June 1, 2025 • 15 mins
James Martinez's perfect credit score couldn't save him from being denied a mortgage, leading to the discovery of alternative credit scoring systems using social media and shopping habits. We investigate how AI-powered financial assessment tools analyze everything from your purchase timing to your social connections to determine creditworthiness. Former fintech developer Lisa Wong reveals how these shadow scores are calculated, while consumer rights advocate Bernard Klein discusses the legal battles over algorithmic lending discrimination. The episode culminates in an exploration of how these systems particularly impact gig economy workers and small business owners.
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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
(00:37):
at a time. So how Ms Martinez's story really highlights
this unsettling trend of AI driven credit scoring a perfect
traditional credit score yet declined for a mortgage. It's a
head scratcher until you dig into these alternative systems.
Speaker 3 (00:51):
Exactly. It's like having a secret credit score you never
knew existed, built on data most people wouldn't even consider
financially relevant. Your social media, the activity, your online shopping habits,
it's all being fed into these algorithms, right.
Speaker 2 (01:04):
And that's where it gets tricky. We're talking about things
like when you buy groceries, who you're connected to online,
factors that have nothing to do with your ability to
repay a loan. Yet they can significantly impact your access
to credit.
Speaker 3 (01:16):
It's almost or willian, it really is, and the lack
of transparency is a huge concern. These algorithms are often proprietary,
meaning we don't know exactly how they work, what data
points they prioritize, or how they arrive at their decisions.
It's a black box, essentially.
Speaker 2 (01:31):
A black box with potentially discriminatory consequences. The input text
mentions racial biases baked into these systems, perpetuating existing inequalities.
Certain demographics systematically receive lower scores, limiting their opportunities for
home ownership, business loans, even employment. This isn't just about
financial access, It's about economic ability, social equity, fundamental rights.
Speaker 3 (01:53):
I see your point about systemic bias, but wouldn't the
algorithms reflect real world disparities in income and wealth distribution.
How can we be certain that these systems are inherently
discriminatory rather than mirroring pre existing social structures.
Speaker 2 (02:10):
That's the million dollar question, isn't it. While some might
argue that these systems simply reflect reality, studies reveal a
clear pattern of racial disparities and credit scoring, even when
controlling for traditional financial indicators. There's a deeper issue here,
a need for algorithmic accountability and fairness. We can't simply
accept the status.
Speaker 3 (02:27):
Quo absolutely, and the legal landscape is just starting to
catch up with this technology. The Fair Credit Reporting Act
of nineteen seventy, for instance, was designed for a pre
AI era. It doesn't adequately address the complexities and potential
biases of these new scoring systems. We need stronger consumer protections,
(02:47):
greater transparency, and mechanisms for redress when these systems make
unfair or inaccurate assessments.
Speaker 2 (02:54):
I couldn't agree more and think about the impact on
gig economy workers and small businessiness owners. Their income streams
are often less predictable, they're financial histories less traditional. These
alternative scoring systems could further disadvantage them, making it even
harder to access the credit they need to thrive. It's
a vicious cycle.
Speaker 3 (03:15):
Precisely, the very people who need access to capital the
most are often penalized by these systems. It's crucial that
we address these issues now before these AI powered tools
further entrench existing inequalities and create new barriers to economic opportunity.
The future of credit worthiness shouldn't be determined by a
(03:35):
hidden score based on opaque algorithms. It should be about fairness, transparency,
and equal access for all.
Speaker 2 (03:42):
So this whole credit scoring system, it's wow, it's a mess,
isn't it. We're talking about people being locked out of
basic necessities homes, cars, even jobs because of these opaque algorithms.
Speaker 3 (03:54):
It's deeply concerning. This twenty eighteen survey, the one by
student debt crisis in summer pain a stark picture. Nearly
sixty percent unable to make large purchases, over half blocked
from buying a home. It's crippling a generation.
Speaker 2 (04:07):
And it's not just about student debt, right, We're talking
about systemic issues with credit reporting itself, inaccuracies, biases, a
lack of transparency. It's a recipe for disaster.
