In this case, the increased crime in these communities are not caused by their minority status, but rather from a multitude of other factors with historical and societal origins.

I'm not sure why causation vs. correlation is relevant here. When, say, someone is up for parole the judge will review their past criminal history, conduct in prison, whether or not they will have a support structure when they are released, etc. None of these factors are causal. A judge cannot point to a past crime, or to conduct in prison and say "this factor will cause you to reoffend". No, the decision is made based on factors that are correlated with higher rates of recidivism.

White Americans = 70% of pop. , $100 trillion in wealth.

African Americans = 14% of the pop, $2.9 trillion in wealth

African Americans in particular owned almost 10-12% of the land in this country (true wealth) and were promised more from the government in reparations (40 acres) not long ago, but discriminatory policies stripped that land away from them over 100 years.

Being in the USA for 150-200 years results in atleast 500k-1million $ in wealth purely due to land and home value appreciation.

The average black person has about ~500$ in wealth. This isn't a fluke, this was designed... This is a country that criminalizes being poor more and more in many ways, so we can fall into the trap of revictimizing the underclass we created, this time using algos, if we are not careful.

When a judge is evaluating whether or not to grant parole, is he or she looking for a causal factor that will directly cause the convict to re-offend or is the judge evaluating the convict's situation with factors that are correlated with re-offending?

Showing causation with whole-group statistics is very, very hard.

The majority of the $500k-1m baseline in white middle-upper class wealth is in homes and land handed down as inheritances, this is not debatable. That property allows a certain amount of leverage to invest in education, businesses etc...

The ben & jerry's founder talked about this in vivid detail on the campaign trail with Bernie Sanders. If he was black where he grew up, no GI bill = no cheap housing = no appreciation of property/land over his childhood = no financial leverage to build his company.

I can take risks and fail without going bankrupt thanks to familial wealth, that is a tremendous luxury not afforded to the group I'm mentioning.

(And in any case, my point was just that it wasn't shown as causation in the stats you cited)

You are correct on that point. More than 50% of the 100$ trillion is held by the top 10%.

On the other point, my point is the first $500k to 1million of wealth was due to inheritance... not the total wealth of the 1%.

Follow up: At one point, any white male could move westward and get free land (whether indegeous owned or not). 40-100 acres after a century+ of property value appreciation is quite a bit of wealth. The $500k-1m number I use as a baseline for white wealth is very conservative.

This reminds me of the argument for the UBI. Instead of having thousands of tiny chairty programs sprinkled all over society, why not let the economy make itself as efficient as possible and then hand out the charity in units of dollars?

More specifically, the current breed of machine learning is correlation engine, the only strength these networks have is finding correlations in the absence of context or explanation.

Sure... and what about the chronic lead exposure issues in many of these areas, the effects of long-term overpolicing of minor crimes on family stability, and the impact of (lack of) generational wealth and education from being used as de facto or literal slaves for generations?

The 'minority' part here is a correlative factor, not a causative one.

Honestly, my preferred method of solving this would be to train the algorithm on a data set with all of the forbidden values included along with anything else the creator feels relevant - zip code, income, familial status, favorite sport, education - and then when running in production, against real people, don't give it the restricted information. Yes, you could theoretically extract race, gender and other protected stats from the information the algorithm actually uses in prod - but it has no incentive to, since a less-noisy signal is already provided.

For instance, suppose the optimal algorithm for your data set is some linear function of X, Y and Z - let's say X+Y+Z to keep things simple. X,Y and Z are all normally distributed variables, mean of 0 and the same standard deviation. Y has a 0.5 correlation with X, and a -0.5 correlation with Z. If not provided Y, your algorithm might come up with 1.5X+0.5Z as an approximation - extracting a bit of the signal for Y from the things it does have access to. It's suboptimal, but better than just X+Z. Unfortunately, Y is verboten - we're not allowed to discriminate on it, and this approximation ends up with results that track Y. So instead we train with X, Y and Z as inputs, so the derived model is X+Y+Z - and we can drop Y from that model in production, leading to a model that (while less accurate) shouldn't unfairly track Y.

The problem is that the output of those algorithms is used to drive decision making that has the effect of maintaining the status quo, by removing opportunities to change it.

I think your comment shows that you understand this. You accept that a decision may be correct, when measured in totally cold and statistical terms. But such decisions would not "change the status quo" and that would be a problem.

But that position is a deeply political one. Why should decisions at banks, tech firms, or wherever be deliberately biased to change the status quo? It's social engineering, a field with a long and terrible track record of catastrophic failure. Failure both to actually change reality, and failure in terms of the resulting human cost.

Injecting bias into otherwise unbiased decisions by manipulating ML models, or by manipulating people (threatening them if they don't toe the line), is never a good thing.

The whole point of using the algorithm was to make sure personal biases aren't impacting the decision. If we're going to alter the algorithm because we don't like the result, then why are we bothering to use an algorithm in the first place? Just use a human to make the decision. At least in that scenario potential biases have an identifiable source, as opposed to an opaque program that may have been made by engineers that deliberately tuned it to avoid any disparities because they think any disparity in outcome is fundamentally problematic

Is the goal of the AI model to predict crime rates in a hypothetical world where everyone has equal rates of lead exposure? Or is the goal of the model to predict crime rates in the real world?

> Is the goal of the AI model to predict crime rates in a hypothetical world where everyone has equal rates of lead exposure? Or is the goal of the model to predict crime rates in the real world?

