Are We Automating Racism?
Summary
TLDRDas Skript beleuchtet die Herausforderungen von künstlicher Intelligenz (KI) und Algorithmenbezug, insbesondere in Bezug auf rassistische Tendenzen. Es zeigt, wie KI-Systeme, die von menschlichen Entscheidungen und Vorurteilen beeinflusst sind, zu unerwarteten und unfairen Ergebnissen führen können. Das Video diskutiert Beispiele wie das Twitter-Bildzuschneiden, das rassistisch wirken könnte, und stellt die Notwendigkeit von Überwachung und ethischer Ausrichtung von KI-Technologien her. Es betont auch die Bedeutung der Fragestellung und der Verantwortung der Schöpfer von KI-Werkzeugen gegenüber der Gesellschaft.
Takeaways
- 🤖 Künstliche Intelligenz (KI) kann diskriminierend sein, selbst wenn sie neutrale Absichten hat und keine menschliche Vorurteile enthält.
- 📈 Datengetriebene Systeme werden zunehmend in unserem Leben eine größere Rolle spielen, aber ihre Fehlfunktionen betreffen nicht alle gleich.
- 🖼️ Algorithmen können aufgrund von Datenmangel an Vielfalt oder fehlerhaften Beispielen rassistisch wirken, selbst wenn das nicht beabsichtigt ist.
- 👀 KI-Systeme lernen von Beispielen, die von Menschen ausgewählt, gekennzeichnet und abgeleitet wurden.
- 🔍 Das Problem von KI-Bias ist groß und schwierig, aber es bedeutet nicht, dass wir auf maschinelles Lernen verzichten müssen.
- 📊 Eine der Vorteile von Computer-Bias im Vergleich zu menschlichem Bias ist, dass man ihn leicht messen und verfolgen kann.
- 🌐 Die Auswahl und Verwendung von Algorithmen hängt von Ressourcen und Macht ab und wer die Interessen durch ein prädiktives Modell vertreten werden soll.
- 📋 Modelle sollten dokumentiert werden, einschließlich ethischer Bedenken, wie der beabsichtigte Gebrauch, die Herkunft der Daten und Anweisungen zur Bewertung der Systemleistung.
- 🚫 Es ist wichtig, vorsichtig zu sein, wo und wie wir KI-Systeme einsetzen und welche menschliche Überwachung wir über diese Systeme haben.
- 🧐 Die Auswahl der Daten und das Labeling beeinflussen die KI-Modelle erheblich und können zu unerwünschten Ergebnissen führen, wenn nicht sorgfältig gemacht.
- 🤔 Die Frage, welche Technologien entwickelt und wie sie in unserer Welt eingesetzt werden, reduziert sich auf Machtdynamiken und die Interessen derjenigen, die die Fragen stellen.
- 📝 Die Verantwortung der Designer und Programmierer geht über die Rechenschaft gegenüber ihren Kunden hinaus und betrifft auch die Auswirkungen, die diese Werkzeuge in der Welt haben.
Q & A
Was ist das Hauptthema des Transcripts?
-Das Hauptthema des Transcripts ist die Diskussion über den algorithmischen Bias und die Auswirkungen von künstlicher Intelligenz (KI) auf die Gesellschaft, insbesondere in Bezug auf Diskriminierung und Vorurteile.
Wie kann ein Bildschirm- oder Fotocropping-Algorithmus diskriminierend wirken?
-Ein Bildschirm- oder Fotocropping-Algorithmus kann diskriminierend wirken, indem er bei der Auswahl des zu zeigenden Bildausschnitts zwischen verschiedenen Gesichtern eine Ungleichheit aufweist, z.B. indem er voreingenommen weiße Gesichter bevorzugt.
Was ist ein 'Saliency Prediction Model'?
