Teaching yourself about structural racism will improve your machine learning(academic.oup.com)
academic.oup.com
Teaching yourself about structural racism will improve your machine learning
https://academic.oup.com/biostatistics/advance-article/doi/10.1093/biostatistics/kxz040/5631851
66 comments
The article mentions why simply excluding race isn't good enough:
"Crucially, incorporating more proximal and predictive variables into models, rather than relying on race variables to act as proxies, will improve transportability of algorithms across contexts."
If we want better models then they need to also model structural racism.
"Crucially, incorporating more proximal and predictive variables into models, rather than relying on race variables to act as proxies, will improve transportability of algorithms across contexts."
If we want better models then they need to also model structural racism.
> If you don't feed race in as a feature, however, the outputs are hardly racist.
The article addresses this
> When “race-neutral” approaches are employed in model development, prediction will tend to be poorer for racial minority populations.... Two explanations for differentially poorer model performance can be addressed by collecting more data: too few observations of members of racial minority groups and unrepresentative sampling that can differentially limit generalizability. However, an additional cause of algorithmic bias is not well appreciated and cannot be overcome simply by adding more of the same kind of data to a learner....
The article addresses this
> When “race-neutral” approaches are employed in model development, prediction will tend to be poorer for racial minority populations.... Two explanations for differentially poorer model performance can be addressed by collecting more data: too few observations of members of racial minority groups and unrepresentative sampling that can differentially limit generalizability. However, an additional cause of algorithmic bias is not well appreciated and cannot be overcome simply by adding more of the same kind of data to a learner....
That is a completely different argument, and has nothing to do with structural racism. That is literally just saying that minorities are less likely to have made up a sizable portion of the data sets trained on, because they're minorities, and the model is potentially less well suited to deal with issues specifically related to that minority. If the primary point of the article was that we should overcorrect for this by making including disproportionately high representation of minority data, then that's a potentially reasonable case, so long as it doesn't break the model. In the case of facial recognition not working as well on non-whites, for example, I think it's an entirely reasonable case to make to include a disproportionately higher amount of training data on those areas where the model fails to perform its function.
But you also have to realize that this is always going to be somewhat arbitrary.
But you also have to realize that this is always going to be somewhat arbitrary.
I think you're confusing race with racism.
Generally speaking..
Ignoring or dismissing relevance of race is privilege for those in the majority who can trivialize something that doesn't apply to them. Apply that to models too and see how much they miss. Value can be where others might not.
It's kind of like making a decision that if one group hasn't had an experience of race, it doesn't mean anyone else could have either. It also signals that if they don't see value or understanding in it, therefore there can't be any in it.
It takes a truly open mind to entertain any viewpoint that isn't immediately their own.
The reality is, many people live with a reality that might seem unimaginable to viewpoints like the above.
I practice each day as if humanity is one family. I go out of my way to talk to every kind of person that doesn't look like me. It doesn't mean strangers talk to me, especially from the majority..
When programmers all think and look the same and grew up in the same way and places, software tends catch fewer edge cases of everyone who doesn't look like them, in areas of computer vision.
If we step beyond CV, and look into hilarious things like automatic motion sensor sinks only detecting certain shades (or lack thereof) of skin.
Just some food for thought, happy to chat offline too :)
Generally speaking..
Ignoring or dismissing relevance of race is privilege for those in the majority who can trivialize something that doesn't apply to them. Apply that to models too and see how much they miss. Value can be where others might not.
It's kind of like making a decision that if one group hasn't had an experience of race, it doesn't mean anyone else could have either. It also signals that if they don't see value or understanding in it, therefore there can't be any in it.
It takes a truly open mind to entertain any viewpoint that isn't immediately their own.
The reality is, many people live with a reality that might seem unimaginable to viewpoints like the above.
I practice each day as if humanity is one family. I go out of my way to talk to every kind of person that doesn't look like me. It doesn't mean strangers talk to me, especially from the majority..
When programmers all think and look the same and grew up in the same way and places, software tends catch fewer edge cases of everyone who doesn't look like them, in areas of computer vision.
If we step beyond CV, and look into hilarious things like automatic motion sensor sinks only detecting certain shades (or lack thereof) of skin.
Just some food for thought, happy to chat offline too :)
> Does the fact that black Americans used to be slaves in the US, or were kept out of certain housing markets, contribute towards the fact that black Americans, on average, have lower credit scores, income, wealth, and collateral? Of course. But is this model racist? Literally not at all.
Saying that because history was racist means I'm absolved of responsibility going forward is not a strong argument. Redlining was literally a racist behavior. The point of the article is to be aware of it so you can try do better than in the past.
Saying that because history was racist means I'm absolved of responsibility going forward is not a strong argument. Redlining was literally a racist behavior. The point of the article is to be aware of it so you can try do better than in the past.
> If you don't feed race in as a feature, however, the outputs are hardly racist.
Isn't the argument being made in various different place that race is there in the data regardless of whether or not you encode it as a feature because the humans that create the data already used race as part of creating the data for the model. And by creating the data, I mean the interactions in real life that create the inputs.
You can't escape it because it's already in the inputs to the model because it's a rather insidious part of our society.
Isn't the argument being made in various different place that race is there in the data regardless of whether or not you encode it as a feature because the humans that create the data already used race as part of creating the data for the model. And by creating the data, I mean the interactions in real life that create the inputs.
You can't escape it because it's already in the inputs to the model because it's a rather insidious part of our society.
People seem to be trying to answer the practical question "How do we build a non-racist model?" the answer to whi h depends entirely on the philosophical question "what does it mean for a model to be racist?" the answer to which no one can seem to agree on.
The argument is that upstream often doesn't have (or is willfully blind to having) a clear understanding of the features impacting the results of the model. The author is making the case that model builders need to be aware that their models may further structural racism, and need to do the work to push this awareness upstream.
Upstream may think the model--which to them looks like a black box--is perfectly rational, optimally profitable and socially beneficial in a way that is it is not. We have numerous examples where a computer has driven a decision and humans carried out its orders in ways that harmed people. Remember the man violently dragged off the United Airlines flight in 2017[1]? ICE justifies detention by tweaking their risk management software to always recommend detention[2].
This is why we need to care as people building the systems that make these decisions.
1. https://www.nbcnews.com/storyline/airplane-mode/united-fiasc...
2. https://www.vice.com/en_us/article/evk3kw/ice-modified-its-r...
