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Lindsey D. Cameron explores how a new relation of production—the shift from human managers to algorithmic managers on digital platforms—manufactures workplace consent.

Image courtesy of interviewee. December 16, 2024

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This video is a transcription of a presentation discussing the author's personal experiences and research on the gig economy, specifically focusing on algorithmic management within ride-hailing services. It begins with the author's reflections on their mother's job loss during the Great Recession and the challenges she faced in finding new employment. This video motivates the author to study how individuals strive to maintain their socioeconomic status, often turning to gig work.

The presentation touches on the concept of algorithmic management, where algorithms control various HR functions in gig economy companies like Uber and Instacart. The author explores why gig workers often express satisfaction despite the precarious nature of their jobs, drawing on sociologist Michael Burawoy's work on consent in labor contexts.

Through qualitative research methods, including driving for a ride-hailing service and conducting interviews with other drivers, the author identifies tactics gig workers use in relation to algorithmic management. These tactics are categorized into "engagement" and "deviance," with workers making small choices within the constraints of the algorithm.

Engagement tactics involve following the system's expectations, while deviance includes actions that push against these rules. The author notes that while companies are aware of some deviant behaviors (like inflating ratings or selectively rejecting rides), they often tolerate them because the ultimate goal is to keep workers online and engaged with the platform.

The research also looks at how these dynamics might differ in global contexts, especially in the Global South, where gig work may be perceived as more prestigious compared to traditional taxi driving. Overall, the presentation emphasizes the complexities of autonomy and consent in algorithmically managed work environments.

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My mother lost her job at the

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height of the great Recession.

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So back in the late aughts,

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and she had a call, she had a, an associate's degree.

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She was a manager of a very big backend call center.

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So it's a really good job, particularly for a black woman,

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you know, who's born in the late fifties.

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And I saw after she got laid off, it was so hard

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for her to find another job.

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You know, age discrimination is very real here in America,

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I'm assuming too, where you're at.

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And I saw her really struggle to try to stay middle class,

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and she did that by doing a lot of gig work.

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So, you know, working at a warehouse in the middle

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of the night, selling food samples at grocery store,

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selling purses at trade shows.

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And this experience really stuck with me

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because so much of our research focuses on the elites

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or upward social mobility,

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and I cared about how do people try

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to stop downward social mobility.

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And more and more individuals are turning to the gig economy

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where, whether it's Uber, Upwork, Airbnb, Lyft, to try

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to make ends meet when they're going through hard times

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or they're going through layoffs.

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And that would, that's what led me to study this,

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what led me to work in one

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of these companies for several years.

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I was a driver for a ride healing company.

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And, you know, eventually for my mom

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and ended up working out, she ended up getting a,

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a traditional full-time job.

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And now she's retired and enjoying her grandkids.

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But it's her personal story.

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And, and watching her, you know, persevere, that,

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that led me to this research topic.

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So algorithmic management is when you have an algorithm

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that's in charge of hiring, firing, evaluating,

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disciplining workers, anything

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that you consider a core HR function

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that you have an algorithm in place with,

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that's algorithmic management.

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And a company like Ride Hailing or Instacart

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or Deliver Ru really rely on algorithmic management

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to make the entire system work.

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The basic question of why do people like their jobs so much,

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particularly when the jobs are not that great, you know,

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and Michael Buray has really the classic study,

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his book is called Manufacturing, consent.

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And he looks at individuals on a factory line

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or an assembly plant,

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and it's, he's looking at these machinists

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and he's like, why are they working so hard?

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You know, if the rate is to produce 80 widgets per hour,

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why are they all doing a hundred widgets per hour?

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And they all collectively seem to be doing this.

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But then there are times where they all collectively decide,

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I'm only gonna make 60 widgets per hour.

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So he's looking at what, you know, makes individuals

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to invest this discretionary effort

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into their jobs when they're under sort of, you know,

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more precarious or, you know, I would say, you know,

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bad labor conditions.

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So that's the core of the paper is looking at this idea

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of consent, which is from, you know,

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Michael Burwood's classic work in the sociology of work.