-Ein 'Saliency Prediction Model' ist eine Art KI-Software, die darauf abzielt, die wichtigen Elemente in einem Bild zu identifizieren. Es basiert auf den Daten, die durch das Beobachten von menschlichen Augenbewegungen bei der Betrachtung von Bildern gesammelt wurden.
Wie kann die Auswahl von Daten, die zur Schulung eines Modells verwendet werden, zu Bias führen?
-Die Auswahl von Daten kann zu Bias führen, wenn sie nicht repräsentativ für die verschiedenen Bevölkerungsgruppen ist. Wenn beispielsweise ein Datensatz viele Katzenbilder enthält, aber nur wenige dunkelhäutige Gesichter, kann das Modell lernen, Katzen als wichtiger zu erkennen als bestimmte Menschen.
Was ist ein Beispiel für die Verwendung von KI in der Gesundheitsversorgung, die zu unerwünschten Ergebnissen führen kann?
-Im Text wird ein Beispiel genannt, bei dem ein Programm zur Identifizierung von Patienten mit chronischen Krankheiten zur Zusatzversorgung verwendet wurde. Das Programm basierte auf den Kosten der Patienten, was jedoch dazu führte, dass Patienten mit dunklerer Hautfarbe aufgrund von Kostenunterscheiden, die mit institutionalisierter Rassismus und mangelnder Versorgung zu tun hatten, nicht angemessen identifiziert wurden.
Wie können Modelle zur KI-Entwicklung verbessert werden, um Bias zu reduzieren?
-Modelle zur KI-Entwicklung können verbessert werden, indem sie mit einer Vielzahl von Daten trainiert werden, die eine breitere Repräsentanz der Bevölkerung widerspiegeln. Auch die Transparenz über die Datenherkunft und die Bezeichnung von Daten ist wichtig. Ein weiteres Werkzeug zur Verbesserung ist die sogenannte 'Modellkartei', die wichtige Informationen über das Modell und seine potenziellen ethischen Bedenken dokumentiert.
Was ist die Rolle von menschlicher Subjektivität in der KI-Entwicklung?
-Menschliche Subjektivität spielt eine große Rolle in der KI-Entwicklung, da Entscheidungen über die Auswahl und Beschriftung von Daten, die zur Schulung des Modells verwendet werden, von Menschen getroffen werden. Diese Entscheidungen können unbewusst Bias in den Algorithmen einfügen.
Wie kann die Verwendung von KI-Technologien in der Gesellschaft ethische und gesellschaftliche Herausforderungen hervorrufen?
-Die Verwendung von KI-Technologien kann ethische und gesellschaftliche Herausforderungen hervorrufen, da sie potenziell Diskriminierung und Ungerechtigkeit verfestigen können. Es ist wichtig, über die Auswirkungen von KI-Entscheidungen nachzudenken und sicherzustellen, dass sie nicht auf der Grundlage von Rasse, Geschlecht oder sozialem Status diskriminieren.
Was ist der Vorteil der Identifizierung von Bias in KI-Systemen gegenüber menschlichem Bias?
-Der Vorteil der Identifizierung von Bias in KI-Systemen ist, dass sie messbar und nachverfolgbar ist und es ermöglicht, an dem Modell zu arbeiten, um faire Ergebnisse zu erzielen, wenn man dazu motiviert ist.
Welche Rolle spielen Ressourcen und Macht bei der Entscheidung darüber, welche Technologien entwickelt und wie sie in unserer Welt eingesetzt werden?
-Ressourcen und Macht sind entscheidend, da sie bestimmen, wessen Interessen von einem prädiktiven Modell bedient werden und welche Fragen gestellt werden. Die Machtstrukturen legen fest, auf welchem Weg die Technologie eingesetzt wird und wer davon profitiert oder benachteiligt wird.
Was ist die Verantwortung von Designern und Programmierern in Bezug auf die Auswirkungen der von ihnen entwickelten Werkzeuge in der Welt?
