We Put 7 Uber Drivers in One Room. What We Found Will Shock You.

00:12:02
https://www.youtube.com/watch?v=OEXJmNj6SPk

摘要

TLDRThe video explores algorithmic wage discrimination in ride-sharing companies like Uber and Lyft, where secret algorithms determine driver earnings. A law professor highlighted concerns that such technology could lead to widespread economic exploitation. The host conducts an experiment with several drivers to examine fare disparities, ultimately finding that drivers often receive different pay for the same services due to opaque pricing algorithms. This lack of transparency raises legal questions about the practices of these companies and calls for regulatory scrutiny to protect workers.

心得

  • 📊 Algorithmic wage discrimination impacts driver earnings.
  • 🔍 Secret algorithms dictate pay based on unseen factors.
  • 🚗 Ride-sharing companies offer different rates for the same rides.
  • 📉 Disparities can be up to $4 between drivers for identical services.
  • ⚖️ The legality of these practices is questionable under labor laws.
  • 💼 Drivers want independence but fair treatment as contractors.
  • 📈 Companies aim to maximize profits over fairness to drivers.
  • 🏢 Legal scrutiny needed for transparency in pricing and earnings.
  • 🤔 Opaque pricing systems lead to distrust among workers and consumers.
  • 🛡️ Regulatory bodies should investigate the implications of algorithmic pricing.

时间轴

  • 00:00:00 - 00:05:00

    In 2023, research revealed that companies like Uber and Lyft utilize secret algorithms for wage determination, leading to algorithmic wage discrimination against drivers. This technology affects not just ride-sharing but can extend to all types of pricing, influencing the economy without accountability. An investigation into wage disparities was initiated after a driver highlighted the issue, revealing that ride-sharing firms might be offering unequal pay for identical jobs due to undisclosed algorithmic adjustments, raising serious legal concerns about their practices.

  • 00:05:00 - 00:12:02

    An experiment conducted with drivers in Los Angeles demonstrated significant pay discrepancies, with some drivers receiving notably higher fares for the same rides. Attempts to clarify fare calculations with Uber and Lyft were met with vague responses. Critics argue that these practices may violate labor laws and reflect a system favored by the companies, where they maintain control over pricing while classifying drivers as independent contractors, thus limiting drivers' rights and compensation. This raises questions about the ethical and legal implications of employing such opaque wage-setting mechanisms.

思维导图

视频问答

  • What is algorithmic wage discrimination?

    Algorithmic wage discrimination refers to the use of secret algorithms by companies to determine wages for workers, leading to unfair pay disparities.

  • How do Uber and Lyft's algorithms affect driver earnings?

    Uber and Lyft's algorithms can pay drivers different rates for the same rides based on factors that are often hidden and unaccountable.

  • What was the outcome of the experiments conducted with drivers?

    The experiments showed that drivers were offered different fares for the same rides, with disparities of up to $4.

  • Are there any legal implications for these practices?

    Yes, the video suggests that these practices may violate labor laws and anti-discrimination regulations.

  • Why do drivers still want to work as independent contractors?

    Drivers prefer the flexibility of being independent contractors, but they also want fair wages and transparency in earnings.