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And what does it look like when you have an algorithm

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as a boss as opposed to a human with coworkers.

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I'm a qualitative scholar,

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which means I get my hands dirty, I jump into the field.

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So as I alluded to before, I worked as a ride hailing driver

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Part-time over three years.

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And I also had a research assistant

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who drove in a different city

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because you can only drive where you're, you're in the state

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where your car is registered in.

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In addition to that, I did conversational interviews

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with people when I was in the back of, you know, the back

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of cars all across the United States.

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But really the, the core part

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beyond the observation were the semi-structured

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interviews I had with drivers.

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I've been following the same group of drivers now,

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interviewing them roughly every year, 18 months

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for the past, I guess seven years.

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So in addition to that interview corpus, my observation

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of a lot of data that's archival from the company Reddit

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forms, post on, you know, uber people.net,

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which is another type of form.

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So this is my a newspaper article.

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So it's a big corpus of qualitative data

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that I use for this particular paper.

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Why do people continue to invest in this type of work?

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That's really, it's hard labor, you know, no,

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employment protections, no, em, you know,

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no unemployment insurance, workers' comp.

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You have an algorithm that's directing you

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and theoretically, what I find in the paper is

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that there are these choices that individuals have

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with the algorithm management system.

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So just imagine that you're getting into a car

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and you're, now, you have

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to imagine about it from the driver's side,

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not the the passenger side

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and all the choices the driver makes.

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They have to decide when they're gonna open the app

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and where are they gonna swipe, yes

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or no to accept the ride.

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What turns they're gonna make is they're gonna go pick

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somebody up, then they bring somebody into the car

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with them, are they gonna talk to them or not?

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Usually people have their headphones on, so

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there's no decision there, they're not there to talk,

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and then they have to decide what to rate them.

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So there are all these very small choices.

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They get to make vis-a-vis the algorithm management system.

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And these are these small choices, I say

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that help manufacture consent

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because people feel like they have a sense of autonomy.

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The choice is real, but the choice is very, very narrow.

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So the paper really goes in depth

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what is happening at these small moments of choice.

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What I talk about are these small choices individuals have

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vis-a-vis the algorithmic management system.

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And there is a set of tech tactics that I call engagement

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and a set of tactics I call deviance.

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And then within those two buckets,

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the tactics can be simple or complex.

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So if you ask just a driver, Hey, you know, tell me

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what you like about driving, or Do you have

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choice or freedom?

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They'll say, yes, I can drive whenever I want,

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therefore I have autonomy.

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And really what this paper is trying to do is

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to go much deeper than that, such that superficial answer.

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So let me give you some examples from these tactics

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only in the engagement tactic

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that I call simple engagement is people

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who the choice is choosing when I'm going to drive the time

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or I'm accepting all the rides,

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but complex engagement is having a little bit more choice

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or thinking about, well, maybe I'm going to ignore

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These surges, or I'm actually gonna drive in the opposite

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direction, or I'm gonna optimize my pickup location

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and only turn it on when I'm at the bar.

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Drive somebody home, turn off the app, drive back

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to the bar and turn it back on.

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So these are all within the boundaries

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of the algorithmic management system.

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And I just, I call them engagement.

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You're basically following the directions, of

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what the system wants you to do.

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Now, deviance is a bit more complex

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and you might have some of your drivers mention this to you,

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but probably not.

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You know, you are essentially their manager,

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their customer in the backseat.

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So they're not gonna tell you some

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of these deviance tactics.

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And actually some of the drivers told me not to share some

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of these tactics in my paper in those

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they asked me not to share.

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I, I didn't, write about.

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But a simple deviance tactics is I'm gonna pre-select.

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I'm gonna say, I'm looking at your information when it's

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coming in, and I think, you have a bad rating.

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I don't like this neighborhood,

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or I'm just not gonna give you a ride, someone.

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And so there's screening

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and there's also pre-selecting people.

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So that's like, Hey, I know you Mark,

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you're sort of a good guy.

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You like to go to the airport early in the morning.

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I get the surge. And we set up an arrangement that I come

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to your house every morning at 6:00 AM when you have a trip

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and you get in my car

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and you click, you know, try, we try to get the algorithm

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to match with you inside the car.