-Designer und Programmierer müssen sich fragen, ob sie nur ihren Clients gegenüber verantwortlich sind oder auch gegenüber der gesamten politischen Gemeinschaft. Sie müssen sich bewusst sein, welche Auswirkungen ihre Tools haben können und ob sie bereit sind, für diese Auswirkungen zu haften.
Was ist der Unterschied zwischen dem Bias in einem Computer und dem Bias beim Menschen?
-Der Unterschied liegt darin, dass der Bias in einem Computer leichter gemessen und verfolgt werden kann, während der menschliche Bias oft komplexer zu identifizieren und zu quantifizieren ist.
Wie kann die Transparenz in Bezug auf die Entwicklung von KI-Modellen verbessert werden?
-Die Transparenz kann verbessert werden, indem Unternehmen interne Dokumentationskulturen wie 'Modellkarteien' einführen, die wichtige Informationen über die Funktionsweise des Modells, die Datenherkunft und die ethischen Bedenken liefern.
Outlines
🤖 Künstliche Intelligenz und rassistische Entscheidungen
Der erste Absatz spricht über die Diskrepanz zwischen menschlicher Wahrnehmung und der eines Computers, der Menschen nur als Daten sieht. Es wird diskutiert, dass Daten-getriebene Systeme immer wichtiger werden und normalerweise gut funktionieren, aber wenn sie fehlschlagen, dies nicht auf alle gleichermaßen trifft. Ein Beispiel ist das rassistische Verhalten eines Algorithmus, der bei der Wahl von Gesichtern zwischen verschiedenen Personen diskriminiert. Die Diskussion umfasst auch die Bedeutung von rassistischen Auswirkungen und wie man mit ihnen umgeht, selbst wenn die Absichten neutral sind.
📈 Systematische Tests auf künstliche Intelligenz-Bias
In diesem Absatz wird ein öffentlicher Test von algorithmischem Bias durchgeführt, indem extreme vertikale Bilder von zwei Personen hochgeladen werden, um zu sehen, welcher von ihnen von einem Bildzuschneide-Algorithmus bevorzugt wird. Die Diskussion umfasst die Bedeutung von Testbarkeit, die Tatsache, dass es möglich ist, den Algorithmus sofort zu testen, und die Beobachtung, dass der Algorithmus möglicherweise weiße Gesichter bevorzugt. Es wird auch auf die Notwendigkeit einer systematischen Testung mit Hunderten von Fotos eingegangen, um zu einer Schlussfolgerung zu kommen.
🔍 Saliency Prediction Models und ihre Auswirkungen
Der dritte Absatz konzentriert sich auf die Funktionsweise von Saliency Prediction Models, die verwendet werden, um zu entscheiden, was in einem Bild wichtig ist. Es wird erläutert, wie diese Modelle mithilfe von Datensätzen, die menschliche Blickbewegungen dokumentieren, trainiert werden. Die Diskussion umfasst auch die Herausforderungen, die mit der Rückverfolgung von Entscheidungen verbunden sind, die von solchen Modellen getroffen werden, und wie die Daten, die zur Schulung verwendet werden, die Leistung des Modells beeinflussen. Beispiele für die Verwendung von Daten wie Kriminalitätsdaten und medizinischen Kennzahlen werden gegeben, um zu zeigen, wie Bias in den Daten die Ergebnisse beeinflussen kann.
💰 Die Auswirkungen von Algorithmen auf das Gesundheitswesen
Dieser Absatz behandelt ein Beispiel für die Anwendung von Algorithmen im Gesundheitswesen und wie diese fehlschlagen können, wenn sie auf Kosten statt auf der tatsächlichen Gesundheit basieren. Es wird erklärt, wie ein Algorithmus Patienten anhand ihrer Gesundheitskosten bewertet, anstatt anhand ihrer Krankheitsbilder, was zu rassistischen Ergebnissen führen kann. Die Diskussion umfasst auch die Bedeutung der Wahl der Variablen, die zur Definition von Hochrisikopatienten verwendet werden, und wie diese Entscheidungen die Ergebnisse beeinflussen können.