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  • 00:00:00
    why does Uber win
  • 00:00:02
    well because it's rigged in their favor
  • 00:00:05
    right in 2023 a law professor published
  • 00:00:08
    a paper with shocking implications her
  • 00:00:11
    research found that Tech firms like uber
  • 00:00:13
    and Lyft were using secret algorithms to
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    dictate what drivers earn based on
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    factors we can't even see she called it
  • 00:00:21
    algorithmic wage
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    discrimination and this technology or
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    revolution isn't restricted to ride
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    share companies once that technology is
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    in there they can raise prices on a hot
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    summer day you know I want a lot of ice
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    cream I want
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    Lemonade they're running low they can
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    raise the price the price of milk even
  • 00:00:40
    your paycheck the very fabric of our
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    economy all controlled by invisible and
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    unaccountable
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    algorithms so about a year ago I started
  • 00:00:49
    reaching out to the
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    companies they largely denied
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    it only to contradict themselves a few
  • 00:00:57
    months
  • 00:00:58
    later then I reached out to the
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    academics and one of the frustrating
  • 00:01:02
    things was that the evidence seemed to
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    be in the shadows a single study a hint
  • 00:01:07
    in an earnings call a story from a
  • 00:01:09
    driver it was mostly
  • 00:01:11
    theoretical until a few months ago when
  • 00:01:14
    I got an email from a guy I had
  • 00:01:16
    published a video that argued Uber's
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    Newfound profitability came at the
  • 00:01:19
    expense of drivers and Riders he said we
  • 00:01:21
    were at the tip of the iceberg so I
  • 00:01:24
    called him we talked 3 weeks later I was
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    on a flight to Los Angeles looking to
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    settle the question one once and for all
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    are ride share companies offering
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    drivers different rates for the same
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    work what I found put the legality of
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    this whole Tech revolution in
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    question they have broken every
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    single local state federal law when it
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    comes to labor when it comes to
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    Transportation that's Sergio aedan he
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    sent me the email he's an experienced
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    driver and a senior contributor at the
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    ride here guy an influential resource
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    for Gig economy workers we were used to
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    something called a rate car we were used
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    to getting paid by time and distance
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    then overnight the transparency vanished
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    replaced by a secret algorithm that
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    takes into account potentially hundreds
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    of different variables they called it
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    upfront
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    pricing Uber's losses turned to profits
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    around that same time
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    coincidence I don't know lift followed
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    suit and now they claim they're on the
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    path to
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    profitability the algorithms
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    are set up in a
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    way to charge the rider as much as it's
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    possible and to pay the driver as little
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    as possible a lot of drivers feel Uber
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    and lft are paying different wages for
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    the same rides but we don't really know
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    because the companies they aren't
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    talking we're doing a very simple thing
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    we're picking up people point a to
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    dropping off point B I shouldn't make
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    less money than the next guy I should
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    not but because it's not regulated
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    because we don't have raid cards because
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    we don't know what we're going to make
  • 00:03:04
    trip to trip it's demoralizing It's
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    upsetting so with Sergio's help we