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Now that's become harder and harder,

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but to do over the years

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with the algorithmic management system.

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And it's done some changes, but that's deviant

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'cause it's against what the algorithm management

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system wants you to do.

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It's against the rules to pre-select or screen.

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But people are able to to do that.

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And then the more complex deviance tactics are

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where people try to change the algorithm management system.

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So you have to have a certain rating

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to stay active on the system.

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And some people will try to inflate their rating.

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So if you're in a bad, if this was,

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talk about our imaginary, you know, passenger mark, again,

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mark's in the car with me, mark's in a bad mood,

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I can just tell he slammed the door,

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he is been talking to somebody on the phone.

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I do not want Mark to rate me.

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So what I'll do is I'll end my ride a little bit early.

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So, and I'll say, I, the, the ride ended,

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I canceled the ride, but I've already dropped him off

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or I've canceled the ride,

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but I'm driving him to his destination.

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So I'm still getting paid for the mileage that I drove

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for him, but because I cancel the ride, he can't rate me

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and he can't hurt my score.

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So in this way I keep my rating up.

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So, so people try to inflict their ratings,

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but they also try to inflate surges.

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And so this would be somebody who's, you know,

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it's like the Thursday, the Wednesday before Thanksgiving.

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It's a big American holiday either at a university trying

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to take students to the airport

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and they'll just, decline a lot

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of rise in the row on a row or they'll turn off the app

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and they'll just keep watching

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and see the surge price go up and up.

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So just to step back, what I've described are these tactics,

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these choices people have vis-a-vis the algorithm

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Management system.

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They can be simple or complex.

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They can be an alliance with the system such

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as an engagement tactics.

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They can sort of go against the system

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or push the boundaries in deviance.

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But these are all the elements of choice that workers have

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that ultimately generate consent.

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The short answer to this is yes, the

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companies know about these behaviors,

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particularly in the more extreme complex deviance ones

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where people are inflating surges

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or trying to inflate their ratings.

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And they'll will temporarily block people for two minutes,

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three minutes if they're declining a

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bunch of rides in a row.

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But what people will do,

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they have these very sophisticated workaround strategies.

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They might have two phones

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or they might do a hard set of their phones

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so it's not picked up upon.

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And so, or when they do a hard reset, they can just log on.

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So I would say for these more complex deviant strategies,

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the algorithm management system does counter.

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But at the very large level,

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I would say the company knows about all of these tactics.

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'cause it's really only the simple engagement ones.

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That's exactly what the system wants you to do.

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Everything else is sort of pushing the, the boundaries.

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But I would say the company turns a blind eye to this.

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You know, it's, it's like a gray zone.

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And the reason I would, the reason

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that the company turns up line eye is an on-demand company

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like this has two goals.

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The first, the most primary, the most basic goal

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that probably comes into mind are these incentives.

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And they want the workers to follow the incentives.

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So when there's high demand, they're dryers in the road.

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But the thing they really, really want is the individuals

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to stay online on the platform

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because it's called a on-demand company.

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If people aren't there on demand,

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there's no work being done,

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there's no revenue being generated.

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And so with all of these tactics, even if you're trying

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to pre-screen passengers, you're trying

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to inflate your rating, you're still online.

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This is still meeting the pen ultimate goal of the company.

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And so they're able to let this,

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to let these behaviors slide.

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And really any management company is like this in which you

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cannot have total compliance to the rules.

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You have these gray zones where you have

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to let individuals operate.

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I am still very deep in the ride hailing world.

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I'm now looking at the global south

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and how these companies have to change.

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And particularly not just the companies

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that the workers have to develop new practices

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to navigate this, their work

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environment in the global south.

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So, I'm in Latin America, Southeast Asia

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and Sub-Saharan Africa.

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Um, also, how do workers

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there perceive the work differently?

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You know, often the United States, this type

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of work is not considered very prestigious,

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but for many individuals in the global south,

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this is actually a pretty decent job

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and it's considered more high status than driving a taxi.

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So that's just some of my work about

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how does platform work evolve in different,

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different parts of the globe.

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