Speaker 3 (04:18):
Exactly, hundreds of thousands of complaints filed with the CFPB
that speaks volumes, and the fact that credit reporting agencies
aren't incentivized to fix errors. It's a perverse system where
consumers are the product, not the customer.
Speaker 2 (04:33):
It's like, how can you dispute something when the system
itself is designed to make it nearly impossible. These agencies
are supposed to investigate, but let's well let's just say
they don't exactly have a stellar track record.
Speaker 3 (04:44):
And then there's the whole issue of algorithmic subjectivity. Right,
these AI powered systems, they're making judgments about people's credit
worthiness based on what social media activity, online shopping habits.
It's a slippery slope.
Speaker 2 (04:57):
It's terrifying. Really, we're talking about digital redlining, potential for
discrimination baked into these algorithms. It's like history repeating itself,
but this time it's hidden behind lines of code, and
the lack.
Speaker 3 (05:08):
Of transparency makes it even worse. These algorithms are proprietary
black boxes. We don't know how they work, what data
they're using, or how they're arriving at their decisions.
Speaker 2 (05:16):
It's unsettling, absolutely, And what about the people who are
credit and visible, Those who don't use credit or can't
access it. They're penalized by a system that's supposed to
assess financial responsibility.
Speaker 3 (05:27):
It's a catch twenty two. You need credit to build credit,
but if you can't get credit in the first place,
well you're stuck. And this disproportionately affects certain communities like
Hispanic Americans who often rely on cash and family resources.
Speaker 2 (05:43):
Right, and millennials too. They're using Venmo mobile payment apps,
things that don't show up on traditional credit reports, so
they're being deemed credit and visible, even though they might
be perfectly responsible with their finances.
Speaker 3 (05:54):
It's a fundamental flaw in the system, and these alternative
scoring systems, while they claim to address credit and visibility,
they also raise a whole host of new concerns.
Speaker 2 (06:03):
Oh like what.
Speaker 3 (06:05):
Well the invasiveness of the data collection. For one, they're
looking at everything, your social media, your browsing history, even
your utility payments. It's like Big Brother on steroids, and.
Speaker 2 (06:16):
The potential for discrimination is huge. These algorithms can perpetuate
existing biases, leading to unfair and inaccurate assessments. It's a
real threat to economic mobility and social equity precisely.
Speaker 3 (06:29):
And then there's the question of whether these scores are
even accurate predictors of risk. Some argue that they're not,
that they penalize people who are financially responsible but simply
don't fit the traditional credit mold.
Speaker 2 (06:41):
It's a complex issue, no doubt, but one thing's clear.
We need greater transparency, stronger consumer protections, and a serious
overhaul of the entire credit scoring system. It's impacting people's
lives in profound ways, so we can't afford to ignore
it any longer.
Speaker 3 (06:55):
I couldn't agree more. The future of credit worthiness shouldn't
be determined by hidden algorithms and okaque data. It should
be about fairness, accuracy, and equal opportunity for all. And
until we achieve that, we'll continue to see people like
Hams Martinez being unfairly denied the opportunities they deserve.
Speaker 2 (07:13):
So we're talking about these predictive algorithms, these scoring systems
that are essentially determined people's life chances, right, Yeah.
Speaker 3 (07:19):
And it's not just about credit scores anymore. It's employment, housing,
even travel. These algorithms are everywhere making decisions about us
based on data we often don't even know they're collecting.
Speaker 2 (07:30):
It's like Shaman's a digital cast system almost where these scores,
these invisible numbers, are determining where we fit in society.
Speaker 3 (07:40):
And the scary part is these scores can become self
fulfilling prophecies. If you're labeled a high risk, you're more
likely to be denied opportunities, which then reinforces that high
risk label. It's a vicious cycle, exactly like.
Speaker 2 (07:53):
If you're flagged as a potential credit risk, the cost
of borrowing goes up, making it even harder to get
back on your feet. It's like the system is rigged
against you.
Speaker 3 (08:02):
And who's holding these algorithms accountable? That's the key question here.
These systems are often proprietary black boxes. We don't know
how they work, what data they're using, or how they're
arriving at their decisions.
