June 8, 2025 • 12 mins
When Michelle Thompson's chronic pain was repeatedly misclassified by her hospital's AI triage system, she began investigating how healthcare algorithms make life-or-death decisions. We examine how machine learning models trained primarily on male patients misdiagnose women's health issues, and explore the racial bias embedded in popular healthcare AI tools. Dr. Raj Patel shares his experience fighting against algorithmic recommendations in emergency medicine, while healthcare data scientist Dr. Emma Chen demonstrates how seemingly objective medical AI can perpetuate historical healthcare disparities.
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Transcript
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. Michelle Thompson's case really highlights the potential pitfalls
of AI triage. It makes you wonder how do these
systems actually decide who gets priority.
Speaker 3 (00:47):
Well, they're designed to assess patient urgency, using algorithms to
analyze symptoms, vital signs, and medical history to categorize and
prioritize patients.
Speaker 2 (00:57):
Sounds good in theory, but the Wikipedia article Clinical Decision
Support Systems CDSS points out how easily biased can creep in,
Like if the algorithms are mainly trained on data from
male patients, then women's health issues might get misdiagnosed or downplayed. Exactly,
and it's not just gender. Racial bias is a huge
concern too. The episode description mentions how seemingly objective medical
(01:21):
AI can perpetuate historical health care disparities.
Speaker 3 (01:24):
Right, Doctor Tren's work on that is crucial. It's like,
even if the AI itself isn't intentionally biased, the data
it learns from might reflect existing societal inequalities.
Speaker 2 (01:36):
So the AI just ends up amplifying those biases, making
things worse instead of better.
Speaker 3 (01:41):
Unfortunately, Yeah, it can lead to misdiagnosis, inadequate treatment, and
even life or death consequences.
Speaker 2 (01:48):
And the article mentions how machine learning models these black
boxes can be hard to interpret. Even clinicians struggle to
understand why an AI makes a certain recommendation.
Speaker 3 (01:58):
That lack of transparency is a major barrier to adoption.
As the article points out, clinicians need to trust the system,
and if they can't understand how it's arriving at its conclusions.
Speaker 2 (02:10):
They're less likely to use it, especially in emergency medicine,
where doctor Patel talks about actively fighting against some algorithmic recommendations.
Speaker 3 (02:18):
It really underscores the tension between relying on AI and
maintaining human oversight and critical medical decisions. We can't just
blindly follow the algorithm's advice.
Speaker 2 (02:29):
Absolutely, and then there's the whole issue of alert fatigue, right.
The article talks about how these systems can generate a
flood of warnings, some crucial, others less.
Speaker 3 (02:36):
So yeah, and that can lead to clinicians becoming desensitized
to the alerts, potentially missing something important. It's like the
Boy who Cried Wolf, but with potentially fatal consequences.
Speaker 2 (02:47):
The Wikipedia article highlights the challenges of integrating these systems
into existing workflows. It mentions how standalone CDSS applications disrupt
the clinicians flow.
Speaker 3 (02:57):
Right having to switch systems input data separately. It takes
time and breaks concentration. Seamless integration with electronic health records
is key, but as the article says, that's easier said
than done.
Speaker 2 (03:09):
It also brings up the challenges of keeping the knowledge
base up to date with the latest medical research. Thousands
of clinical trials are published every year.
Speaker 3 (03:17):
It's a massive undertaking to manually review and incorporate all
that information, and even then there can be conflicting findings
or interpretations to resolve.
Speaker 2 (03:28):
It really raises the question, how do we ensure these
AI tools are actually improving patient care, not hindering it.
Speaker 3 (03:35):
It's a complex problem, no easy answers, but addressing bias
and training data, improving transparency, and focusing on seamless EHR
integration are definitely important starting points, and perhaps most importantly,
recognizing that AI should be a tool to assist clinicians,
not replace their judgment. That human element is still essential.
Speaker 2 (04:00):
We're talking about how AI in healthcare, while promising, has
some serious pitfalls. This whole Michelle Thompson case. It's a
wake up call, right.