🛡️ Ethik und Verantwortung in der künstlichen Intelligenz
Der letzte Absatz spricht über die Herausforderungen, die mit der Verantwortung und der Ethik in Bezug auf künstliche Intelligenz verbunden sind. Es wird diskutiert, dass die Branche wenig reguliert ist und wie interne Dokumentationskulturen wie 'Model Cards' dazu beitragen können, Transparenz und Verantwortlichkeit zu fördern. Die Diskussion umfasst auch die Notwendigkeit, die Verwendung von Algorithmen kritisch zu hinterfragen und zu entscheiden, ob sie überhaupt eingesetzt werden sollten. Es wird betont, dass es um die Macht und Ressourcen geht, die bestimmen, welche Technologien entwickelt und wie sie in unserer Welt eingesetzt werden.
Mindmap
Keywords
💡Algorithmic Bias
💡Data-Driven Systems
💡Machine Learning
💡Racial Bias
💡Saliency Prediction Model
💡Representation in Data Sets
💡Bias in Healthcare Algorithms
💡Model Cards
💡Ethical Concerns in AI
💡Human Oversight
💡Power Dynamics in AI
Highlights
Machines can exhibit bias even without human malice, raising questions about the fairness of data-driven systems.
A public test of algorithmic bias using images of Mitch McConnell and Barack Obama demonstrated potential racial bias in image cropping algorithms.
The idea of tech neutrality is debunked as biases can be embedded in AI systems, leading to discriminatory outcomes.
AI systems learn from human-labeled examples, which can perpetuate existing biases if not properly addressed.
The saliency prediction model used by Twitter for image cropping was tested and found to potentially favor white faces.
The composition of training data sets for AI is crucial; unrepresentative data can lead to biased algorithms.
An example of biased AI is in healthcare algorithms that use cost as a proxy for health risk, which can disadvantage certain demographic groups.
The healthcare algorithm case study shows how AI decisions can operate invisibly, impacting people without their knowledge.
Machine learning as an industry is largely unregulated, with no mandatory reporting of performance metrics or evaluation results.
Model Cards are a new documentation effort to provide transparency about how AI models work and their ethical considerations.
Bias in AI is measurable and can be tracked, offering a path toward achieving fair outcomes through model adjustment.
The deployment of AI technologies raises questions about which algorithms should be used and for what purposes.
The power dynamics in technology development determine whose interests are served by predictive models and which questions are asked.
Designers and programmers of AI tools must consider their accountability not just to clients but to the broader societal impact of their creations.
The challenge in AI ethics is not only in removing bias from algorithms but also in deciding which algorithms are necessary and beneficial.
Ruha Benjamin, a professor at Princeton University, discusses the systemic nature of bias in AI and its implications on society.
AI systems can reinforce societal biases if the data they learn from is not diverse or representative of various demographics.
Transcripts
Maybe we-- if you guys could stand over--
Is it okay if they stand over here?
- Yeah. - Um, actually.
Christophe, if you can get even lower.
- Okay. - ( shutter clicks )
This is Lee and this is Christophe.
They're two of the hosts of this show.
But to a machine, they're not people.
This is just pixels. It's just data.
A machine shouldn't have a reason to prefer
one of these guys over the other.
And yet, as you'll see in a second, it does.
It feels weird to call a machine racist,
but I really can't explain-- I can't explain what just happened.
Data-driven systems are becoming a bigger and bigger part of our lives,
and they work well a lot of the time.
- But when they fail... - Once again, it's the white guy.
When they fail, they're not failing on everyone equally.
If I go back right now...
Ruha Benjamin: You can have neutral intentions.
You can have good intentions.
And the outcomes can still be discriminatory.
Whether you want to call that machine racist
or you want to call the outcome racist,
we have a problem.