Upstream may think the model--which to them looks like a black box--is perfectly rational, optimally profitable and socially beneficial in a way that is it is not. We have numerous examples where a computer has driven a decision and humans carried out its orders in ways that harmed people. Remember the man violently dragged off the United Airlines flight in 2017[1]? ICE justifies detention by tweaking their risk management software to always recommend detention[2].
This is why we need to care as people building the systems that make these decisions.
1. https://www.nbcnews.com/storyline/airplane-mode/united-fiasc...
2. https://www.vice.com/en_us/article/evk3kw/ice-modified-its-r...
If the case you're making is that you think that there should be a national effort to correct for historical injustices that were done by the state by actively discriminating by race, that is a completely different discussion.
That is what proponents of the structural racism model are doing. Here's an example I took from the book Weapons of Math Destruction:
When people are convicted of a crime, they undergo a number of personality tests, including the LSI-R (Level of Service Inventory - Revised). This is a highly detailed questionnaire that asks about prior convictions, whether the prisoner had accomplices in their crimes, whether drugs or alcohol were involved, etc.
It does not ask about race.
What it does ask about are things which highly correlate with race, such as the number of police encounters (no criminal suspicion necessary), the number of friends/family/neighbours who have committed crimes, etc. If two first-time offenders have committed identical crimes but one of them grew up in wealthy suburbs and the other grew up in the rough inner city, they will receive very different scores on the LSI-R.
So what do they use the LSI-R for? They feed it into a model which assigns the offender a recidivism risk score. Then they use that risk factor directly when determining the person's sentence, restrictions, parole eligibility, etc.
So now we're not even talking about historical injustices, we're talking about ongoing injustice based on historical injustice. It's a vicious cycle, or a negative feedback loop, if you will. This is a serious problem!
Edit: Just to add another piece of the puzzle, the reason wealthy suburbs vs rough inner cities correlate so highly with race is a direct result of the historical racist practices of redlining [1] and white flight [2]. Now combine that with grinding poverty (also a result of redlining and segregation) and the war on drugs, and the result is high-crime neighbourhoods in the inner city. Those high crime neighbourhoods attract highly increased police presence, which leads to more convictions, which leads to more patrols, etc. This is another vicious cycle which feeds into the above statistical model.
[1] https://en.wikipedia.org/wiki/Redlining
[2] https://en.wikipedia.org/wiki/White_flight
That is what proponents of the structural racism model are doing. Here's an example I took from the book Weapons of Math Destruction:
When people are convicted of a crime, they undergo a number of personality tests, including the LSI-R (Level of Service Inventory - Revised). This is a highly detailed questionnaire that asks about prior convictions, whether the prisoner had accomplices in their crimes, whether drugs or alcohol were involved, etc.
It does not ask about race.
What it does ask about are things which highly correlate with race, such as the number of police encounters (no criminal suspicion necessary), the number of friends/family/neighbours who have committed crimes, etc. If two first-time offenders have committed identical crimes but one of them grew up in wealthy suburbs and the other grew up in the rough inner city, they will receive very different scores on the LSI-R.
So what do they use the LSI-R for? They feed it into a model which assigns the offender a recidivism risk score. Then they use that risk factor directly when determining the person's sentence, restrictions, parole eligibility, etc.
So now we're not even talking about historical injustices, we're talking about ongoing injustice based on historical injustice. It's a vicious cycle, or a negative feedback loop, if you will. This is a serious problem!
Edit: Just to add another piece of the puzzle, the reason wealthy suburbs vs rough inner cities correlate so highly with race is a direct result of the historical racist practices of redlining [1] and white flight [2]. Now combine that with grinding poverty (also a result of redlining and segregation) and the war on drugs, and the result is high-crime neighbourhoods in the inner city. Those high crime neighbourhoods attract highly increased police presence, which leads to more convictions, which leads to more patrols, etc. This is another vicious cycle which feeds into the above statistical model.
[1] https://en.wikipedia.org/wiki/Redlining
[2] https://en.wikipedia.org/wiki/White_flight
I assume that the LSI-R is something that is actually trained based off of how much those factors actually predict the rate of recidivism, though, no? If friends/family/neighbors who have committed crimes is an accurate predictor of recidivism, the fact that black Americans in the inner city have more friends/family/neighbors who have committed crimes does not make the model racist. They're either good predictors or they're not. A black kid in the inner city with friends/family/neighbors who have committed crimes very likely does have a higher rate of recidivism than a white kid in the suburbs, and if this weren't true, but was being predicted by the model, then this would simply be a bad model. If it turns out that there are many black kids who happen to live near neighbors who've committed crimes, but actually do not have a higher rate of recidivism, then the model is as racist as it is using a poorly correlated indicators of recidivism.
Your indicator for whether or not a model is racist cannot simply be that the model produces outputs that are delineated by race in such a manner that is unpalatable. So long as the model is actually not using race as a means of predicting outcomes, though, any behavior that is racist would simply be due to including poor features.
Your indicator for whether or not a model is racist cannot simply be that the model produces outputs that are delineated by race in such a manner that is unpalatable. So long as the model is actually not using race as a means of predicting outcomes, though, any behavior that is racist would simply be due to including poor features.
I think you’re still missing the point. Whether the model is accurate or not is beside the point. A completely accurate model may indeed show a higher recidivism risk for an inner city kid compared to one from the suburbs. If it’s used in sentencing or other life-affecting decisions then it’s going to amplify historical injustices.
People commit more crimes when they have less opportunity. People have less opportunity when they grow up in high crime neighbourhoods. This is a negative feedback loop which was started by slavery and accelerated by segregation and redlining.
It’s not enough to use a hands-off approach. To correct the problem requires an active push in the opposite direction, to restore opportunity and break the cycles.
Edit: Think of it this way. You and some friends are playing Monopoly, drinking a few beers and having a great time. An hour and a half into the game (we all know games of Monopoly can last 4 hours or more), you discover one of your friends has been cheating. Now what?
He says "Sorry everyone! I'll stop cheating now and everything will be fine."
Is that true? Of course not. The proceeds from cheating may have been used to acquire the orange properties and maybe even put houses up on them. Every time you and the other friends land on those properties you end up paying rent to the previously cheating friend. Rent that he should not be collecting because those assets were acquired by cheating.
This is what it's like to have historical injustices continue to perpetuate into the future.
People commit more crimes when they have less opportunity. People have less opportunity when they grow up in high crime neighbourhoods. This is a negative feedback loop which was started by slavery and accelerated by segregation and redlining.
It’s not enough to use a hands-off approach. To correct the problem requires an active push in the opposite direction, to restore opportunity and break the cycles.