Speaker 3 (04:08):
Yeah, absolutely, it makes you question the entire premise of
AI triage. How can something designed to be objective end
up causing so much harm?
Speaker 2 (04:17):
Well, the problem isn't necessarily the intention behind the AI,
but the data it's trained on, right, Like, if the
data reflects existing biases, then the AI.
Speaker 3 (04:29):
Just learns to be biased too. It's like a student
mimicking a bad teacher exactly.
Speaker 2 (04:35):
And the Wikipedia article on clinical decision support systems it
really lays this out. It talks about how bias can
creep in, especially with things like gender.
Speaker 3 (04:43):
And race, and the implications are huge misdiagnosis, inadequate treatment. No,
I mean, we're talking about people's lives here.
Speaker 2 (04:50):
It's frightening. Frankly, the article mentions doctor Chen's work showing
how AI can perpetuate health care disparities. That's a big concern.
Speaker 3 (04:58):
It is we're trying to you create a more equitable system,
but if the AI is amplifying existing inequalities.
Speaker 2 (05:06):
M h, we're just making things worse. It's almost like
algorithmic gas lighting. You know, the AI tells your pain
isn't that bad, but you know it is.
Speaker 3 (05:18):
It's a real issue. And then there's the black box problem,
the lack of transparency. Even doctors can't always understand why
an AI makes a specific recommendation, which makes it.
Speaker 2 (05:29):
Hard to trust. As the article points out doctor Patel's
experience fighting against algorithmic recommendations, that really resonated with me.
Imagine being a doctor and having to actively disagree with
the AI.
Speaker 3 (05:40):
That takes courage. It speaks to the tension between relying
on AI and maintaining human oversight. We can't just blindly
trust the algorithm. There needs to be critical thinking involved.
Speaker 2 (05:51):
Yeah, and what about the sheer volume of alerts these
systems generate. Alert fatigue is a real danger. It's it's
almost information overload.
Speaker 3 (06:00):
It desensitizes you you start ignoring the alerts and then
you might miss something crucial. It's a delicate balance.
Speaker 2 (06:06):
I mean, integrating these systems into existing workflows is also
a challenge. As the Wikipedia article highlights standalone CDSS applications.
They disrupt the clinicians flow, don't.
Speaker 3 (06:15):
They absolutely constantly switching systems in putting data separately. It's
inefficient and increases the risk of errors. Seamless EHR integration
is the ideal, but that's proving to be difficult. And
then there's the problem of keeping the AI's knowledge base.
Current medical research is constantly evolving.
Speaker 2 (06:35):
Thousands of new studies every year. How do you keep
up with all that? It's a massive undertaking, and even
then there can be conflicting findings.
Speaker 3 (06:42):
Right exactly, It's a constant process of refinement and validation.
And I think that's the key takeaway here AI in healthcare.
It has enormous potential, but we need to be mindful
of its limitations. We need to address these biases, improve transparency,
and most importantly, remember that AI should be a tool
(07:02):
to assist clinicians, not replace them.
Speaker 2 (07:05):
So this article really dives into the dark side of algorithms. Huh,
it's kind of unsettling how much bias can creep into
these systems.
Speaker 3 (07:13):
It is, yeah, and it's everywhere, from search results to
well even facial recognition software. It's a bit unnerving.
Speaker 2 (07:19):
The part about Google's early policy on transparency and search
results that really struck me. They knew about this potential
for bias from the start.
Speaker 3 (07:26):
Right inherently bias towards advertisers, they said, that's pretty upfront.
Speaker 2 (07:30):
And then there's the stuff about voting behavior influencing elections
by twenty percent.
Speaker 3 (07:35):
That's not huge digital gerrymanderings. It train calls it chilling.
Speaker 2 (07:41):
The gender discrimination examples are pretty stark too, LinkedIn recommending
male variations of women's names.
Speaker 3 (07:48):
Really, it's like, come on, and target predicting pregnancies. That's
a whole other level discressive.
Speaker 2 (07:54):
The point about them having no legal obligation to protect
privacy because the data was predicted, that's a loophole that
needs closing, absolutely.