  • 00:03:09
    decided to test
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    it we called seven experienced
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    drivers to a hi traffic area in Los
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    Angeles with the sole purpose of testing
  • 00:03:21
    if ride Sher companies are paying
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    different rates for the same rides at
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    the start each driver will activate
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    their screen capture software open up
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    the app and place their phones on the
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    table why the table Sergio has done
  • 00:03:34
    similar experiments and the rate
  • 00:03:36
    disparities were blamed on location
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    differences when the experiments are
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    done the drivers will send their screen
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    recording to Sergio's team who will then
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    sync and analyze the
  • 00:03:47
    fairs and then we're going to see what's
  • 00:03:49
    really going on
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    [Music]
  • 00:04:10
    he's getting paid a dollar more if he
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    goes there according to the map and uh
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    nobody else is nobody else is seeing
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    that dollar
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    surge we found that Uber offered the
  • 00:04:21
    same rides 46 times to multiple drivers
  • 00:04:25
    63% of the time one of the drivers was
  • 00:04:27
    offered a bit less money for the exact
  • 00:04:30
    same ride they want to make few pennies
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    difference and few Pennies on 2.7
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    billion trips a quarter which is from
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    the last earnings report is millions
  • 00:04:40
    hundreds of millions of dollars lift was
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    even worse the price Gap wasn't just a
  • 00:04:44
    little off it was a lot off we saw
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    differences of up to $3 to4 after
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    bonuses and drivers don't know why one
  • 00:04:53
    person gets a bonus and another doesn't
  • 00:04:55
    well that adds up like on 30 chips at 10
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    bucks a day that's 300 bucks a month
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    why should I get paid less 300 bucks a
  • 00:05:02
    month than the other guy I contacted
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    Uber and lft to see how they calculate
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    fairs Lyft well they never responded
  • 00:05:09
    Uber directed me to a blog post when I
  • 00:05:11
    explained the experiment's results they
  • 00:05:13
    replied by copying and pasting a portion
  • 00:05:15
    of that same blog post the section
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    outlines different reasons drivers could
  • 00:05:19
    see different fairs but we controlled
  • 00:05:22
    for everything that we could we called
  • 00:05:24
    the drivers to the same space placed
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    their phones inches apart had driver
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    monitoring incoming promotions and
  • 00:05:32
    constantly refreshing their screen the
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    only thing we didn't account for was any
  • 00:05:37
    secret test Uber could have been running
  • 00:05:38
    at the time which we couldn't because
  • 00:05:41
    the fair algorithm is secret and the
  • 00:05:43
    test is secret there's a whole lot of
  • 00:05:45
    questions here like mainly is this legal
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    and there's no laws against algorithmic
  • 00:05:50
    wage
  • 00:05:51
    discrimination that article I mentioned
  • 00:05:53
    at the start suggests that opaque
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    algorithms can easily facilitate
  • 00:05:56
    violations of anti-discrimination laws
  • 00:05:59
    Uber denies this but their own research
  • 00:06:01
    reveals a glaring disparity wom drivers
  • 00:06:04
    earn 7% less than men potentially due to
  • 00:06:08
    algorithmic wage
  • 00:06:11
    setting but there's an argument that the
  • 00:06:13
    entire business model is illegal from
  • 00:06:16
    our perspective Uber's and lift's
  • 00:06:18
    business model is based on control uh
  • 00:06:21
    without responsibility that's David
  • 00:06:24
    seigman he's the executive director of
  • 00:06:26
    towards Justice a legal nonprofit that
  • 00:06:29
    defends workers from corporate overreach
  • 00:06:31
    in 2022 the organization helped lead a
  • 00:06:33
    lawsuit on behalf of three ride shair
  • 00:06:35
    drivers the lawsuit says if you are not
  • 00:06:38
    an employer um subject to your and
  • 00:06:41
    accountable to your workers under the
  • 00:06:43
    labor laws then you are powerful firms
  • 00:06:46
    that are abusing your power under State
  • 00:06:49
    uh antitrust competition law and unfair
  • 00:06:52
    and deceptive acent practices laws David
  • 00:06:55
    is describing the cognitive dissidence
  • 00:06:57
    rer operators have when classifying
  • 00:06:59
    driver they want to have it both ways
  • 00:07:01
    Uber and lift classifi drivers as
  • 00:07:03
    independent contractors not employers a
  • 00:07:05
    crucial
  • 00:07:06
    distinction as an employee you're making
  • 00:07:09
    money for someone else which means
  • 00:07:10
    benefits protections and a boss who can
  • 00:07:13
    tell you what time to show up but as an
  • 00:07:16
    independent contractor you're supposedly
  • 00:07:18
    making money for yourself with no
  • 00:07:20
    benefits or protections but Freedom
  • 00:07:23