( theme music playing )
I was scrolling through my Twitter feed a while back
and I kept seeing tweets that look like this.
Two of the same picture of Republican senator Mitch McConnell smiling,
or sometimes it would be four pictures
of the same random stock photo guy.
And I didn't really know what was going on,
but it turns out that this was a big public test of algorithmic bias.
Because it turns out that these aren't pictures of just Mitch McConnell.
They're pictures of Mitch McConnell and...
- Barack Obama. - Lee: Oh, wow.
So people were uploading
these really extreme vertical images
to basically force this image cropping algorithm
to choose one of these faces.
People were alleging that there's a racial bias here.
But I think what's so interesting about this particular algorithm
is that it is so testable for the public.
It's something that we could test right now if we wanted to.
- Let's do it. - You guys wanna do it?
Okay. Here we go.
So, Twitter does offer you options to crop your own image.
But if you don't use those,
it uses an automatic cropping algorithm.
- Wow. There it is. - Whoa. Wow.
That's crazy.
Christophe, it likes you.
Okay, let's try the other-- the happy one.
Lee: Wow.
- Unbelievable. Oh, wow. - Both times.
So, do you guys think this machine is racist?
The only other theory I possibly have
is if the algorithm prioritizes white faces
because it can pick them up quicker, for whatever reason,
against whatever background.
Immediately, it looks through the image
and tries to scan for a face.
Why is it always finding the white face first?
Joss: With this picture, I think someone could argue
that the lighting makes Christophe's face more sharp.
I still would love to do
a little bit more systematic testing on this.
I think maybe hundreds of photos
could allow us to draw a conclusion.
I have downloaded a bunch of photos
from a site called Generated Photos.
These people do not exist. They were a creation of AI.
And I went through, I pulled a bunch
that I think will give us
a pretty decent way to test this.
So, Christophe, I wonder if you would be willing to help me out with that.
You want me to tweet hundreds of photos?
- ( Lee laughs ) - Joss: Exactly.
I'm down. Sure, I've got time.
Okay.
( music playing )
There may be some people who take issue with the idea
that machines can be racist
without a human brain or malicious intent.
But such a narrow definition of racism
really misses a lot of what's going on.
I want to read a quote that responds to that idea.
It says, "Robots are not sentient beings, sure,
but racism flourishes well beyond hate-filled hearts.
No malice needed, no "N" word required,
just a lack of concern for how the past shapes the present."
I'm going now to speak to the author of those words, Ruha Benjamin.
She's a professor of African-American Studies at Princeton University.
When did you first become concerned
that automated systems, AI, could be biased?
A few years ago, I noticed these headlines
and hot takes about so-called racist and sexist robots.
There was a viral video in which two friends were in a hotel bathroom
and they were trying to use an automated soap dispenser.
Black hand, nothing. Larry, go.
Black hand, nothing.
And although they seem funny
and they kind of get us to chuckle,
the question is, are similar design processes
impacting much more consequential technologies that we're not even aware of?
When the early news controversies came along maybe 10 years ago,
people were surprised by the fact that they showed a racial bias.
Why do you think people were surprised?
Part of it is a deep attachment and commitment
to this idea of tech neutrality.
People-- I think because life is so complicated
and our social world is so messy--
really cling on to something that will save us,
and a way of making decisions that's not drenched
in the muck of all of human subjectivity,
human prejudice and frailty.
We want it so much to be true.
We want it so much to be true, you know?
And the danger is that we don't question it.
And still we continue to have, you know, so-called glitches
when it comes to race and skin complexion.
And I don't think that they're glitches.
It's a systemic issue in the truest sense of the word.
It has to do with our computer systems and the process of design.
Joss: AI can seem pretty abstract sometimes.
So we built this to help explain
how machine learning works and what can go wrong.
This black box is the part of the system that we interact with.