Edit: Think of it this way. You and some friends are playing Monopoly, drinking a few beers and having a great time. An hour and a half into the game (we all know games of Monopoly can last 4 hours or more), you discover one of your friends has been cheating. Now what?
He says "Sorry everyone! I'll stop cheating now and everything will be fine."
Is that true? Of course not. The proceeds from cheating may have been used to acquire the orange properties and maybe even put houses up on them. Every time you and the other friends land on those properties you end up paying rent to the previously cheating friend. Rent that he should not be collecting because those assets were acquired by cheating.
This is what it's like to have historical injustices continue to perpetuate into the future.
I don't think this is a good analogy.
In your example, it could be argued that a person who isn't cheating can keep collecting rent on their properties (however "unfair" that might seem) - i.e. the (un)fairness of the current situation (and the degree to which we try to "fix" it) depends on the path used to get there.
In the "inner city kid" example, it doesn't matter how people got there - either due to historical injustices / racism (i.e. "cheating" by the rest of society) or simply because their parents were drunks or criminals or poor or whatever - so, again, race doesn't and shouldn't matter, and helping poor inner city black kids in preference to poor inner city white kids is racism, no other way of putting it.
In your example, it could be argued that a person who isn't cheating can keep collecting rent on their properties (however "unfair" that might seem) - i.e. the (un)fairness of the current situation (and the degree to which we try to "fix" it) depends on the path used to get there.
In the "inner city kid" example, it doesn't matter how people got there - either due to historical injustices / racism (i.e. "cheating" by the rest of society) or simply because their parents were drunks or criminals or poor or whatever - so, again, race doesn't and shouldn't matter, and helping poor inner city black kids in preference to poor inner city white kids is racism, no other way of putting it.
Yes, but you can't build it into your model. In this example, you would end up with a result of putting people back into these communities who have a high level of recidivism. You are actively not avoiding an actual issue because of perceived racial injustice when the issue is not racial.
This is the problem with processing our world down racial lines. You're trying to correct for a historical injustice. The fact that race factors into the circumstance of why people are where they are right now doesn't change the fact that those variables lead to recidivism. It's not racist. It's accurate.
If you want to fix the problem, then you need to fix the underlying issues, which tend to be economic. Those economic issues stem from an issue that affects all races, and therefore splitting it across racial lines only serves to reduce the possibility of actual change.
All you're doing when you try to account for historical injustice is slapping a band-aid on a deeper issue.
(Edit: Grammar)
This is the problem with processing our world down racial lines. You're trying to correct for a historical injustice. The fact that race factors into the circumstance of why people are where they are right now doesn't change the fact that those variables lead to recidivism. It's not racist. It's accurate.
If you want to fix the problem, then you need to fix the underlying issues, which tend to be economic. Those economic issues stem from an issue that affects all races, and therefore splitting it across racial lines only serves to reduce the possibility of actual change.
All you're doing when you try to account for historical injustice is slapping a band-aid on a deeper issue.
(Edit: Grammar)
I agree with you when it comes to the model: the model should be as accurate as possible. The big question is what to do with the model. The way it's being used now, the model is kind of a self-fulfilling prophecy. A prediction of high recidivism risk leads to a longer sentence which increases the likelihood of recidivism. This creates a feedback loop which increases real recidivism risk and the model changes to reflect that. If your goal is to reduce crime in society, then this may be a flawed approach.
Yes but that's not about race, that's about how we deal with crime as a society. These things aren't being unfairly applied to minority communities and that's the point. The system would be working the same way for a non-minority community, and it does, where the economic situation is similar.
That's why the racial angle is a waste of everyone's time and energy. It's not the relevant issue. The more relevant issue is how we deal with crime prevention. Currently, we go with a punishment approach rather than a truly rehabilitative one. This also has a lot to do with economics, and lobbying and private prisons and so on. It's much more complicated than 'everybody's racist'.
That's why the racial angle is a waste of everyone's time and energy. It's not the relevant issue. The more relevant issue is how we deal with crime prevention. Currently, we go with a punishment approach rather than a truly rehabilitative one. This also has a lot to do with economics, and lobbying and private prisons and so on. It's much more complicated than 'everybody's racist'.
The purpose of the term structural racism, instead of just racism, is to distinguish the theory of historical racism and its downstream effects from the category people who hold views of racial superiority.
Besides that, there are plenty of people around today who actually are racist and they are major proponents of punishment-based approaches. If you try to switch to rehabilitation and intervention, they will resist you. They hold views that some races are innately of lower intelligence and have higher criminal tendencies. You aren't going to counteract that pressure by saying "the racial angle is a waste of everyone's time."
Besides that, there are plenty of people around today who actually are racist and they are major proponents of punishment-based approaches. If you try to switch to rehabilitation and intervention, they will resist you. They hold views that some races are innately of lower intelligence and have higher criminal tendencies. You aren't going to counteract that pressure by saying "the racial angle is a waste of everyone's time."
Yes, but what relevance does structural racism have to the model?
That's only true only if you believe the majority of people are racist. I don't.
There's also a number of ways that you don't even have to interact with that argument. You can show that the end results. It's not like we don't already have the studies and statistics that show how to resolve these issues. You can easily say to someone who thinks that way 'ok sure, you go on believing that, but even if you do believe that, there are still better ways to resolve this.'
Also most people are actually pretty receptive to new information if you are capable of packaging it well, and acknowledging their biases without judging them for it.
That's only true only if you believe the majority of people are racist. I don't.
There's also a number of ways that you don't even have to interact with that argument. You can show that the end results. It's not like we don't already have the studies and statistics that show how to resolve these issues. You can easily say to someone who thinks that way 'ok sure, you go on believing that, but even if you do believe that, there are still better ways to resolve this.'
Also most people are actually pretty receptive to new information if you are capable of packaging it well, and acknowledging their biases without judging them for it.
It doesn't take a majority of people in anything to create a problem. It only takes a concerted effort by a minority and complacency from the rest.
But besides this, we have too many people fighting a tug-o-war over the term racist. Some people want to apply the word to everyone. Other people think it should only apply to literal Nazis.
But again, that's a distraction. The membership in the Nazi party was around 8.5 million in 1945, yet the population of Germany was around 90 million at the time. That's right, less than 10% of the population were Nazis, yet they controlled everything.
But besides this, we have too many people fighting a tug-o-war over the term racist. Some people want to apply the word to everyone. Other people think it should only apply to literal Nazis.
But again, that's a distraction. The membership in the Nazi party was around 8.5 million in 1945, yet the population of Germany was around 90 million at the time. That's right, less than 10% of the population were Nazis, yet they controlled everything.