Speaker 2 (08:13):
Yeah, right, Pesqually and Citron, they're arguing for moral justification. Right,
They're saying these algorithms have such a profound impact on
people's lives that they need to be held to a
higher standard.
Speaker 3 (08:23):
Absolutely, we need transparency, we need accountability, we need to
understand how these decisions are being made, and we need
a way to challenge them when they're wrong.
Speaker 2 (08:31):
Because, let's be honest, these systems are not infallible. They're
based on data, and data can be flawed, biased, incomplete,
and when those flaws lead to discriminatory outcomes, that's a
serious problem.
Speaker 3 (08:42):
It's like, how can we trust these algorithms to make
fair and accurate assessments when they're built on data that
reflects existing inequalities. It's like using a broken ruler to
measure something you're never going to get an accurate result.
Speaker 2 (08:54):
And what about the people who are credit invisible, those
who don't use traditional credit or can't access it. They're
penalized by system that's supposed to assess financial responsibility, but
they're not even given a chance to participate.
Speaker 3 (09:04):
It's a catch twenty two. You need credit to build credit,
but if you can't get credit in the first place,
well you're locked out. And this disproportionately affects certain communities,
further exacerbating existing inequalities.
Speaker 2 (09:17):
It's a mess, really, and it's not just about individual hardship,
it's about systemic injustice. These algorithms are perpetuating and amplifying
existing biases, creating a society where opportunity is increasingly determined
by factors beyond our control.
Speaker 3 (09:33):
And until we address these issues, the lack of transparency,
the potential for discrimination, the lack of accountability, we're going
to continue to see people unfairly denied the opportunities they deserve.
It's a critical issue and one that we can't afford
to ignore any longer.
Speaker 2 (09:52):
So these alternative scoring systems, they're not just looking at
your Fyco score anymore, right, It's way more invasive than.
Speaker 3 (09:57):
That, it's unsettling, to say the least. They're scriping data
from everywhere, social media, online shopping habits, even your geolocation data.
It's like they're building a complete digital profile of.
Speaker 2 (10:09):
You and that data. It's not always accurate, is it.
I mean, a single negative review on a product could
potentially impact your credit worthiness. That seems absurd, it.
Speaker 3 (10:19):
Is, and think about the potential for bias these algorithms.
They're trained on an existing data, which often reflects societal biases.
So you're essentially baking those biases into the system.
Speaker 2 (10:30):
Which then perpetuates those biases, creating a self fulfilling prophecy.
Speaker 3 (10:33):
It's a vicious cycle, exactly, and the lack of transparency
makes it even worse. These algorithms are often proprietary black boxes.
You don't know how they work, what data they're using,
or how they're arriving at their decisions.
Speaker 2 (10:45):
It's like being judged by a secret court with no
right to appeal. You're guilty until proven innocent, but you
don't even know what you're being.
Speaker 3 (10:52):
Accused of, right and what about the people who are
credit invisible, those who don't use credit or can't access it.
They're penalized by a system that's supposed to assess financial responsibility,
but they're not even given a chance to participate.
Speaker 2 (11:06):
It's a catch twenty two. You need credit to build credit,
but if you can't get credit in the first place,
well you're locked out. And this disproportionally affects certain communities,
exacerbating existing inequalities.
Speaker 3 (11:18):
It's a fundamental flaw in the system, and these alternative
scoring systems, while they claim to address credit and visibility,
they also raise a whole host of new concerns. The
sheer volume of data being collected is overwhelming. It's information
overload on steroids.
Speaker 2 (11:35):
And how do we even know this data is being
used responsibly. There's no real oversight, no regulation to speak of.
It's the wild West of data.
Speaker 3 (11:43):
Collection, and the potential for misuse is huge. This data
could be used for targeted advertising, price discrimination, even political manipulation.
It's a slippery slope.
Speaker 2 (11:55):
It's a real threat to privacy, to autonomy, to basic
human rights. To be having a serious conversation about the
ethical implications of these technologies before it's too late.
Speaker 3 (12:05):
We need greater transparency, stronger consumer protections, and a serious
overhaul of the entire credit scoring system. It's impacting people's
lives in profound ways, and we can't afford to ignore
it any longer. The future of credit worthiness shouldn't be
determined by hidden algorithms and opaque data. It should be
about fairness, accuracy, and equal opportunity for all. And until
(12:28):
we achieve that, will continue to see people being unfairly
deny the opportunities they deserve.