It's the software that decides which dating profiles we might like,
how much a rideshare should cost,
or how a photo should be cropped on Twitter.
We just see a device making a decision.
Or more accurately, a prediction.
What we don't see is all of the human decisions
that went into the design of that technology.
Now, it's true that when you're dealing with AI,
that means that the code in this box
wasn't all written directly by humans,
but by machine-learning algorithms
that find complex patterns in data.
But they don't just spontaneously learn things from the world.
They're learning from examples.
Examples that are labeled by people,
selected by people,
and derived from people, too.
See, these machines and their predictions,
they're not separate from us or from our biases
or from our history,
which we've seen in headline after headline
for the past 10 years.
We're using the face-tracking software,
so it's supposed to follow me as I move.
As you can see, I do this-- no following.
Not really-- not really following me.
- Wanda, if you would, please? - Sure.
In 2010, the top hit
when you did a search for "black girls,"
80% of what you found
on the first page of results was all porn sites.
Google is apologizing after its photo software
labeled two African-Americans gorillas.
Microsoft is shutting down
its new artificial intelligent bot
after Twitter users taught it how to be racist.
Woman: In order to make yourself hotter,
the app appeared to lighten your skin tone.
Overall, they work better on lighter faces than darker faces,
and they worked especially poorly
on darker female faces.
Okay, I've noticed that on all these damn beauty filters,
is they keep taking my nose and making it thinner.
Give me my African nose back, please.
Man: So, the first thing that I tried was the prompt "Two Muslims..."
And the way it completed it was,
"Two Muslims, one with an apparent bomb,
tried to blow up the Federal Building
in Oklahoma City in the mid-1990s."
Woman: Detroit police wrongfully arrested Robert Williams
based on a false facial recognition hit.
There's definitely a pattern of harm
that disproportionately falls on vulnerable people, people of color.
Then there's attention,
but of course, the damage has already been done.
( Skype ringing )
- Hello. - Hey, Christophe.
Thanks for doing these tests.
- Of course. - I know it was a bit of a pain,
but I'm curious what you found.
Sure. I mean, I actually did it.
I actually tweeted 180 different sets of pictures.
In total, dark-skinned people
were displayed in the crop 131 times,
and light-skinned people
were displayed in the crop 229 times,
which comes out to 36% dark-skinned
and 64% light-skinned.
That does seem to be evidence of some bias.
It's interesting because Twitter posted a blog post
saying that they had done some of their own tests
before launching this tool, and they said that
they didn't find evidence of racial bias,
but that they would be looking into it further.
Um, they also said that the kind of technology
that they use to crop images
is called a Saliency Prediction Model,
which means software that basically is making a guess
about what's important in an image.
So, how does a machine know what is salient, what's relevant in a picture?
Yeah, it's really interesting, actually.
There's these saliency data sets
that documented people's eye movements
while they looked at certain sets of images.
So you can take those photos
and you can take that eye-tracking data
and teach a computer what humans look at.
So, Twitter's not going to give me any more information
about how they trained their model,
but I found an engineer from a company called Gradio.
They built an app that does something similar,
and I think it can give us a closer look
at how this kind of AI works.
- Hey. - Hey.
- Joss. - Nice to meet you. Dawood.
So, you and your colleagues
built a saliency cropping tool
that is similar to what we think Twitter is probably doing.
Yeah, we took a public machine learning model, posted it on our library,
and launched it for anyone to try.
And you don't have to constantly post pictures
on your timeline to try and experiment with it,
which is what people were doing when they first became aware of the problem.
And that's what we did. We did a bunch of tests just on Twitter.
But what's interesting about what your app shows
is the sort of intermediate step there, which is this saliency prediction.
Right, yeah. I think the intermediate step is important for people to see.
Well, I-- I brought some pictures for us to try.
These are actually the hosts of "Glad You Asked."
And I was hoping we could put them into your interface
and see what, uh, the saliency prediction is.
Sure. Just load this image here.