What I'm saying is that the focus on race is counterproductive because it's thoroughly irrelevant to the solution. If you have one group blaming minority failure on their race, and another group saying that the failure is because of racism, the conversation revolves around race. We look to racially based solutions, and miss what's right in front of our nose. We assume that the issue is racial when it's not. That's what I mean by distraction.
The topic should be 'How do we fix crime, recidivism, and poverty?'. Pointing out historical injustice does absolutely nothing. There is no reasonable thing to do if that's your focal point, because the only ways to 'resolve' it is what? Reparations? Making decisions about jail time and release based on race? It's illogical. You'd end up putting people with high levels of recidivism back into a community, only serving to repeat the cycle, because you never actually look at the real problem. The things that cause that recidivism. The things that are actually causing it right now, instead of the reasons that it happens to be black people. If history were different, it could have been anybody. It could have been white people, brown people, any color, any ethnicity. It explains why the people in this situation are black more often than they are not, but it does not explain how we fix the problem.
It is an absolute waste of time, and the real issue is that while we screw around talking about pointless grievances people will continue to go to jail, and die, because we're still not talking about the problem.
Edit: I realized that this wasn't quite a response to your last post, but the relevancy is that you will only get complacency if you're focusing on things that can't really be fixed, or that essentially blame others, but what you can do is draw parallels, and essentially say 'Your issues are my issues too, and we can and should work together on them,' which is also true.
The topic should be 'How do we fix crime, recidivism, and poverty?'. Pointing out historical injustice does absolutely nothing. There is no reasonable thing to do if that's your focal point, because the only ways to 'resolve' it is what? Reparations? Making decisions about jail time and release based on race? It's illogical. You'd end up putting people with high levels of recidivism back into a community, only serving to repeat the cycle, because you never actually look at the real problem. The things that cause that recidivism. The things that are actually causing it right now, instead of the reasons that it happens to be black people. If history were different, it could have been anybody. It could have been white people, brown people, any color, any ethnicity. It explains why the people in this situation are black more often than they are not, but it does not explain how we fix the problem.
It is an absolute waste of time, and the real issue is that while we screw around talking about pointless grievances people will continue to go to jail, and die, because we're still not talking about the problem.
Edit: I realized that this wasn't quite a response to your last post, but the relevancy is that you will only get complacency if you're focusing on things that can't really be fixed, or that essentially blame others, but what you can do is draw parallels, and essentially say 'Your issues are my issues too, and we can and should work together on them,' which is also true.
You can have an accurate prediction which also reflects systemic bias.
There was a story recently about NYC cops being given race based targets for arrests. If that data was fed into a system and predictions generated they could be both correct and racist.
That's maybe an extreme example, I think the person you're replying to was trying to illustrate the same thing but with greater indirection between the racism and the arrest.
To give a non-race example, I've heard that ugly people get convicted at a higher rate than good looking people. So a 'hot or not' rating could help predict reoffending conviction rates. I'd assume we would want to adjust our models to avoid that, even though it's not an incorrect prediction.
There was a story recently about NYC cops being given race based targets for arrests. If that data was fed into a system and predictions generated they could be both correct and racist.
That's maybe an extreme example, I think the person you're replying to was trying to illustrate the same thing but with greater indirection between the racism and the arrest.
To give a non-race example, I've heard that ugly people get convicted at a higher rate than good looking people. So a 'hot or not' rating could help predict reoffending conviction rates. I'd assume we would want to adjust our models to avoid that, even though it's not an incorrect prediction.
But that's not an issue with the model, that's an issue with our response to the model.
Fundamentally there's nothing wrong with this. The somewhat harsh truth is that 'systemic bias' is actually just... statistics. There are a lot of minority criminals. It's not that there's something special about these people that makes them criminals - it's the same thing that makes everybody turn to crime: lack of opportunity, low capacity for upward mobility, limited access to education, and so on. We act as if the information is not accurate, and that this is a result of racism, but the cold truth is that if you were to take a white person and a black person and only look at the likelihood of criminal behavior, the black person is going to come out on top of that. It's just the math.
Where the human element comes in is where we decide what to do about that math. Do we blame the race? Do we utilize these models to preemptively police people on the basis of race? The answer obviously, should be no. That said, our model can give us insights into this. We can take this data and go, 'well we know that it's unlikely that race is the major causal factor here, so what else can we look at?'
This is a much deeper issue, and 'structural racism' is a really bad way to look at it, because it forces you to focus on the racial elements, even if they're not relevant. It's asking for a model that is not representative of reality, because it looks ugly, rather than looking at it for what it is - just data - and figuring out what to do with that data.
Fundamentally there's nothing wrong with this. The somewhat harsh truth is that 'systemic bias' is actually just... statistics. There are a lot of minority criminals. It's not that there's something special about these people that makes them criminals - it's the same thing that makes everybody turn to crime: lack of opportunity, low capacity for upward mobility, limited access to education, and so on. We act as if the information is not accurate, and that this is a result of racism, but the cold truth is that if you were to take a white person and a black person and only look at the likelihood of criminal behavior, the black person is going to come out on top of that. It's just the math.
Where the human element comes in is where we decide what to do about that math. Do we blame the race? Do we utilize these models to preemptively police people on the basis of race? The answer obviously, should be no. That said, our model can give us insights into this. We can take this data and go, 'well we know that it's unlikely that race is the major causal factor here, so what else can we look at?'
This is a much deeper issue, and 'structural racism' is a really bad way to look at it, because it forces you to focus on the racial elements, even if they're not relevant. It's asking for a model that is not representative of reality, because it looks ugly, rather than looking at it for what it is - just data - and figuring out what to do with that data.
Other commenters have imo adequately addressed the major flaws in this comment/argument so I won't be redundant here.
That being said what is unfortunately NOT shocking, is that anyone upvoted this comment at all and it that doesn't have a negative score.
Pretending something doesn't exist (or ignoring the fact that it does exist), and modeling systems under that pretense, doesn't make the thing not exist - it only reinforces the existence of the thing.
That being said what is unfortunately NOT shocking, is that anyone upvoted this comment at all and it that doesn't have a negative score.
Pretending something doesn't exist (or ignoring the fact that it does exist), and modeling systems under that pretense, doesn't make the thing not exist - it only reinforces the existence of the thing.
Downvoted by racists again.
I can understand the parent commenter downvoting, esp if not convinced by the position of the article or the subsequent comments.
I knew better than to wade into this topic on this site given the audience demo of this site, I am hardly surprised at the reaction by lurkers.
