problem participants identified was uneven
information access by the platform. For example, P3 says
"They definitely treat it like it's a perk, when it's kind of a
necessity." P3 is explaining that platforms unfairly withhold
information such as the rider's final destination, which drivers
often use as clues about their safety situation.
In their solutions, participants displayed an awareness for the
complexities of why platforms may not want to reveal all of
the ride request information. For example, P6 recognizes that
destination can be used to discriminate against riders and
not wanting to exacerbate that problem, suggests platforms
provide translucency such as direction or distance of a
destination rather than the exact destination to still
support drivers protecting their well-being.
Finally, drivers discuss how inherently their work isolates
them from their peers and from their platforms. "Our
communication network is pretty flimsy and non-existent. And so
we don't really have the strength in numbers that you see
in a lot of workplaces because we're so isolated." This
encapsulates the lack of formal ways to collaborate with one
another or receive support from the platform.
One of the ways participants viewed for ameliorating this was
through a platform or forum for collective information sharing
that could serve for investigative or advocacy
purposes. P17 illustrates here one way that collective
information or data sharing can assist drivers by supporting
them to investigate perceived inequalities. P17 has a
suspicion that Quest promotions become harder to accomplish as
drivers get closer to completing it, and imagines how they can
leverage aggregated driver data to investigate.
Participants envision how collective auditing or
collective information sharing can allow them to advocate for
worker rights by drawing out findings such as this, or even
supporting larger policy changes to support workers.
Our work explores and presents workers' ideas and imaginaries
on how to redesign algorithmic work features to support their
well-being. Workers offered realistic and
stakeholder-inclusive solutions that mutually align their
interests with platform business needs, such as allowing
information translucency over transparency to address
information asymmetries or suggesting configurable
incentives that platforms can then match to their own
compensation models.
Second, in discussing their fairness perceptions of
platforms, workers identified specific processes where
inequities exist. These can be avenues that HCI and human
centered data science researchers can look to for
supporting workers.
And finally, our findings offer nuanced understandings of how
workers view algorithmic management, fairness
perceptions, and platform impact on well-being. We contribute to
a growing understanding of the impact of algorithmic management
on worker well-being, sharing the ways that workers imagined
technology design that centers their well-being and work
preferences.