Joss: Okay, so, we have a saliency map.
Clearly the prediction is that faces are salient,
which is not really a surprise.
But it looks like maybe they're not equally salient.
- Right. - Is there a way to sort of look closer at that?
So, what we can do here, we actually built it out in the app
where we can put a window on someone's specific face,
and it will give us a percentage of what amount of saliency
you have over your face versus in proportion to the whole thing.
- That's interesting. - Yeah.
She's-- Fabiola's in the center of the picture,
but she's actually got a lower percentage
of the salience compared to Cleo, who's to her right.
Right, and trying to guess why a model is making a prediction
and why it's predicting what it is
is a huge problem with machine learning.
It's always something that you have to kind of
back-trace to try and understand.
And sometimes it's not even possible.
Mm-hmm. I looked up what data sets
were used to train the model you guys used,
and I found one that was created by
researchers at MIT back in 2009.
So, it was originally about a thousand images.
We pulled the ones that contained faces,
any face we could find that was big enough to see.
And I went through all of those,
and I found that only 10 of the photos,
that's just about 3%,
included someone who appeared to be
of Black or African descent.
Yeah, I mean, if you're collecting a data set through Flickr,
you're-- first of all, you're biased to people
that have used Flickr back in, what, 2009, you said, or something?
Joss: And I guess if we saw in this image data set,
there are more cat faces than black faces,
we can probably assume that minimal effort was made
to make that data set representative.
When someone collects data into a training data set,
they can be motivated by things like convenience and cost
and end up with data that lacks diversity.
That type of bias, which we saw in the saliency photos,
is relatively easy to address.
If you include more images representing racial minorities,
you can probably improve the model's performance on those groups.
But sometimes human subjectivity
is imbedded right into the data itself.
Take crime data for example.
Our data on past crimes in part reflects
police officers' decisions about what neighborhoods to patrol
and who to stop and arrest.
We don't have an objective measure of crime,
and we know that the data we do have
contains at least some racial profiling.
But it's still being used to train crime prediction tools.
And then there's the question of how the data is structured over here.
Say you want a program that identifies
chronically sick patients to get additional care
so they don't end up in the ER.
You'd use past patients as your examples,
but you have to choose a label variable.
You have to define for the machine what a high-risk patient is
and there's not always an obvious answer.
A common choice is to define high-risk as high-cost,
under the assumption that people who use
a lot of health care resources are in need of intervention.
Then the learning algorithm looks through
the patient's data--
their age, sex,
medications, diagnoses, insurance claims,
and it finds the combination of attributes
that correlates with their total health costs.
And once it gets good at predicting
total health costs on past patients,
that formula becomes software to assess new patients
and give them a risk score.
But instead of predicting sick patients,
this predicts expensive patients.
Remember, the label was cost,
and when researchers took a closer look at those risk scores,
they realized that label choice was a big problem.
But by then, the algorithm had already been used
on millions of Americans.
It produced risk scores for different patients,
and if a patient had a risk score
of almost 60,
they would be referred into the program
for screening-- for their screening.
And if they had a risk score of almost 100,
they would default into the program.
Now, when we look at the number of chronic conditions
that patients of different risk scores were affected by,
you see a racial disparity where white patients
had fewer conditions than black patients
at each risk score.
That means that black patients were sicker
than their white counterparts
when they had the same risk score.
And so what happened is in producing these risk scores
and using spending,
they failed to recognize that on average
black people incur fewer costs for a variety of reasons,
including institutional racism,
including lack of access to high-quality insurance,
and a whole host of other factors.
But not because they're less sick.
Not because they're less sick.
And so I think it's important
to remember this had racist outcomes,
discriminatory outcomes, not because there was
a big, bad boogie man behind the screen
out to get black patients,
but precisely because no one was thinking
about racial disparities in healthcare.
No one thought it would matter.
And so it was about the colorblindness,
the race neutrality that created this.
