June 15, 2025 • 8 mins
Follow the story of Alex Rivera, a warehouse worker whose performance metrics suddenly plummeted after his company implemented an AI management system. Through his experience, we uncover how algorithmic management systems are reshaping workplace surveillance, productivity tracking, and employee evaluation. Former Silicon Valley engineer turned whistleblower Patricia Yang explains how these systems can be manipulated to justify layoffs, while labor rights attorney Michael Foster discusses emerging legal frameworks for algorithmic workplace rights. The episode reveals how AI management systems are spreading from warehouses to white-collar professions, fundamentally changing the nature of work itself.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:03):
AI ethics in everyday life. AI ethics in everyday life.
Speaker 2 (00:16):
Right now, as you're listening to this, dozens of algorithms
are making decisions about you. They're deciding what news you'll see,
whether you'll get that loan, and who you might fall
in love with, all without asking your permission or explaining
their reasoning. I'm Jason Park, and this is AI ethics
in everyday life, where we pull back the curtain on
the invisible digital forces, reshaping human experience one algorithm at
(00:37):
a time.
Speaker 3 (00:38):
So Alex Rivera's story, right, performance plummets after the AI
management system comes in classic it really highlights the potential
pitfalls of these systems.
Speaker 4 (00:47):
Yeah, and it's not just about individual cases like Alex's.
This is a systemic issue. We're talking about a fundamental
shift in workplace dynamics, how work is managed, how employees
are evaluated.
Speaker 3 (00:59):
Exactly. Episode description mentions algorithmic management systems impacting everything from
surveillance to productivity tracking to well, you know, deciding who
gets to keep their job.
Speaker 4 (01:10):
It's a bit unsettling, isn't it. This whole idea of
an algorithm being your.
Speaker 3 (01:13):
Boss definitely unsettling. I mean, where's the human element in
all of this, Well, where's the room for you know,
individual circumstances, explanations, nuance, Right.
Speaker 4 (01:22):
And that's where Patricia Yang's perspective comes in. A former
Silicon Valley engineer turned whistleblower. I'm really curious to hear
what she has to say about how these systems can
be manipulated.
Speaker 3 (01:32):
Oh, that's going to be insightful for sure, especially you
know the bit about justifying layoffs. That's a huge red flag.
Speaker 4 (01:39):
Absolutely, it raises some serious ethical questions. And then there's
the legal side of things. Michael Foster, the labor rights attorney,
will be discussing emerging legal frameworks for algorithmic workplace rights.
That's crucial.
Speaker 3 (01:50):
Yeah, we desperately need some legal clarity in this space because,
i mean, let's be honest, this is uncharted territory for
most of us totally.
Speaker 4 (01:56):
We're talking about a whole new set of rights that
need to be defined and protected. The right to well
not be managed by a biased algorithm, for example.
Speaker 3 (02:04):
Right, and the right to understand how these algorithms are
making decisions that affect your livelihood.
Speaker 4 (02:10):
Transparency is key. People deserve to know what metrics are
being used, how they're being weighted, and what recourse they
have if they believe the system is unfair.
Speaker 3 (02:19):
Absolutely, because you know, the thing is, this isn't just
about warehouse workers like Alex. The episode description mentions how
AI management systems are spreading dot.
Speaker 4 (02:27):
Two white collar professions exactly. This is something that could
potentially affect all of us. It's changing the very nature
of work itself.
Speaker 3 (02:36):
So algorithmic management it's everywhere, isn't it From right sharing
apps too well? Who knows what else these.
Speaker 4 (02:40):
Days it's spreading, that's for sure. And it's not just
about gigwork anymore. We're seeing it creep into all sorts
of industries.
Speaker 3 (02:46):
It's like this constant data collection, right surveillance tracking. It's
a bit unsettling.
Speaker 4 (02:53):
Yeah, the level of monitoring is intense. Everything's being measured, quantified,
every click, every keystroke, every pause.
Speaker 3 (02:59):
And then there's the decision making aspect algorithms deciding who
gets the best shifts, who gets promoted, who gets like
oh let go. That's a lot of power, right, a.
Speaker 4 (03:09):
Huge amount of power, and it raises some serious questions
about well, fairness, bias, transparency exactly.
Speaker 3 (03:16):
Transparency is key. People deserve to know how these systems work,
what metrics are being used, how they're being weighted, right.
Speaker 4 (03:23):
Because if you don't understand the rules of the game,
how can you even begin to play it, let alone win?