Speaker 3 (08:03):
And the racial bias, the nikon cameras asking Asian users
if they're blinking, it's just wow.
Speaker 2 (08:09):
It's like these systems are perpetuating stereotypes, you know. And
then there's the healthcare algorithm favoring white patients. How does
that happen.
Speaker 3 (08:16):
It's because the algorithm is focused on cost and existing
health care disparities mean black patients often incur lower costs
for the same conditions, so the algorithm sees them as
less at risk even when they're sicker. It's twisted.
Speaker 2 (08:31):
It's horrifying. The mortgage algorithms discriminating against minorities, the language
models with covert racism against AAE speakers, it's just one
thing after another.
Speaker 3 (08:40):
And the anti Semitic bias in major llms. It's like,
are we even making progress?
Speaker 2 (08:45):
The examples in law enforcement are especially troubling. Compass that
risk assessment tool inaccurate eighty percent of the time and
biased against black defendants.
Speaker 3 (08:56):
That's dot dot a recipe for injustice. It's literally impact
in people's lives, their freedom.
Speaker 2 (09:01):
The Facebook hate speech algorithm too, protecting broad categories but
allowing hate speech against black children. How does that even
make sense. It's because they're targeting subsets, not the entire group,
So all white men gets blocked, but black children doesn't.
It's a flawed logic, to say the least.
Speaker 3 (09:17):
It's messed up. And the surveillance software biased based on
the diversity of its training data. That's a huge problem.
Speaker 2 (09:24):
The Grinder example, linking it to apps for finding sex offenders.
That's just DoD stigmatizing.
Speaker 3 (09:30):
It's harmful. And Amazon delisting books with LGBTQ plus themes
come on.
Speaker 2 (09:36):
The facial recognition issues for transgender people, the AI potentially
outing people based on facial images. These are serious ethical concerns.
Speaker 3 (09:45):
They are. And then there's the whole issue of disability discrimination,
which is often overlooked in these discussions.
Speaker 2 (09:50):
The lack of data about disabilities make it hard to
address bias in these systems. It's a vicious cycle.
Speaker 3 (09:56):
Exactly, and it all comes back to the challenges of
defining and measure during fairness, the complexity of these systems,
the lack of transparency. It's a complex problem, no doubt,
but we have to find solutions. The technical approaches, the
transparency and monitoring, the right to remedy, diversity and inclusion.
Speaker 2 (10:15):
Efforts, these are all important steps.
Speaker 3 (10:17):
They are and regulation too, the JPR in Europe, the
new laws in New York City. We need more of that.
We need to hold these companies accountable because this isn't
just about technology, it's about people's lives.
Speaker 2 (10:29):
So this article it really not lays bare the pervasive
nature of algorithmical bias, doesn't it.
Speaker 3 (10:39):
Yeah, it's pretty unsettling. It's like it's everywhere everywhere you look.
Speaker 2 (10:44):
I mean, from search results to facial recognition to healthcare
it's dot. It's a lot.
Speaker 3 (10:52):
And the thing is it's not always intentional, right, Like
the article talks about how these biases can creep in unintentionally.
Speaker 2 (10:58):
Right, right, Like with the healthcare algorithm favoring white patients,
it wasn't designed to be racist, but.
Speaker 3 (11:05):
It ended up that way because of the data it
was trained on. It it's a complex issue.
Speaker 2 (11:10):
Yeah, it's like that example with the micon cameras asking
Asian users if they're blinking just not wow.
Speaker 3 (11:15):
It's perpetuating these harmful stereotypes. And the mortgage algorithm is
discriminating against minorities. That's that's a huge problem.
Speaker 2 (11:23):
Absolutely, And the language models with covert racism against a speakers.
I mean, how how do we even begin to address this?
Speaker 3 (11:31):
Well, the article mentions a few approaches, transparency, monitoring, the
right to remedy.
Speaker 2 (11:36):
DOT diversity and inclusion efforts, right right, But it's it's
a massive undertaking.