    you're Your Own Boss if these drivers
  • 00:07:26
    are as you say independent then they
  • 00:07:28
    need to have true economic independence
  • 00:07:30
    we allege that the companies have denied
  • 00:07:32
    them that economic independence most
  • 00:07:34
    importantly by taking away their uh
  • 00:07:37
    their ability to set prices for
  • 00:07:39
    themselves but right now Uber LIF set
  • 00:07:41
    prices for Riders and wages for drivers
  • 00:07:43
    to maximize one thing their own profits
  • 00:07:46
    drivers are left out Uber and LIF are
  • 00:07:49
    saying we're not one company right we're
  • 00:07:52
    we're we're just a platform and all of
  • 00:07:54
    our drivers uh are um Independent
  • 00:07:58
    Business people
  • 00:08:00
    under our antitrust laws they need to
  • 00:08:02
    have the economic independence to set
  • 00:08:05
    their own prices we heard something
  • 00:08:08
    similar at our Roundtable discussion
  • 00:08:09
    after the
  • 00:08:11
    experiment every participant wanted to
  • 00:08:13
    be an independent contractor they like
  • 00:08:15
    the freedom they also want Uber and lift
  • 00:08:18
    to treat them like actual independent
  • 00:08:20
    contractors when I don't have a control
  • 00:08:22
    over the pricing and you keep calling me
  • 00:08:24
    an independent contractor there's
  • 00:08:25
    something very wrong with that picture
  • 00:08:28
    and for drivers dependent on the r your
  • 00:08:29
    platforms to make a living it's hard
  • 00:08:32
    when you're making under minimum wage
  • 00:08:34
    especially for people that do this
  • 00:08:36
    full-time in
  • 00:08:38
    California they're working 70 to 80
  • 00:08:40
    hours just to put food on the table
  • 00:08:42
    which is really really rough if you look
  • 00:08:44
    at who's on the board of both of these
  • 00:08:47
    companies it tells you that we have
  • 00:08:50
    nobody on our side right when the
  • 00:08:53
    general Council of uber is Tony West and
  • 00:08:56
    he's the brother-in-law of kamla Harris
  • 00:08:59
    then then it tells you that these
  • 00:09:01
    companies are tight with government why
  • 00:09:04
    does Uber win well because it's rigged
  • 00:09:06
    in their favor
  • 00:09:09
    right there's a reality where drivers
  • 00:09:12
    could be actual independent contractors
  • 00:09:14
    set their own rates on these platforms
  • 00:09:16
    and compete for your business but that's
  • 00:09:18
    not our world yet instead the companies
  • 00:09:22
    are spending hundreds of millions of
  • 00:09:24
    dollars rewriting State labor law to
  • 00:09:26
    reinforce the idea that drivers aren't
  • 00:09:28
    employees while using the fine print to
  • 00:09:30
    handle private challenges that's been
  • 00:09:33
    absolutely essential to Uber's and
  • 00:09:35
    lift's business model so well they've
  • 00:09:36
    tried to race to write laws to their
  • 00:09:39
    benefit they've also tried to protect
  • 00:09:41
    themselves from public litigation
  • 00:09:43
    through the fine print of their
  • 00:09:44
    contracts and through arbitration
  • 00:09:45
    clauses that's where David's case on
  • 00:09:47
    behalf of the three drivers ended a
  • 00:09:50
    superior court sent it to private
  • 00:09:52
    arbitration the end result isn't public
  • 00:09:54
    we should be also be clear about is just
  • 00:09:56
    because you're exempt from laws
  • 00:10:00
    governing you know for example
  • 00:10:01
    unemployment insurance premiums that
  • 00:10:03
    doesn't mean you're exempt from
  • 00:10:05
    Anti-Trust laws and competition
  • 00:10:07
    laws federal and state enforcers aren't
  • 00:10:11
    subject to forced arbitration agreements
  • 00:10:14
    they can investigate um and bring
  • 00:10:16
    enforcement actions against these R
  • 00:10:18
    share companies it's likely also a
  • 00:10:20
    violation of state competition law
  • 00:10:22
    especially in States like California
  • 00:10:24
    that prohibit vertical price fixing so
  • 00:10:26
    the California Attorney General's office
  • 00:10:28
    would also be in a prime position to
  • 00:10:29
    investigate the issue and bring an
  • 00:10:31
    enforcement action like that
  • 00:10:34
    one but this issue of algorithmic
  • 00:10:36
    pricing stretches Beyond ride share
  • 00:10:39
    we're just at the beginning stages of
  • 00:10:40
    what I feel is like a battle between the
  • 00:10:43
    common folk and then that manager class
  • 00:10:45
    and upwards there's credible reporting
  • 00:10:48
    that Walmart Amazon and McDonald's are
  • 00:10:50
    all experimenting with the technology
  • 00:10:53
    those companies employ over 3 million
  • 00:10:55
    people in July the FTC announced it was
  • 00:10:58
    investigating surveillance pricing
  • 00:11:00
    that's the practice of companies using
  • 00:11:02
    complex algorithms and Untold data
  • 00:11:04
    points to offer personalized pricing the
  • 00:11:06
    fear is that prices for something like
  • 00:11:08
    Children's Tylenol will suddenly
  • 00:11:09
    Skyrocket across major online stores
  • 00:11:12
    just because you typed infant fever into
  • 00:11:14
    Google why because retailers know you're
  • 00:11:17
    desperate the FDC is investigating
  • 00:11:19
    Walmart Amazon and the leading
  • 00:11:21
    technology vendors we need to do the
  • 00:11:23
    same thing but for wages the power of
  • 00:11:25
    these Giants lies in their ability to
  • 00:11:27
    keep everyone consumer
  • 00:11:29
    drivers Regulators blindfolded they're
  • 00:11:33
    backed by billions of dollars worth of
  • 00:11:35
    sophisticated algorithms and legal
  • 00:11:37
    gymnastics leaving us to fight in the
  • 00:11:39
    dark maybe there's an innocent
  • 00:11:41
    explanation to our Fair experiment but
  • 00:11:44
    we won't know until the federal
  • 00:11:45
    government shines a
  • 00:11:49
    light thanks for watching be sure to
  • 00:11:51
    like And subscribe to our Channel if you
  • 00:11:53
    have ideas for stories you want us to
  • 00:11:54
    uncover next please drop them in the
  • 00:11:56
    comments below
标签
  • Uber
  • Lyft
  • Algorithmic Wage Discrimination
  • Drivers
  • Gig Economy
  • Labor Laws
  • Ride-sharing
  • Pricing Algorithms
  • Transparency
  • Economic Independence