Speaker 2 (12:34):
So it's almost like these alternative scoring systems are creating
a parallel financial universe, right, one where the rules are
hidden and the judgments are made by algorithms we don't understand.
Speaker 3 (12:46):
It's a digital shadow economy almost, And the people who
are most vulnerable the gig workers, the small business owners,
those with limited credit histories. They're the ones who are
most likely to be penalized by this system.
Speaker 2 (13:00):
Like digital redlining almost where these invisible lines being drawn
based on data we don't even know is being collected.
Speaker 3 (13:08):
And the potential for discrimination is huge. These algorithms can
perpetuate existing biases, leading to unfair and inaccurate assessments. It's
a real threat to economic mobility and social equity right.
Speaker 2 (13:21):
And the lack of transparency just exacerbates the problem. These
algorithms are proprietary black boxes. We don't know how they work,
what data they're using, or how they're arriving at their decisions.
Speaker 3 (13:33):
It's like being judged by a secret court with no
right to appeal. You're guilty until proven innocent, but you
don't even know what you're being accused of.
Speaker 2 (13:41):
It's a fundamental flaw in the system, and these alternative
scoring systems, while they claim to address credit and visibility,
they also raise a whole host of new concerns.
Speaker 3 (13:51):
The sheer volume of data being collected is overwhelming. It's
information overload on steroids. And how do we even know
this data is being used responsibly. There's no real oversight,
no regulation to speak of. It's the wild West of
data collection.
Speaker 2 (14:10):
And the potential for misuse is huge. This data could
be used for targeted advertising, price discrimination, even political manipulation.
It's a slippery slope. Where does it end.
Speaker 3 (14:19):
It's a real threat to privacy, to autonomy, to basic
human rights. We need to be having a serious conversation
about the ethical implications of these technologies before it's too late.
We need greater transparency, stronger consumer protections, and a serious
overhaul of the entire credit scoring system. The next time
an app suggests something, a website shows you certain content,
(14:42):
or you get an unexpected decision from a company, ask
yourself what algorithm made this choice for me? And would
I 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 (15:01):
Stupid step step step step, stant step, stupid st
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
(00:37):
at a time. So how Ms Martinez's story really highlights
this unsettling trend of AI driven credit scoring a perfect
traditional credit score yet declined for a mortgage. It's a
head scratcher until you dig into these alternative systems.
Speaker 3 (00:51):
Exactly. It's like having a secret credit score you never
knew existed, built on data most people wouldn't even consider
financially relevant. Your social media, the activity, your online shopping habits,
it's all being fed into these algorithms, right.
Speaker 2 (01:04):
And that's where it gets tricky. We're talking about things
like when you buy groceries, who you're connected to online,
factors that have nothing to do with your ability to
repay a loan. Yet they can significantly impact your access
to credit.
Speaker 3 (01:16):
It's almost or willian, it really is, and the lack
of transparency is a huge concern. These algorithms are often proprietary,
meaning we don't know exactly how they work, what data
points they prioritize, or how they arrive at their decisions.
It's a black box, essentially.
Speaker 2 (01:31):
A black box with potentially discriminatory consequences. The input text
mentions racial biases baked into these systems, perpetuating existing inequalities.
Certain demographics systematically receive lower scores, limiting their opportunities for
home ownership, business loans, even employment. This isn't just about
financial access, It's about economic ability, social equity, fundamental rights.
Speaker 3 (01:53):
I see your point about systemic bias, but wouldn't the
algorithms reflect real world disparities in income and wealth distribution.
How can we be certain that these systems are inherently
discriminatory rather than mirroring pre existing social structures.
Speaker 2 (02:10):
That's the million dollar question, isn't it. While some might
argue that these systems simply reflect reality, studies reveal a
clear pattern of racial disparities and credit scoring, even when
controlling for traditional financial indicators. There's a deeper issue here,
a need for algorithmic accountability and fairness. We can't simply
accept the status.