Speaker 3 (11:41):
It is, and it requires a a multifaceted approach, technical solutions, regulation,
and a real shift in how we think about these systems.
We can't just blindly trust them exactly. We need to
hold these companies accountable because ultimately, this isn't just about technology,
(12:01):
It's about people. The next time and 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 what 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.
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. Michelle Thompson's case really highlights the potential pitfalls
of AI triage. It makes you wonder how do these
systems actually decide who gets priority.
Speaker 3 (00:47):
Well, they're designed to assess patient urgency, using algorithms to
analyze symptoms, vital signs, and medical history to categorize and
prioritize patients.
Speaker 2 (00:57):
Sounds good in theory, but the Wikipedia article Clinical Decision
Support Systems CDSS points out how easily biased can creep in,
Like if the algorithms are mainly trained on data from
male patients, then women's health issues might get misdiagnosed or downplayed. Exactly,
and it's not just gender. Racial bias is a huge
concern too. The episode description mentions how seemingly objective medical
(01:21):
AI can perpetuate historical health care disparities.
Speaker 3 (01:24):
Right, Doctor Tren's work on that is crucial. It's like,
even if the AI itself isn't intentionally biased, the data
it learns from might reflect existing societal inequalities.
Speaker 2 (01:36):
So the AI just ends up amplifying those biases, making
things worse instead of better.
Speaker 3 (01:41):
Unfortunately, Yeah, it can lead to misdiagnosis, inadequate treatment, and
even life or death consequences.
Speaker 2 (01:48):
And the article mentions how machine learning models these black
boxes can be hard to interpret. Even clinicians struggle to
understand why an AI makes a certain recommendation.
Speaker 3 (01:58):
That lack of transparency is a major barrier to adoption.
As the article points out, clinicians need to trust the system,
and if they can't understand how it's arriving at its conclusions.
Speaker 2 (02:10):
They're less likely to use it, especially in emergency medicine,
where doctor Patel talks about actively fighting against some algorithmic recommendations.
Speaker 3 (02:18):
It really underscores the tension between relying on AI and
maintaining human oversight and critical medical decisions. We can't just
blindly follow the algorithm's advice.
Speaker 2 (02:29):
Absolutely, and then there's the whole issue of alert fatigue, right.
The article talks about how these systems can generate a
flood of warnings, some crucial, others less.
Speaker 3 (02:36):
So yeah, and that can lead to clinicians becoming desensitized
to the alerts, potentially missing something important. It's like the
Boy who Cried Wolf, but with potentially fatal consequences.
Speaker 2 (02:47):
The Wikipedia article highlights the challenges of integrating these systems
into existing workflows. It mentions how standalone CDSS applications disrupt
the clinicians flow.
Speaker 3 (02:57):
Right having to switch systems input data separately. It takes
time and breaks concentration. Seamless integration with electronic health records
is key, but as the article says, that's easier said
than done.
Speaker 2 (03:09):
It also brings up the challenges of keeping the knowledge
base up to date with the latest medical research. Thousands
of clinical trials are published every year.
Speaker 3 (03:17):
It's a massive undertaking to manually review and incorporate all
that information, and even then there can be conflicting findings
or interpretations to resolve.
Speaker 2 (03:28):
It really raises the question, how do we ensure these
AI tools are actually improving patient care, not hindering it.
Speaker 3 (03:35):
It's a complex problem, no easy answers, but addressing bias
and training data, improving transparency, and focusing on seamless EHR
integration are definitely important starting points, and perhaps most importantly,
recognizing that AI should be a tool to assist clinicians,
not replace their judgment. That human element is still essential.
Speaker 2 (04:00):
We're talking about how AI in healthcare, while promising, has
some serious pitfalls. This whole Michelle Thompson case. It's a
wake up call, right.
Speaker 3 (04:08):
Yeah, absolutely, it makes you question the entire premise of
AI triage. How can something designed to be objective end
up causing so much harm?
Speaker 2 (04:17):
Well, the problem isn't necessarily the intention behind the AI,
but the data it's trained on, right, Like, if the
data reflects existing biases, then the AI.