I am encouraged and heartened by the commenters that actually read the article and have provided excellent reasons why you would want to build systems and models that account for systemic racism and bias.
Very illuminating all around.
I can understand the parent commenter downvoting, esp if not convinced by the position of the article or the subsequent comments.
I knew better than to wade into this topic on this site given the audience demo of this site, I am hardly surprised at the reaction by lurkers.
I am encouraged and heartened by the commenters that actually read the article and have provided excellent reasons why you would want to build systems and models that account for systemic racism and bias.
Very illuminating all around.
Re: the bank loan scenario. The model was implemented by people. The assumption is everyone considered for a loan has had a fair opportunity to reach the threshold to be approved. That is not the case.
"A national effort to correct historical injustices" is one way but not the only. The people who create these models can refine the model or create others that determine acceptable business risks to provide loans to an under-served market.
"A national effort to correct historical injustices" is one way but not the only. The people who create these models can refine the model or create others that determine acceptable business risks to provide loans to an under-served market.
> The assumption is everyone considered for a loan has had a fair opportunity to reach the threshold to be approved
What? In what world is this assumption being made? Do we assume that every person was born into a stable household? That every person has the same IQ, the same height, looks, had the same lucky encounters with the right people whose needs intersected with their capabilities? There are countless dimensions along which people are not the same, why would you ever assume that whether or not someone gets a bank loan has taken into account every advantage or disadvantage they have been given?
A bank loan is a business transaction where the likelihood of you being able to pay back the loan at the prescribed interest rate is being determined based off of highly predictive features, that's it.
If you want to do corrective social justice, do it in a handful of places, and let the rest of the system operate off of sensible rules. Social justice cannot permeate every single decision made in our society, it is irreducibly complex even on a single decision.
What? In what world is this assumption being made? Do we assume that every person was born into a stable household? That every person has the same IQ, the same height, looks, had the same lucky encounters with the right people whose needs intersected with their capabilities? There are countless dimensions along which people are not the same, why would you ever assume that whether or not someone gets a bank loan has taken into account every advantage or disadvantage they have been given?
A bank loan is a business transaction where the likelihood of you being able to pay back the loan at the prescribed interest rate is being determined based off of highly predictive features, that's it.
If you want to do corrective social justice, do it in a handful of places, and let the rest of the system operate off of sensible rules. Social justice cannot permeate every single decision made in our society, it is irreducibly complex even on a single decision.
that a model could easily mistake race as a causal factor.
Statistical models as used in real world systems don’t have a concept of a “casual factor”. It literally doesn’t matter for the model why in certain zip codes, there is more property crime. It doesn’t care if it’s caused by poverty of residents, by pigmentation of their skin, by lead in the paint, or by the cultural traits of residents. All it cares about is the correlation: if the risk is higher, the insurance premiums go up too. For some it might seem unfair, and for some groups such statistical discrimination might be illegal (though not for all, eg. it’s perfectly legal to charge men higher insurance rates, which suggests that the moral principle here is not equality, but rather compensation for historical mistreatment), but without a doubt, from the model’s and business perspective, such reasoning is undoubtedly correct.
Statistical models as used in real world systems don’t have a concept of a “casual factor”. It literally doesn’t matter for the model why in certain zip codes, there is more property crime. It doesn’t care if it’s caused by poverty of residents, by pigmentation of their skin, by lead in the paint, or by the cultural traits of residents. All it cares about is the correlation: if the risk is higher, the insurance premiums go up too. For some it might seem unfair, and for some groups such statistical discrimination might be illegal (though not for all, eg. it’s perfectly legal to charge men higher insurance rates, which suggests that the moral principle here is not equality, but rather compensation for historical mistreatment), but without a doubt, from the model’s and business perspective, such reasoning is undoubtedly correct.
The supreme court had a different take on it.
You should check out the disparate impact rule. If your involved in housing.
https://www.hud.gov/press/press_releases_media_advisories/HU...
You should check out the disparate impact rule. If your involved in housing.
https://www.hud.gov/press/press_releases_media_advisories/HU...
I did research briefly in a lab studying heart rate variability. HRV is a really interesting statistic in predicting heart health outcomes, and very interesting is the racial difference in HRV.
Basically, African Americans exhibit much higher heart rate variability, meaning their nervous system is much quicker to react to stimuli (quicker time to fight or flight response, for example) and this still isn’t well understood in the field.
A naive understanding is that racial physiology is just different. And plenty of people will stand by this. However, self reported stress scores offer some insight into the difference.
High stress African Americans with High HRV lived as long as Low stress, low HRV White/Asian Americans. Most likely, the process by which the nervous system regulates itself is heavily influenced by life course events.
Medical science, in my experience, lacks in quantifying these social factors, and too often underplays their significance in determining physiological differences. Humans are incredibly dynamic systems, and the case can be made that we adapt to stimuli in order to survive. It’s certainly possible that the physiological difference we observe in different racial populations is due to survival based on this principal.
It’s only recently that I’ve seen research trying to get at these social/physiological mechanisms, but as far as funding is concerned, hard biological sciences are more interesting. Everyone just wants to edit the genome and call it a day, but I think we could get much further if we understood how life events lead to physiological ailments later in life.
Basically, African Americans exhibit much higher heart rate variability, meaning their nervous system is much quicker to react to stimuli (quicker time to fight or flight response, for example) and this still isn’t well understood in the field.
A naive understanding is that racial physiology is just different. And plenty of people will stand by this. However, self reported stress scores offer some insight into the difference.
High stress African Americans with High HRV lived as long as Low stress, low HRV White/Asian Americans. Most likely, the process by which the nervous system regulates itself is heavily influenced by life course events.
Medical science, in my experience, lacks in quantifying these social factors, and too often underplays their significance in determining physiological differences. Humans are incredibly dynamic systems, and the case can be made that we adapt to stimuli in order to survive. It’s certainly possible that the physiological difference we observe in different racial populations is due to survival based on this principal.
It’s only recently that I’ve seen research trying to get at these social/physiological mechanisms, but as far as funding is concerned, hard biological sciences are more interesting. Everyone just wants to edit the genome and call it a day, but I think we could get much further if we understood how life events lead to physiological ailments later in life.
There is also the political implications. Nobody wants to fund research which will only stir up controversy, especially since many higher education institutions are located in liberal areas. Too many medical studies on gender, race, and intelligence would only create unrest. Just take a look at how badly the Stanford ML paper on predicting gender orientation was received. Emphasizing the differences in "fight or flight" response timings may save lives, but at the same time provide ammunition for people to draw lines between metrics and historical events/practices along societal fault lines, which I think everyone can agree is not a good thing.