Speaker 3 (02:27):
Quo absolutely, and the legal landscape is just starting to
catch up with this technology. The Fair Credit Reporting Act
of nineteen seventy, for instance, was designed for a pre
AI era. It doesn't adequately address the complexities and potential
biases of these new scoring systems. We need stronger consumer protections,
(02:47):
greater transparency, and mechanisms for redress when these systems make
unfair or inaccurate assessments.
Speaker 2 (02:54):
I couldn't agree more and think about the impact on
gig economy workers and small businessiness owners. Their income streams
are often less predictable, they're financial histories less traditional. These
alternative scoring systems could further disadvantage them, making it even
harder to access the credit they need to thrive. It's
a vicious cycle.
Speaker 3 (03:15):
Precisely, the very people who need access to capital the
most are often penalized by these systems. It's crucial that
we address these issues now before these AI powered tools
further entrench existing inequalities and create new barriers to economic opportunity.
The future of credit worthiness shouldn't be determined by a
(03:35):
hidden score based on opaque algorithms. It should be about fairness, transparency,
and equal access for all.
Speaker 2 (03:42):
So this whole credit scoring system, it's wow, it's a mess,
isn't it. We're talking about people being locked out of
basic necessities homes, cars, even jobs because of these opaque algorithms.
Speaker 3 (03:54):
It's deeply concerning. This twenty eighteen survey, the one by
student debt crisis in summer pain a stark picture. Nearly
sixty percent unable to make large purchases, over half blocked
from buying a home. It's crippling a generation.
Speaker 2 (04:07):
And it's not just about student debt, right, We're talking
about systemic issues with credit reporting itself, inaccuracies, biases, a
lack of transparency. It's a recipe for disaster.
Speaker 3 (04:18):
Exactly, hundreds of thousands of complaints filed with the CFPB
that speaks volumes, and the fact that credit reporting agencies
aren't incentivized to fix errors. It's a perverse system where
consumers are the product, not the customer.
Speaker 2 (04:33):
It's like, how can you dispute something when the system
itself is designed to make it nearly impossible. These agencies
are supposed to investigate, but let's well let's just say
they don't exactly have a stellar track record.
Speaker 3 (04:44):
And then there's the whole issue of algorithmic subjectivity. Right,
these AI powered systems, they're making judgments about people's credit
worthiness based on what social media activity, online shopping habits.
It's a slippery slope.
Speaker 2 (04:57):
It's terrifying. Really, we're talking about digital redlining, potential for
discrimination baked into these algorithms. It's like history repeating itself,
but this time it's hidden behind lines of code, and
the lack.
Speaker 3 (05:08):
Of transparency makes it even worse. These algorithms are proprietary
black boxes. We don't know how they work, what data
they're using, or how they're arriving at their decisions.
Speaker 2 (05:16):
It's unsettling, absolutely, And what about the people who are
credit and visible, Those who don't use credit or can't
access it. They're penalized by a system that's supposed to
assess financial responsibility.
Speaker 3 (05:27):
It's a catch twenty two. You need credit to build credit,
but if you can't get credit in the first place,
well you're stuck. And this disproportionately affects certain communities like
Hispanic Americans who often rely on cash and family resources.
Speaker 2 (05:43):
Right, and millennials too. They're using Venmo mobile payment apps,
things that don't show up on traditional credit reports, so
they're being deemed credit and visible, even though they might
be perfectly responsible with their finances.
Speaker 3 (05:54):
It's a fundamental flaw in the system, and these alternative
scoring systems, while they claim to address credit and visibility,
they also raise a whole host of new concerns.
Speaker 2 (06:03):
Oh like what.
Speaker 3 (06:05):
Well the invasiveness of the data collection. For one, they're
looking at everything, your social media, your browsing history, even
your utility payments. It's like Big Brother on steroids, and.
Speaker 2 (06:16):
The potential for discrimination is huge. These algorithms can perpetuate
existing biases, leading to unfair and inaccurate assessments. It's a
real threat to economic mobility and social equity precisely.
Speaker 3 (06:29):
And then there's the question of whether these scores are
even accurate predictors of risk. Some argue that they're not,
that they penalize people who are financially responsible but simply
don't fit the traditional credit mold.