Speaker 3 (04:29):
Just learns to be biased too. It's like a student
mimicking a bad teacher exactly.
Speaker 2 (04:35):
And the Wikipedia article on clinical decision support systems it
really lays this out. It talks about how bias can
creep in, especially with things like gender.
Speaker 3 (04:43):
And race, and the implications are huge misdiagnosis, inadequate treatment. No,
I mean, we're talking about people's lives here.
Speaker 2 (04:50):
It's frightening. Frankly, the article mentions doctor Chen's work showing
how AI can perpetuate health care disparities. That's a big concern.
Speaker 3 (04:58):
It is we're trying to you create a more equitable system,
but if the AI is amplifying existing inequalities.
Speaker 2 (05:06):
M h, we're just making things worse. It's almost like
algorithmic gas lighting. You know, the AI tells your pain
isn't that bad, but you know it is.
Speaker 3 (05:18):
It's a real issue. And then there's the black box problem,
the lack of transparency. Even doctors can't always understand why
an AI makes a specific recommendation, which makes it.
Speaker 2 (05:29):
Hard to trust. As the article points out doctor Patel's
experience fighting against algorithmic recommendations, that really resonated with me.
Imagine being a doctor and having to actively disagree with
the AI.
Speaker 3 (05:40):
That takes courage. It speaks to the tension between relying
on AI and maintaining human oversight. We can't just blindly
trust the algorithm. There needs to be critical thinking involved.
Speaker 2 (05:51):
Yeah, and what about the sheer volume of alerts these
systems generate. Alert fatigue is a real danger. It's it's
almost information overload.
Speaker 3 (06:00):
It desensitizes you you start ignoring the alerts and then
you might miss something crucial. It's a delicate balance.
Speaker 2 (06:06):
I mean, integrating these systems into existing workflows is also
a challenge. As the Wikipedia article highlights standalone CDSS applications.
They disrupt the clinicians flow, don't.
Speaker 3 (06:15):
They absolutely constantly switching systems in putting data separately. It's
inefficient and increases the risk of errors. Seamless EHR integration
is the ideal, but that's proving to be difficult. And
then there's the problem of keeping the AI's knowledge base.
Current medical research is constantly evolving.
Speaker 2 (06:35):
Thousands of new studies every year. How do you keep
up with all that? It's a massive undertaking, and even
then there can be conflicting findings.
Speaker 3 (06:42):
Right exactly, It's a constant process of refinement and validation.
And I think that's the key takeaway here AI in healthcare.
It has enormous potential, but we need to be mindful
of its limitations. We need to address these biases, improve transparency,
and most importantly, remember that AI should be a tool
(07:02):
to assist clinicians, not replace them.
Speaker 2 (07:05):
So this article really dives into the dark side of algorithms. Huh,
it's kind of unsettling how much bias can creep into
these systems.
Speaker 3 (07:13):
It is, yeah, and it's everywhere, from search results to
well even facial recognition software. It's a bit unnerving.
Speaker 2 (07:19):
The part about Google's early policy on transparency and search
results that really struck me. They knew about this potential
for bias from the start.
Speaker 3 (07:26):
Right inherently bias towards advertisers, they said, that's pretty upfront.
Speaker 2 (07:30):
And then there's the stuff about voting behavior influencing elections
by twenty percent.
Speaker 3 (07:35):
That's not huge digital gerrymanderings. It train calls it chilling.
Speaker 2 (07:41):
The gender discrimination examples are pretty stark too, LinkedIn recommending
male variations of women's names.
Speaker 3 (07:48):
Really, it's like, come on, and target predicting pregnancies. That's
a whole other level discressive.
Speaker 2 (07:54):
The point about them having no legal obligation to protect
privacy because the data was predicted, that's a loophole that
needs closing, absolutely.
Speaker 3 (08:03):
And the racial bias, the nikon cameras asking Asian users
if they're blinking, it's just wow.
Speaker 2 (08:09):
It's like these systems are perpetuating stereotypes, you know. And
then there's the healthcare algorithm favoring white patients. How does
that happen.