> provide ammunition for people to draw lines between metrics and historical events/practices along societal fault lines, which I think everyone can agree is not a good thing.
Putting your head in the sand and trying to deny the existence of potentially uncomfortable facts actually fuels these fringe thinkers more imo. Part of their whole schtick is that the truth is being hidden from them.
Look at how the media handled the claim that Serena Williams couldn't beat a top 10 male player. Instead of actually putting it to the test, the whole angle was about how insulting and preposterous that was etc etc.
We are not all the same, but we deserve to be treated so. It's as simple as that. Trying to halt scientific progress because it doesn't fit your world view is quasi religious.
Putting your head in the sand and trying to deny the existence of potentially uncomfortable facts actually fuels these fringe thinkers more imo. Part of their whole schtick is that the truth is being hidden from them.
Look at how the media handled the claim that Serena Williams couldn't beat a top 10 male player. Instead of actually putting it to the test, the whole angle was about how insulting and preposterous that was etc etc.
We are not all the same, but we deserve to be treated so. It's as simple as that. Trying to halt scientific progress because it doesn't fit your world view is quasi religious.
Thank you for mentioning this.
>quicker time to fight or flight response
If cops are more likely to stop you because of your skin color (also further increasing the chances of further abuses), that would probably have an impact on your physiology over time.
https://www.nationalgeographic.com/magazine/2018/04/the-stop...
>quicker time to fight or flight response
If cops are more likely to stop you because of your skin color (also further increasing the chances of further abuses), that would probably have an impact on your physiology over time.
https://www.nationalgeographic.com/magazine/2018/04/the-stop...
Interestingly enough, I transitioned to gun violence research, which is what I do now, after wondering something along these lines.
If you live in a neighborhood where gun violence is a common occurrence, do you still get startled when you hear gunshots? If so, this would have a physiological effect, because it breaks your rhythm in an abrupt way, and does so at the frequency of hearing gun shots.
Obviously this is highly theoretical, but if true it would mean that just being in the proximity of violence puts you at an extra health risk, one that very few people would assume.
If you live in a neighborhood where gun violence is a common occurrence, do you still get startled when you hear gunshots? If so, this would have a physiological effect, because it breaks your rhythm in an abrupt way, and does so at the frequency of hearing gun shots.
Obviously this is highly theoretical, but if true it would mean that just being in the proximity of violence puts you at an extra health risk, one that very few people would assume.
From personal experience myself and with friends, the level by which you are "startled" is in direct proportion to the proximity of the gunfire.
e.g. Hearing gunshots in the distance, you recognize they are gunshots, take note, but continue on your way. Hearing gunshots on your block or within a group you are in, you take immediate notice and react accordingly. But in either case, any time you hear them, is a reminder that the underlying fear never leaves.
e.g. Hearing gunshots in the distance, you recognize they are gunshots, take note, but continue on your way. Hearing gunshots on your block or within a group you are in, you take immediate notice and react accordingly. But in either case, any time you hear them, is a reminder that the underlying fear never leaves.
I love how my comment, which was only praise of the parent comment, was downvoted - by someone I can only assume is racist.
Hacker News at its finest ;)
Hacker News at its finest ;)
> 4. Conclusion "...grounding one’s work in an understanding of structural racism will improve model accuracy..."
It is not an interesting result to say models not modeling reality are less accurate; the cogent discussion is to what degree systemic racism exists IN reality. This is textbook begging the question.
> Acknowledgements: Conflict of Interest: None declared.
> Funding: Whitney R. Robinson is supported by the National Institute of Minority Health
I am not familiar with standards of conflict declaration, but this looks like a pretty clear conflict of interest to me.
It is not an interesting result to say models not modeling reality are less accurate; the cogent discussion is to what degree systemic racism exists IN reality. This is textbook begging the question.
> Acknowledgements: Conflict of Interest: None declared.
> Funding: Whitney R. Robinson is supported by the National Institute of Minority Health
I am not familiar with standards of conflict declaration, but this looks like a pretty clear conflict of interest to me.
This paper fails to account for (or even mention) genetics. To expand on the breathing capacity example, consider the case of the sherpas.
https://www.npr.org/sections/goatsandsoda/2017/05/28/5302041...
https://www.npr.org/sections/goatsandsoda/2017/05/28/5302041...
The real problem is that there are mutually exclusive desiderata from your models.
1. If you have two people with identical relevant behavior and different races, you want the model to score them identically.
2. Each race should receive a comparable distribution of scores.
3. The scores should be as accurate of a predictor of ground facts as possible.
Relax the first desiderata and your model is now either explicitly or implicitly (via irrelevant proxy variables) using race to determine results, opening you up to racial discrimination lawsuits. Relax the second desiderata and your model is now creating disparate impact across racial groups, opening you up to racial discrimination lawsuit. Relax the third and you're leaving accuracy, and thus money, on the table.
1. If you have two people with identical relevant behavior and different races, you want the model to score them identically.
2. Each race should receive a comparable distribution of scores.
3. The scores should be as accurate of a predictor of ground facts as possible.
Relax the first desiderata and your model is now either explicitly or implicitly (via irrelevant proxy variables) using race to determine results, opening you up to racial discrimination lawsuits. Relax the second desiderata and your model is now creating disparate impact across racial groups, opening you up to racial discrimination lawsuit. Relax the third and you're leaving accuracy, and thus money, on the table.
Disparate impact of results is NOT proof of racism. People are complicated - and some racial correlations are perfectly valid - at least until enough members of a racial group decide to change them...
"Conflict of Interest: None declared."
In an article about structural racism, I would have expected more here.
In an article about structural racism, I would have expected more here.
A bias is not the same thing as a conflict of interest.
This post was on the front page 10 minutes ago but now it can't be found anywhere. It's pretty disappointing that it has been removed solely on the basis of being about race or being controversial. Race and racism are a part of society and it presumably made it to the front page because it's a topic that enough people found interesting to vote for...so why remove it?
It hasn't been removed. I can see it at position 43 at the moment, but it is showing as flagged for some reason.
People of every race find success in technology and in America more broadly. Many of these people's ancestors came to America with nothing. Those groups which find more success than average generally have a cultural focus on education and other behaviors associated with responsible action. Groups that don't succeed generally do not share these qualities.
It is these cultural differences which cause most of the group disparity in America, not "structural racism", yet the "critical race theorists" (race hustlers and grievance mongers) and their followers ignore these major factors and replace them with straw men.