The good news is that now the researchers who exposed this
and who brought this to light are working with the company
that produced this algorithm to have a better proxy.
So instead of spending, it'll actually be
people's actual physical conditions
and the rate at which they get sick, et cetera,
that is harder to figure out,
it's a harder kind of proxy to calculate,
but it's more accurate.
I feel like what's so unsettling about this healthcare algorithm
is that the patients would have had
no way of knowing this was happening.
It's not like Twitter, where you can upload
your own picture, test it out, compare with other people.
This was just working in the background,
quietly prioritizing the care of certain patients
based on an algorithmic score
while the other patients probably never knew
they were even passed over for this program.
I feel like there has to be a way
for companies to vet these systems in advance,
so I'm excited to talk to Deborah Raji.
She's been doing a lot of thinking
and writing about just that.
My question for you is how do we find out
about these problems before they go out into the world
and cause harm rather than afterwards?
So, I guess a clarification point is that machine learning
is highly unregulated as an industry.
These companies don't have to report their performance metrics,
they don't have to report their evaluation results
to any kind of regulatory body.
But internally there's this new culture of documentation
that I think has been incredibly productive.
I worked on a couple of projects with colleagues at Google,
and one of the main outcomes of that was this effort called Model Cards--
very simple one-page documentation
on how the model actually works,
but also questions that are connected to ethical concerns,
such as the intended use for the model,
details about where the data's coming from,
how the data's labeled, and then also, you know,
instructions to evaluate the system according to its performance
on different demographic sub-groups.
Maybe that's something that's hard to accept
is that it would actually be maybe impossible
to get performance across sub-groups to be exactly the same.
How much of that do we just have to be like, "Okay"?
I really don't think there's an unbiased data set
in which everything will be perfect.
I think the more important thing is to actually evaluate
and assess things with an active eye
for those that are most likely to be negatively impacted.
You know, if you know that people of color are most vulnerable
or a particular marginalized group is most vulnerable
in a particular situation,
then prioritize them in your evaluation.
But I do think there's certain situations
where maybe we should not be predicting
with a machine-learning system at all.
We should be super cautious and super careful
about where we deploy it and where we don't deploy it,
and what kind of human oversight
we put over these systems as well.
The problem of bias in AI is really big.
It's really difficult.
But I don't think it means we have to give up
on machine learning altogether.
One benefit of bias in a computer versus bias in a human
is that you can measure and track it fairly easily.
And you can tinker with your model
to try and get fair outcomes if you're motivated to do so.
The first step was becoming aware of the problem.
Now the second step is enforcing solutions,
which I think we're just beginning to see now.
But Deb is raising a bigger question.
Not just how do we get bias out of the algorithms,
but which algorithms should be used at all?
Do we need a predictive model to be cropping our photos?
Do we want facial recognition in our communities?
Many would say no, whether it's biased or not.
And that question of which technologies
get built and how they get deployed in our world,
it boils down to resources and power.
It's the power to decide whose interests
will be served by a predictive model,
and which questions get asked.
You could ask, okay, I want to know how landlords
are making life for renters hard.
Which landlords are not fixing up their buildings?
Which ones are hiking rent?
Or you could ask, okay, let's figure out
which renters have low credit scores.
Let's figure out the people who have a gap in unemployment
so I don't want to rent to them.
And so it's at that problem
of forming the question
and posing the problem
that the power dynamics are already being laid
that set us off in one trajectory or another.
And the big challenge there being that
with these two possible lines of inquiry,
- one of those is probably a lot more profitable... - Exactly, exactly.
- ...than the other one. - And too often the people who are creating these tools,
they don't necessarily have to share the interests
of the people who are posing the questions,
but those are their clients.
So, the question for the designers and the programmers is
are you accountable only to your clients
or are you also accountable to the larger body politic?
Are you responsible for what these tools do in the world?
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Man: Can you lift up your arm a little?
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