Speaker 2 (06:41):
It's a complex issue, no doubt, but one thing's clear.
We need greater transparency, stronger consumer protections, and a serious
overhaul of the entire credit scoring system. It's impacting people's
lives in profound ways, so we can't afford to ignore
it any longer.
Speaker 3 (06:55):
I couldn't agree more. The future of credit worthiness shouldn't
be determined by hidden algorithms and okaque data. It should
be about fairness, accuracy, and equal opportunity for all. And
until we achieve that, we'll continue to see people like
Hams Martinez being unfairly denied the opportunities they deserve.
Speaker 2 (07:13):
So we're talking about these predictive algorithms, these scoring systems
that are essentially determined people's life chances, right, Yeah.
Speaker 3 (07:19):
And it's not just about credit scores anymore. It's employment, housing,
even travel. These algorithms are everywhere making decisions about us
based on data we often don't even know they're collecting.
Speaker 2 (07:30):
It's like Shaman's a digital cast system almost where these scores,
these invisible numbers, are determining where we fit in society.
Speaker 3 (07:40):
And the scary part is these scores can become self
fulfilling prophecies. If you're labeled a high risk, you're more
likely to be denied opportunities, which then reinforces that high
risk label. It's a vicious cycle, exactly like.
Speaker 2 (07:53):
If you're flagged as a potential credit risk, the cost
of borrowing goes up, making it even harder to get
back on your feet. It's like the system is rigged
against you.
Speaker 3 (08:02):
And who's holding these algorithms accountable? That's the key question here.
These systems are often proprietary black boxes. We don't know
how they work, what data they're using, or how they're
arriving at their decisions.
Speaker 2 (08:13):
Yeah, right, Pesqually and Citron, they're arguing for moral justification. Right,
They're saying these algorithms have such a profound impact on
people's lives that they need to be held to a
higher standard.
Speaker 3 (08:23):
Absolutely, we need transparency, we need accountability, we need to
understand how these decisions are being made, and we need
a way to challenge them when they're wrong.
Speaker 2 (08:31):
Because, let's be honest, these systems are not infallible. They're
based on data, and data can be flawed, biased, incomplete,
and when those flaws lead to discriminatory outcomes, that's a
serious problem.
Speaker 3 (08:42):
It's like, how can we trust these algorithms to make
fair and accurate assessments when they're built on data that
reflects existing inequalities. It's like using a broken ruler to
measure something you're never going to get an accurate result.
Speaker 2 (08:54):
And what about the people who are credit invisible, those
who don't use traditional credit or can't access it. They're
penalized by system that's supposed to assess financial responsibility, but
they're not even given a chance to participate.
Speaker 3 (09:04):
It's a catch twenty two. You need credit to build credit,
but if you can't get credit in the first place,
well you're locked out. And this disproportionately affects certain communities,
further exacerbating existing inequalities.
Speaker 2 (09:17):
It's a mess, really, and it's not just about individual hardship,
it's about systemic injustice. These algorithms are perpetuating and amplifying
existing biases, creating a society where opportunity is increasingly determined
by factors beyond our control.
Speaker 3 (09:33):
And until we address these issues, the lack of transparency,
the potential for discrimination, the lack of accountability, we're going
to continue to see people unfairly denied the opportunities they deserve.
It's a critical issue and one that we can't afford
to ignore any longer.
Speaker 2 (09:52):
So these alternative scoring systems, they're not just looking at
your Fyco score anymore, right, It's way more invasive than.
Speaker 3 (09:57):
That, it's unsettling, to say the least. They're scriping data
from everywhere, social media, online shopping habits, even your geolocation data.
It's like they're building a complete digital profile of.
Speaker 2 (10:09):
You and that data. It's not always accurate, is it.
I mean, a single negative review on a product could
potentially impact your credit worthiness. That seems absurd, it.
Speaker 3 (10:19):
Is, and think about the potential for bias these algorithms.
They're trained on an existing data, which often reflects societal biases.
So you're essentially baking those biases into the system.
Speaker 2 (10:30):
Which then perpetuates those biases, creating a self fulfilling prophecy.