In order to fix a problem, it's important to understand the actual causes. The sociologists and other assorted race hustlers will only divide us and lead us astray.
It is these cultural differences which cause most of the group disparity in America, not "structural racism", yet the "critical race theorists" (race hustlers and grievance mongers) and their followers ignore these major factors and replace them with straw men.
In order to fix a problem, it's important to understand the actual causes. The sociologists and other assorted race hustlers will only divide us and lead us astray.
The definition for structural racism according to the article:
> Structural racism refers to “the totality of ways in which societies foster [racial] discrimination, via mutually reinforcing [inequitable] systems...(e.g., in housing, education, employment, earnings, benefits, credit, media, health care, criminal justice, etc.) that in turn reinforce discriminatory beliefs, values, and distribution of resources,” reflected in history, culture, and interconnected institutions (Bailey and others, 2017).
I think I might be misunderstanding, but given this includes “culture”, is this so sufficiently broad such that hypothetical scenarios such as this (no idea if this is accurate) would be captured “white people are culturally more likely to use crystal meth than other racial groups, ergo they are victims of (a certain kind of) systemic racism”?
It seems like this is just a catchall for any kind of error associated with a racial group, and the article is merely cautioning against such errors. If so, it begs the questions “why not just say so?” and “why use such a loaded term like systemic racism?”.
> Structural racism refers to “the totality of ways in which societies foster [racial] discrimination, via mutually reinforcing [inequitable] systems...(e.g., in housing, education, employment, earnings, benefits, credit, media, health care, criminal justice, etc.) that in turn reinforce discriminatory beliefs, values, and distribution of resources,” reflected in history, culture, and interconnected institutions (Bailey and others, 2017).
I think I might be misunderstanding, but given this includes “culture”, is this so sufficiently broad such that hypothetical scenarios such as this (no idea if this is accurate) would be captured “white people are culturally more likely to use crystal meth than other racial groups, ergo they are victims of (a certain kind of) systemic racism”?
It seems like this is just a catchall for any kind of error associated with a racial group, and the article is merely cautioning against such errors. If so, it begs the questions “why not just say so?” and “why use such a loaded term like systemic racism?”.
Modern sociology in no way resembles a scientific study and is heavily politicized. A bunch of nonsense political "researchers" citing each other in drivel papers desperate for relevance.
It's now leaking into other fields. I remember when I first heard stem be changed to steam to include "arts" a laughed at how inclusive and utterly useless it is.
It's now leaking into other fields. I remember when I first heard stem be changed to steam to include "arts" a laughed at how inclusive and utterly useless it is.
Postmodern sociology is revisionist towards trying to see society only through a lens that is also easily probable to be statistically sound, this is because of how more quantitative socioeconomic theories failed to predict very big crises or events in society. It can be argued that asking for scientific rigor in sociology to the same extent as in other human sciences raises the bar too much because to validate some theories the experiments are either impossible, prohibitively expensive, or so massive that they would bias the whole of society.
Also, I think it would marvel you knowing how much things you would call "scientific study" are also heavily politicized.
If this bothers you to a big extent I would recommend you try to find comfort in thinking about postmodern sociology as a religion different than yours. They won't be bothered by it and it will probably fit your mindset in a more soothing way than thinking about them as scientists. It's not that they are trying to publish their findings in ACM TOPLAS or something, they have their own community and books and kinda like it.
Also, I think it would marvel you knowing how much things you would call "scientific study" are also heavily politicized.
If this bothers you to a big extent I would recommend you try to find comfort in thinking about postmodern sociology as a religion different than yours. They won't be bothered by it and it will probably fit your mindset in a more soothing way than thinking about them as scientists. It's not that they are trying to publish their findings in ACM TOPLAS or something, they have their own community and books and kinda like it.
If it was treated as religion instead is science I would be open to that, but the papers churned out by "experts" today become policy tomorrow that effect everyone.
Religions dictate and have been dictating policy since forever we had governments and even before that. It's what people understand and mostly everyone seems to be ok with it.
I do think of it as a religion—one that is state sponsored and masquerades as science.
They even make up math. I had a sociology prof in college teach us that to mathematically determine that there is a difference between groups, the difference between the means had to be greater than the range in either group (not even any reference to sample size). I replied that by this test you couldn't even determine that there is a height difference between men and women, and the prof said that in fact you can't and that it was a good example of this test.
That professor was wrong or your understanding is.
There are plenty of statistically viable ways to determine differences between groups. Even a simple T-test is usually sufficient.
The difference of means does not have to be more than the range in either group. Standard deviation is what’s important here. You use a test statistic to determine the relative value of the real mean within a confidence interval. It’s whether these intervals overlap that you can determine a significant difference between groups.
There are plenty of statistically viable ways to determine differences between groups. Even a simple T-test is usually sufficient.
The difference of means does not have to be more than the range in either group. Standard deviation is what’s important here. You use a test statistic to determine the relative value of the real mean within a confidence interval. It’s whether these intervals overlap that you can determine a significant difference between groups.
I know that the professor was wrong. That was the point of the comment. That a professor in a so called science class could get the math so comically wrong.
Sociology is way more rigorous than you’re making it out to be. Studying people is hard, but it’s making good faith effort in that direction, reproducibility crisis notwithstanding.
Take the “Sokal 2.0” affair, where some profs sent obviously bogus research to various journals. Notably, while they were able to get “rape culture among dogs at the dog park” (or maybe it was racism, easy to look up) published in a gender studies journal, they couldn’t get published in sociology journals. Sociology has standards. The absurdities committed by gender studies as an institution don’t falsify racism/other forms of oppression. They might be wrong, but you’re certainly not right.
Take the “Sokal 2.0” affair, where some profs sent obviously bogus research to various journals. Notably, while they were able to get “rape culture among dogs at the dog park” (or maybe it was racism, easy to look up) published in a gender studies journal, they couldn’t get published in sociology journals. Sociology has standards. The absurdities committed by gender studies as an institution don’t falsify racism/other forms of oppression. They might be wrong, but you’re certainly not right.
>Sociology has standards.
Mostly political and much of it unreproducible. Desperate political academics using non science to argue their opinions and right and citing each other into fake legitimacy.
Garbage studies garbage standards garbage journals from people who couldn't hack it elsewhere.
Mostly political and much of it unreproducible. Desperate political academics using non science to argue their opinions and right and citing each other into fake legitimacy.
Garbage studies garbage standards garbage journals from people who couldn't hack it elsewhere.