Speaker 3 (03:29):
And what about the human element? What does that fit
in with all this automation.
Speaker 4 (03:32):
That's a good question. I mean, are we just becoming
cogs in a giant digital machine. Where's the room for creativity,
for individual initiative.
Speaker 3 (03:42):
It's almost like we're being managed by robots and not
the friendly kind.
Speaker 4 (03:48):
Yeah, it's a bit dystopian, isn't it. This whole idea
of an algorithm being your boss.
Speaker 3 (03:52):
Definitely dystopium. But you know it's not all doom and gloom.
There are potential benefits too, right, Oh like what?
Speaker 1 (03:58):
Well?
Speaker 3 (03:59):
The proponents algorithmic management argue that it can create new opportunities,
improve efficiency, even promote fairness in some cases.
Speaker 4 (04:09):
I see. So it's not inherently bad. Then, it's more
about how it's implemented exactly.
Speaker 3 (04:14):
It's about finding the right balance between automation and human oversight,
between efficiency and well humanity.
Speaker 4 (04:24):
I think the key is to ensure that these systems
are used responsibly, ethically and transparently, and that workers have
a voice in the process.
Speaker 3 (04:34):
So this algorithmic management thing, it's kind of a big deal, right, Yeah.
Speaker 4 (04:40):
It's a pretty fundamental shift. I mean, we're talking about
algorithms taking over managerial functions, impacting everything from scheduling to
performance reviews.
Speaker 3 (04:50):
It's like scientific management on steroids, right, this scientific management
two point zero idea.
Speaker 4 (04:55):
That's one way to look at it. But it's also different,
more nuanced. Maybe it's not just about efficiency like tailorism.
Speaker 3 (05:03):
Wise, Oh how so?
Speaker 4 (05:05):
Well, think about platforms like Uber. They use algorithms not
just to optimize routes, but also to nudge driver behavior,
control pricing, even influence customer choices.
Speaker 3 (05:17):
Nudging. That's a whole other layer, isn't it subtle manipulation
through data Exactly?
Speaker 4 (05:21):
It's about shaping choices, not just dictating actions. And with
AI it becomes incredibly personalized, incredibly effective.
Speaker 3 (05:31):
I see. So it's not just about you know, making
workers faster or more productive, it's about controlling the entire ecosystem.
Speaker 4 (05:37):
Right yeah, And that's where the problems start to emerge,
this lack of transparency, the potential for bias.
Speaker 3 (05:45):
The bias, right, how do we even begin to address that?
When the decision making process is hidden inside a black box.
Speaker 4 (05:52):
It's a huge challenge, especially when these algorithms are making
decisions that affect people's livelihoods, who gets the best shifts,
who gets promoted, who gets let go.
Speaker 3 (06:04):
And it's not just gig workers anymore, is it. This
is spread into all sorts of industries, even white collar
jobs exactly.
Speaker 4 (06:10):
We're talking about a potential power imbalance. Workers feeling like
they have no control, no say, and how they're being managed.
Speaker 3 (06:17):
So it's almost like being managed by robots and not
the friendly kind.
Speaker 4 (06:23):
Yeah, it's a bit dystopian, isn't it. This whole idea
of an algorithm being your boss. Where's the human element
in all of this?
Speaker 3 (06:30):
So algorithmic management, it's not just about efficiency, right, it's
about control.
Speaker 4 (06:35):
Yeah, and that control can extend to reclassifying workers, especially
impacting migrant workers who might face worse conditions.
Speaker 3 (06:43):
That's a really critical point. And then there's the transparency issue.
It exacerbates the existing information asymmetry between employers and employees.
Speaker 4 (06:51):
It's like they're playing a game where only one side
knows the rules.
Speaker 3 (06:56):
Right exactly, and that lack of transparency has led to well,
you know, public outcry, lawsuits, even strikes. It's a big deal, it.
Speaker 4 (07:04):
Is, But there's hope, right at least in Europe, there
are legal frameworks that could promote transparency and protect worker rights.
Speaker 3 (07:11):
Yeah, the regulatory antibodies you mentioned, data protection laws, employment laws,
they could be key to uncovering and preventing abuses.