Speaker 3 (10:33):
It's a vicious cycle, exactly, and the lack of transparency
makes it even worse. These algorithms are often proprietary black boxes.
You don't know how they work, what data they're using,
or how they're arriving at their decisions.
Speaker 2 (10:45):
It's like being judged by a secret court with no
right to appeal. You're guilty until proven innocent, but you
don't even know what you're being.
Speaker 3 (10:52):
Accused of, right and what about the people who are
credit invisible, those who don't use credit or can't access it.
They're penalized by a system that's supposed to assess financial responsibility,
but they're not even given a chance to participate.
Speaker 2 (11:06):
It's a catch twenty two. You need credit to build credit,
but if you can't get credit in the first place,
well you're locked out. And this disproportionally affects certain communities,
exacerbating existing inequalities.
Speaker 3 (11:18):
It's a fundamental flaw in the system, and these alternative
scoring systems, while they claim to address credit and visibility,
they also raise a whole host of new concerns. The
sheer volume of data being collected is overwhelming. It's information
overload on steroids.
Speaker 2 (11:35):
And how do we even know this data is being
used responsibly. There's no real oversight, no regulation to speak of.
It's the wild West of data.
Speaker 3 (11:43):
Collection, and the potential for misuse is huge. This data
could be used for targeted advertising, price discrimination, even political manipulation.
It's a slippery slope.
Speaker 2 (11:55):
It's a real threat to privacy, to autonomy, to basic
human rights. To be having a serious conversation about the
ethical implications of these technologies before it's too late.
Speaker 3 (12:05):
We need greater transparency, stronger consumer protections, and a serious
overhaul of the entire credit scoring system. It's impacting people's
lives in profound ways, and we can't afford to ignore
it any longer. The future of credit worthiness shouldn't be
determined by hidden algorithms and opaque data. It should be
about fairness, accuracy, and equal opportunity for all. And until
(12:28):
we achieve that, will continue to see people being unfairly
deny the opportunities they deserve.
Speaker 2 (12:34):
So it's almost like these alternative scoring systems are creating
a parallel financial universe, right, one where the rules are
hidden and the judgments are made by algorithms we don't understand.
Speaker 3 (12:46):
It's a digital shadow economy almost, And the people who
are most vulnerable the gig workers, the small business owners,
those with limited credit histories. They're the ones who are
most likely to be penalized by this system.
Speaker 2 (13:00):
Like digital redlining almost where these invisible lines being drawn
based on data we don't even know is being collected.
Speaker 3 (13:08):
And the potential for discrimination is huge. These algorithms can
perpetuate existing biases, leading to unfair and inaccurate assessments. It's
a real threat to economic mobility and social equity right.
Speaker 2 (13:21):
And the lack of transparency just exacerbates the problem. These
algorithms are proprietary black boxes. We don't know how they work,
what data they're using, or how they're arriving at their decisions.
Speaker 3 (13:33):
It's like being judged by a secret court with no
right to appeal. You're guilty until proven innocent, but you
don't even know what you're being accused of.
Speaker 2 (13:41):
It's a fundamental flaw in the system, and these alternative
scoring systems, while they claim to address credit and visibility,
they also raise a whole host of new concerns.
Speaker 3 (13:51):
The sheer volume of data being collected is overwhelming. It's
information overload on steroids. And how do we even know
this data is being used responsibly. There's no real oversight,
no regulation to speak of. It's the wild West of
data collection.
Speaker 2 (14:10):
And the potential for misuse is huge. This data could
be used for targeted advertising, price discrimination, even political manipulation.
It's a slippery slope. Where does it end.
Speaker 3 (14:19):
It's a real threat to privacy, to autonomy, to basic
human rights. We need to be having a serious conversation
about the ethical implications of these technologies before it's too late.
We need greater transparency, stronger consumer protections, and a serious
overhaul of the entire credit scoring system. The next time
an app suggests something, a website shows you certain content,
(14:42):
or you get an unexpected decision from a company, ask
yourself what algorithm made this choice for me? And would
I 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 (15:01):
Stupid step step step step, stant step, stupid st
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