Speaker 3 (08:16):
It's because the algorithm is focused on cost and existing
health care disparities mean black patients often incur lower costs
for the same conditions, so the algorithm sees them as
less at risk even when they're sicker. It's twisted.
Speaker 2 (08:31):
It's horrifying. The mortgage algorithms discriminating against minorities, the language
models with covert racism against AAE speakers, it's just one
thing after another.
Speaker 3 (08:40):
And the anti Semitic bias in major llms. It's like,
are we even making progress?
Speaker 2 (08:45):
The examples in law enforcement are especially troubling. Compass that
risk assessment tool inaccurate eighty percent of the time and
biased against black defendants.
Speaker 3 (08:56):
That's dot dot a recipe for injustice. It's literally impact
in people's lives, their freedom.
Speaker 2 (09:01):
The Facebook hate speech algorithm too, protecting broad categories but
allowing hate speech against black children. How does that even
make sense. It's because they're targeting subsets, not the entire group,
So all white men gets blocked, but black children doesn't.
It's a flawed logic, to say the least.
Speaker 3 (09:17):
It's messed up. And the surveillance software biased based on
the diversity of its training data. That's a huge problem.
Speaker 2 (09:24):
The Grinder example, linking it to apps for finding sex offenders.
That's just DoD stigmatizing.
Speaker 3 (09:30):
It's harmful. And Amazon delisting books with LGBTQ plus themes
come on.
Speaker 2 (09:36):
The facial recognition issues for transgender people, the AI potentially
outing people based on facial images. These are serious ethical concerns.
Speaker 3 (09:45):
They are. And then there's the whole issue of disability discrimination,
which is often overlooked in these discussions.
Speaker 2 (09:50):
The lack of data about disabilities make it hard to
address bias in these systems. It's a vicious cycle.
Speaker 3 (09:56):
Exactly, and it all comes back to the challenges of
defining and measure during fairness, the complexity of these systems,
the lack of transparency. It's a complex problem, no doubt,
but we have to find solutions. The technical approaches, the
transparency and monitoring, the right to remedy, diversity and inclusion.
Speaker 2 (10:15):
Efforts, these are all important steps.
Speaker 3 (10:17):
They are and regulation too, the JPR in Europe, the
new laws in New York City. We need more of that.
We need to hold these companies accountable because this isn't
just about technology, it's about people's lives.
Speaker 2 (10:29):
So this article it really not lays bare the pervasive
nature of algorithmical bias, doesn't it.
Speaker 3 (10:39):
Yeah, it's pretty unsettling. It's like it's everywhere everywhere you look.
Speaker 2 (10:44):
I mean, from search results to facial recognition to healthcare
it's dot. It's a lot.
Speaker 3 (10:52):
And the thing is it's not always intentional, right, Like
the article talks about how these biases can creep in unintentionally.
Speaker 2 (10:58):
Right, right, Like with the healthcare algorithm favoring white patients,
it wasn't designed to be racist, but.
Speaker 3 (11:05):
It ended up that way because of the data it
was trained on. It it's a complex issue.
Speaker 2 (11:10):
Yeah, it's like that example with the micon cameras asking
Asian users if they're blinking just not wow.
Speaker 3 (11:15):
It's perpetuating these harmful stereotypes. And the mortgage algorithm is
discriminating against minorities. That's that's a huge problem.
Speaker 2 (11:23):
Absolutely, And the language models with covert racism against a speakers.
I mean, how how do we even begin to address this?
Speaker 3 (11:31):
Well, the article mentions a few approaches, transparency, monitoring, the
right to remedy.
Speaker 2 (11:36):
DOT diversity and inclusion efforts, right right, But it's it's
a massive undertaking.
Speaker 3 (11:41):
It is, and it requires a a multifaceted approach, technical solutions, regulation,
and a real shift in how we think about these systems.
We can't just blindly trust them exactly. We need to
hold these companies accountable because ultimately, this isn't just about technology,
(12:01):
It's about people. The next time and 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 what 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.
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