The goal is to use the results of a model for something (pet peeve, I hate the use of "AI" to describe what are usually pretty standard statistical or ML models). The model you create, and how you apply/interpret it, depend entirely on what you're actually trying to accomplish or change with the results.

Depending on what that is, the kind of "bias reflection" we're discussing is hugely problematic.

For example, crime rates are not equal between men and women. If we force our AI to assign equal risk of crime to men and women then we will have introduced a bias that either under predicts the rate of male crime, or over prdicts the rate of female crime.

What matters is: What are the outcomes and consequences of active systems, AI or not.

For instance: How do the algo cope with derivatives of its own output being fed into itself as input at a later stage?

The reality is, truth is relevant and sometimes the truth is inconvenient. Tech workers may want to build an AI that measures risk of recidivism that produces uniform risks across race and gender. But the truth is, rates of recidivism is not the same across all groups. If we produce the desired outcome of equal reporting of risk, then the consequence is that men have their risk underreported to put them on parity with women, or vice versa.

The justice system should absolutely not use this system for any purposes, since justice is based on the circumstances of the individual case in front of it, not the societal statistics which apply in the aggregate but may not apply to that specific case.

And if an activist engineer deliberately biased the model to avoid indicating disparities in crime, then we will have sabotaged police's abilities to allocated resources. Hence, why this assumption that disparate outcomes are indicative of a biased model is a problem

We already know where crime is occurring. We don't need an AI model for that.

People aren't arguing about not using biased data, they're arguing that the model needs to be designed and trained so that the bias in the data doesn't affect the predictions in the model. And yes, that means deliberately de-engineering bias out of the model, which may involve introducing a counter-bias.

For example, you and others kept bringing up race earlier as a legitimate bias for criminal profiling. But socioeconomic status is far more correlated with propensity to commit criminal acts than race. A model of crime in LA based on race, for example, would assume that people in Ladera Heights are just as likely to commit crime as people in South LA because they have the same race...but Ladera Heights has a fraction of the crime as South LA (and several times the average income). Similarly, you would expect South LA to have less crime than the largely Caucasian Joshua Tree or Fontana...but both cities have higher crime rates than South LA, and for a period were some of the most dangerous cities in California. (Joshua Tree was the inspiration for, and original setting of, Breaking Bad. Fontana used to be known as the Felony Flats.)

What I am saying is that pointing to disparities in the outcome of these models to claim that the models are biased is not, on it's own, a reasonable conclusion. As you point out, people in Joshua Tree and South LA have higher rates of crime than average. So if our model flags people from these areas as high risk more frequently than other places, is our algorithm biased? If we deliberately make the model to produce uniform results across different locations because an engineer feels that it's problematic to have a model that produces different results between different geographic cohorts, then have we mitigated bias? No, that engineer intentionally introduced our own bias to make the model adhere to his or her worldview.

[1] See the 1999 documentary Blast from the Past.

But I think we're diverging considerably from the original point: that forcing an AI to produce equal outcomes despite unequal behavior in the real world is not the elimination of bias, it's the deliberate introduction of bias. If we have an AI that predicts recidivism rates, and we engineer it to produce equal predicted rates across all groups despite different between rates groups in the real world then we are deliberate introducing bias. The truth, regrettable thought may be, is that a magical AI that operates with 100% accuracy - the only people who it flags would have re-offended - is going to produce disparities because recidivism rates are not equal.

The blindspots end up being extremely problematic.

Statisticians refer to this process as "controlling for confounding factors." What really matters is what questions you're asking. Data is too often abused, not always intentionally, by people with vague questions.

If you're going to use a model trained on simple, biased data, to get, say, insurance estimates for a for-profit company, the model will probably successfully increase profits, so it was a good model.

On the other hand, if you're going to use the same model to help with sentencing, where your goal is to see equality and justice, then the model will do very badly, since it will punish many people for the community/skin they happened to be born in.

For instance, men commit more crimes than women. If we are building an AI that predicts risk of committing crime (say, estimating rates of recidivism) and we forcibly make it report equal rates between men and women then we will be creating a discriminatory system because it will either under report the risk of men or over report the risk of women in order to achieve parity. Engineering parity of outcome in the model when the real world outcomes have disparities necessarily results in bias.

What a terrible example. There are many other features being omitted that would predict crime rates. If anything, this is an example of enforcing your own bias on the model by not including all relevant features.

Problems like violence have multiple causes, but are widely understood to be linked to problems of poverty, inequality, and marginalization. Violence is also self-perpetuating through social networks. When an incautious user of statistical tools fails to investigate the causal story behind such findings, they're going to get the wrong answer. When a policymaker acts on incomplete or misleading findings, they're going to make the problem worse.

*Unless you're arguing that it's the fact of being non-white which makes people more violent, in which case I have nothing to say to you.

This is not how I interpreted OPs comment at all. I just read it as a note that data shows correlation between minority communities and crime. Minority is a flexible, relative term, to my understanding - which, notably in recent years is frequently attributed to "non-white", but over time has been attributed to many different groups of people (caucasian as well).

Interpreting it as meaning "non-white" in this context is yet another good example of our predisposed biases and relativistic understandings at play, I suppose.

Not to harp on you - just noting the differences in our perceptions.

At the end of the day, there's enough real, vile bigotry out there in our society, I think it's important to be extra clear when discussing topics like this.

I agree, there is are many negative biases in the real world. More so, I'd argue, on the internet, where people feel safer at a distance to state disagreeable opinions.

It's worth noting, especially when we are discussing the nature of how an AI learns.