Speaker 4 (07:19):
It's about finding that balance, isn't it, between leveraging the
benefits of these systems and well safeguarding the rights of
the people who work within them. The next time an
app suggests something, a website shows you certain content, or
you get an unexpected decision from a company, ask yourself
what algorithm made this choice for me? And would I
(07:40):
have made the same one? Until next time, remember awareness
is the first step toward agency. Thanks for listening to
AI ethics in everyday life.
Speaker 1 (08:00):
In stupid, stupid, stupid stud stupidly stupid
AI ethics in everyday life. AI ethics in everyday life.
Speaker 2 (00:16):
Right now, as you're listening to this, dozens of algorithms
are making decisions about you. They're deciding what news you'll see,
whether you'll get that loan, and who you might fall
in love with, all without asking your permission or explaining
their reasoning. I'm Jason Park, and this is AI ethics
in everyday life, where we pull back the curtain on
the invisible digital forces, reshaping human experience one algorithm at
(00:37):
a time.
Speaker 3 (00:38):
So Alex Rivera's story, right, performance plummets after the AI
management system comes in classic it really highlights the potential
pitfalls of these systems.
Speaker 4 (00:47):
Yeah, and it's not just about individual cases like Alex's.
This is a systemic issue. We're talking about a fundamental
shift in workplace dynamics, how work is managed, how employees
are evaluated.
Speaker 3 (00:59):
Exactly. Episode description mentions algorithmic management systems impacting everything from
surveillance to productivity tracking to well, you know, deciding who
gets to keep their job.
Speaker 4 (01:10):
It's a bit unsettling, isn't it. This whole idea of
an algorithm being your.
Speaker 3 (01:13):
Boss definitely unsettling. I mean, where's the human element in
all of this, Well, where's the room for you know,
individual circumstances, explanations, nuance, Right.
Speaker 4 (01:22):
And that's where Patricia Yang's perspective comes in. A former
Silicon Valley engineer turned whistleblower. I'm really curious to hear
what she has to say about how these systems can
be manipulated.
Speaker 3 (01:32):
Oh, that's going to be insightful for sure, especially you
know the bit about justifying layoffs. That's a huge red flag.
Speaker 4 (01:39):
Absolutely, it raises some serious ethical questions. And then there's
the legal side of things. Michael Foster, the labor rights attorney,
will be discussing emerging legal frameworks for algorithmic workplace rights.
That's crucial.
Speaker 3 (01:50):
Yeah, we desperately need some legal clarity in this space because,
i mean, let's be honest, this is uncharted territory for
most of us totally.
Speaker 4 (01:56):
We're talking about a whole new set of rights that
need to be defined and protected. The right to well
not be managed by a biased algorithm, for example.
Speaker 3 (02:04):
Right, and the right to understand how these algorithms are
making decisions that affect your livelihood.
Speaker 4 (02:10):
Transparency is key. People deserve to know what metrics are
being used, how they're being weighted, and what recourse they
have if they believe the system is unfair.
Speaker 3 (02:19):
Absolutely, because you know, the thing is, this isn't just
about warehouse workers like Alex. The episode description mentions how
AI management systems are spreading dot.
Speaker 4 (02:27):
Two white collar professions exactly. This is something that could
potentially affect all of us. It's changing the very nature
of work itself.
Speaker 3 (02:36):
So algorithmic management it's everywhere, isn't it From right sharing
apps too well? Who knows what else these.
Speaker 4 (02:40):
Days it's spreading, that's for sure. And it's not just
about gigwork anymore. We're seeing it creep into all sorts
of industries.
Speaker 3 (02:46):
It's like this constant data collection, right surveillance tracking. It's
a bit unsettling.
Speaker 4 (02:53):
Yeah, the level of monitoring is intense. Everything's being measured, quantified,
every click, every keystroke, every pause.
Speaker 3 (02:59):
And then there's the decision making aspect algorithms deciding who
gets the best shifts, who gets promoted, who gets like
oh let go. That's a lot of power, right, a.
Speaker 4 (03:09):
Huge amount of power, and it raises some serious questions
about well, fairness, bias, transparency exactly.
Speaker 3 (03:16):
Transparency is key. People deserve to know how these systems work,
what metrics are being used, how they're being weighted, right.
Speaker 4 (03:23):
Because if you don't understand the rules of the game,
how can you even begin to play it, let alone win?
Speaker 3 (03:29):
And what about the human element? What does that fit
in with all this automation.
Speaker 4 (03:32):
That's a good question. I mean, are we just becoming
cogs in a giant digital machine. Where's the room for creativity,
for individual initiative.