Please don't take HN threads on generic ideological tangents. They are boring and repetitive and therefore off topic here.
https://news.ycombinator.com/newsguidelines.html
https://news.ycombinator.com/newsguidelines.html
It wasnt an unrelated tangent. It's directly related to the drivel in op.
Tangents are always related at some point. The problem is not where they start, but what they lead to. If they start from drivel but lead to hotter drivel, like internet ideology wars, HN gets much worse. Please just avoid generic ideological battle here.
Fair enough, although I think it's unavoidable when you're talking about what I think to be essentially made up terms.
It is odd that your comment was “ideological battle” but not the original article. I guess the original was sufficiently jargon-laden.
It's talking specifically about racial discrimination that results from <long list of interrelated things>. I don't think that means "any kind of error" and I don't think white people are racially discriminated against for having higher meth usage.
You really misunderstood me. I didn’t claim anything about white people; I gave a hypothetical scenario. And that hypothetical scenario wasn’t about white people facing discrimination for higher meth usage, only that such an inequitable distribution of meth usage seems to satisfy TFA’s definition for structural racism. The fact that it seems implausible to say so suggests the definition is not especially useful.
There are some very real effects that need to be acknowledged in order to design a good system. Here's a hypothetical example I gave in a similar thread here a few months back. It's loosely based (Though not too far removed from reality!) on some real-world systems that failed to address this:
Suppose you are designing a facial recognition system for police to use in the field while investigating a recent crime to see if anyone with a criminal history is nearby.
(Data taken from: https://en.wikipedia.org/wiki/Incarceration_in_the_United_St...) Because blacks are over-represented in the US criminal justice system (40% of the prison population vs 13% of the population) and because part of what defines "black" is the outward appearance of certain facial features, a facial-recognition algorithm which is trained to recognize criminals, with a cost function based on prediction accuracy alone, and facial features as input parameters is likely going to have false positives that over-represent blacks.
It's very important to consider this when you develop a training set. The developer error (who mostly failed to understand Baye's theorem here) might work something like this: They take 100 innocent people's faces at random. (On average it will have only 13 blacks) Then take 100 random criminal faces from inmates. (On average it will have 40 blacks.)
Then mix up the groups into your training set and assign a prediction score 1 or 0 depending on whether or not your classifier has correctly predicted whether or not a face was in the criminal group. Then, based on no other feature than race, your neural net can get better performance based solely on guessing more often that black people are criminals. That's not a good thing. In fact, if it's looking at a black face from its training set, the odds are nearly 2 to 1 that it's one of the criminals, even though the odds that are at least 2 to 1 against a random black person having a criminal history.
The likelihood of being falsely identified as having a criminal history is much greater based on the only variable of being black. And this type of thing has happened several times already in production systems!
Conversely, the same system, trained on the same data in the same training set, can get higher performance than random by simply guessing that any non-hispanic white person does NOT have a criminal history.
Thus, it's pretty important to correct your training set to reflect the correct Bayesian prior, and the underlying structures that sometimes go by the label "structural racism" or "institutional racism" are essentially exactly that reality in this case.
Suppose you are designing a facial recognition system for police to use in the field while investigating a recent crime to see if anyone with a criminal history is nearby.
(Data taken from: https://en.wikipedia.org/wiki/Incarceration_in_the_United_St...) Because blacks are over-represented in the US criminal justice system (40% of the prison population vs 13% of the population) and because part of what defines "black" is the outward appearance of certain facial features, a facial-recognition algorithm which is trained to recognize criminals, with a cost function based on prediction accuracy alone, and facial features as input parameters is likely going to have false positives that over-represent blacks.
It's very important to consider this when you develop a training set. The developer error (who mostly failed to understand Baye's theorem here) might work something like this: They take 100 innocent people's faces at random. (On average it will have only 13 blacks) Then take 100 random criminal faces from inmates. (On average it will have 40 blacks.)
Then mix up the groups into your training set and assign a prediction score 1 or 0 depending on whether or not your classifier has correctly predicted whether or not a face was in the criminal group. Then, based on no other feature than race, your neural net can get better performance based solely on guessing more often that black people are criminals. That's not a good thing. In fact, if it's looking at a black face from its training set, the odds are nearly 2 to 1 that it's one of the criminals, even though the odds that are at least 2 to 1 against a random black person having a criminal history.
The likelihood of being falsely identified as having a criminal history is much greater based on the only variable of being black. And this type of thing has happened several times already in production systems!
Conversely, the same system, trained on the same data in the same training set, can get higher performance than random by simply guessing that any non-hispanic white person does NOT have a criminal history.
Thus, it's pretty important to correct your training set to reflect the correct Bayesian prior, and the underlying structures that sometimes go by the label "structural racism" or "institutional racism" are essentially exactly that reality in this case.
This is a really good illustration of why it matters to be aware of what your sample sets are, especially in the case of using machine learning / automation in regards to things that have a very high likelihood of impacting human rights.
To be honest, I don't think our understanding of these systems is mature enough for us to be just throwing them out into our societal systems right now. There needs to be a lot more testing because the possibility of unethical results is pretty damn high.
That said, I still think that the concept of structural racism is a bad way to look at this problem, as it's simply one form of a common error when looking at sample sets.
To be honest, I don't think our understanding of these systems is mature enough for us to be just throwing them out into our societal systems right now. There needs to be a lot more testing because the possibility of unethical results is pretty damn high.
That said, I still think that the concept of structural racism is a bad way to look at this problem, as it's simply one form of a common error when looking at sample sets.
I’m not challenging the importance of minimizing those errors, especially with respect to healthcare. I’m challenging how meaningful or useful such a broad, vague, overloaded, and inflammatory definition can be, if indeed I’m understanding the definition properly.
Take something like a bank loan. If you had a model at a bank which took credit score, income, wealth, and collateral into account, black Americans would have loans rejected at a higher rate than white Americans. Is this model racist? No, this model doesn't even know what race is, all it knows is credit scores, income, wealth, and collateral. Does the fact that black Americans used to be slaves in the US, or were kept out of certain housing markets, contribute towards the fact that black Americans, on average, have lower credit scores, income, wealth, and collateral? Of course. But is this model racist? Literally not at all. It is completely unbiased, and exactly what the model should be. If the case you're making is that you think that there should be a national effort to correct for historical injustices that were done by the state by actively discriminating by race, that is a completely different discussion.
Having all of our decision-making apparatuses factor in the infinite pile of historical injustices that may have contributed to an individual's particular circumstances is not the way to go. Keep models simple and limited to what is relevant for that particular criteria. Fix injustices further upstream, or you make the whole system a convoluted nightmare.