Speaker 3 (03:42):
It's almost like we're being managed by robots and not
the friendly kind.
Speaker 4 (03:48):
Yeah, it's a bit dystopian, isn't it. This whole idea
of an algorithm being your boss.
Speaker 3 (03:52):
Definitely dystopium. But you know it's not all doom and gloom.
There are potential benefits too, right, Oh like what?
Speaker 1 (03:58):
Well?
Speaker 3 (03:59):
The proponents algorithmic management argue that it can create new opportunities,
improve efficiency, even promote fairness in some cases.
Speaker 4 (04:09):
I see. So it's not inherently bad. Then, it's more
about how it's implemented exactly.
Speaker 3 (04:14):
It's about finding the right balance between automation and human oversight,
between efficiency and well humanity.
Speaker 4 (04:24):
I think the key is to ensure that these systems
are used responsibly, ethically and transparently, and that workers have
a voice in the process.
Speaker 3 (04:34):
So this algorithmic management thing, it's kind of a big deal, right, Yeah.
Speaker 4 (04:40):
It's a pretty fundamental shift. I mean, we're talking about
algorithms taking over managerial functions, impacting everything from scheduling to
performance reviews.
Speaker 3 (04:50):
It's like scientific management on steroids, right, this scientific management
two point zero idea.
Speaker 4 (04:55):
That's one way to look at it. But it's also different,
more nuanced. Maybe it's not just about efficiency like tailorism.
Speaker 3 (05:03):
Wise, Oh how so?
Speaker 4 (05:05):
Well, think about platforms like Uber. They use algorithms not
just to optimize routes, but also to nudge driver behavior,
control pricing, even influence customer choices.
Speaker 3 (05:17):
Nudging. That's a whole other layer, isn't it subtle manipulation
through data Exactly?
Speaker 4 (05:21):
It's about shaping choices, not just dictating actions. And with
AI it becomes incredibly personalized, incredibly effective.
Speaker 3 (05:31):
I see. So it's not just about you know, making
workers faster or more productive, it's about controlling the entire ecosystem.
Speaker 4 (05:37):
Right yeah, And that's where the problems start to emerge,
this lack of transparency, the potential for bias.
Speaker 3 (05:45):
The bias, right, how do we even begin to address that?
When the decision making process is hidden inside a black box.
Speaker 4 (05:52):
It's a huge challenge, especially when these algorithms are making
decisions that affect people's livelihoods, who gets the best shifts,
who gets promoted, who gets let go.
Speaker 3 (06:04):
And it's not just gig workers anymore, is it. This
is spread into all sorts of industries, even white collar
jobs exactly.
Speaker 4 (06:10):
We're talking about a potential power imbalance. Workers feeling like
they have no control, no say, and how they're being managed.
Speaker 3 (06:17):
So it's almost like being managed by robots and not
the friendly kind.
Speaker 4 (06:23):
Yeah, it's a bit dystopian, isn't it. This whole idea
of an algorithm being your boss. Where's the human element
in all of this?
Speaker 3 (06:30):
So algorithmic management, it's not just about efficiency, right, it's
about control.
Speaker 4 (06:35):
Yeah, and that control can extend to reclassifying workers, especially
impacting migrant workers who might face worse conditions.
Speaker 3 (06:43):
That's a really critical point. And then there's the transparency issue.
It exacerbates the existing information asymmetry between employers and employees.
Speaker 4 (06:51):
It's like they're playing a game where only one side
knows the rules.
Speaker 3 (06:56):
Right exactly, and that lack of transparency has led to well,
you know, public outcry, lawsuits, even strikes. It's a big deal, it.
Speaker 4 (07:04):
Is, But there's hope, right at least in Europe, there
are legal frameworks that could promote transparency and protect worker rights.
Speaker 3 (07:11):
Yeah, the regulatory antibodies you mentioned, data protection laws, employment laws,
they could be key to uncovering and preventing abuses.
Speaker 4 (07:19):
It's about finding that balance, isn't it, between leveraging the
benefits of these systems and well safeguarding the rights of
the people who work within them. The next time an
app suggests something, a website shows you certain content, or
you get an unexpected decision from a company, ask yourself
what algorithm made this choice for me? And would I
(07:40):
have made the same one? Until next time, remember awareness
is the first step toward agency. Thanks for listening to
AI ethics in everyday life.
Speaker 1 (08:00):
In stupid, stupid, stupid stud stupidly stupid
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