The accuracy, fairness, and limits of predicting recidivism
advances.sciencemag.org
advances.sciencemag.org
This assumes the data sets of the criminal punishment system are not inherently biased, which is of course a false assumption.
All this does is make the runaway train of the US criminal punishment system a more efficient machine.
It doesn't even require that assumption to go awry.
Assume there exists a stereotype, say "people with freckles are more likely to be criminals". It doesn't actually matter if the stereotype has any basis in reality, just that it is widely believed.
People will, on the margin, be less likely to hire freckled people. This reduces the legitimate employment opportunities available to a freckled individual, which tends to make the illegitimate opportunities more attractive by comparison. So the stereotype becomes a self-fulfilling prophecy: by assuming that freckled people are more likely to commit crimes, society actually causes freckled people to be more likely to commit crimes.
An expert system will notice this and begin using "has freckles" as a weighting factor in predicting recidivism. It's important to note that the expert system is not wrong. Freckles are in fact, at this point, statistically correlated to recidivism. But the expert system can't know why the correlation exists. All it can do is tighten the vicious feedback loop, noticing a statistical correlation that strengthens the existing stereotype, which exacerbates the real world impacts, which increases the statistical correlation, which strengthens the stereotype, and so on.
>it is argued that the COMPAS score is not biased against blacks because the likelihood of recidivism among high-risk offenders is the same regardless of race (predictive parity), it can discriminate between recidivists and nonrecidivists equally well for white and black defendants as measured with the area under the curve of the receiver operating characteristic, AUC-ROC (accuracy equity), and the likelihood of recidivism for any given score is the same regardless of race (calibration)
Simpon's paradox [0], probably one of the most insidious problems of well intentioned statistics.
More importantly, the bucketing involved is reasonable and has explanatory power. (If this were not the case, you might have picked the buckets specifically to get this result. Still an example of Simpsons Paradox, but not interesting.)
The most obvious criticism is that the uninspiring performance of the COMPAS model does not provide any evidence of the weakness of automated systems in general, which is surely correct. This kind of argument would not, however, address more general concerns about how and why these kinds of systems are adopted - why is this system being used when its performance is not obviously competitive? (The answer to that is presumably "corruption", in a generalised sense, but that is hardly an adequate response.)
Even more broadly, as the complexity of human society increases, it seems likely that the difficulty of making constructive interventions will also increase, and possibly more rapidly ("The Collapse of Complex Societies" by Joseph Tainter investigates this hypothesis). It does seem risky to just plough on, hoping that we can invent our way out of any problems that might arise as a result of what we are doing.
In other words, do judges perform better than COMPAS? And slightly less important: is the variance lower?
If it isn't, then there is no point arguing whether COMPAS is risky: it would be less so than the alternative.
Although I suppose that the 'crowd of non-experts' is essentially what a jury is and they had about the same performance.
At any rate, it sounds like much less of a hassle to input characteristics into COMPAS and get a probability than getting a jury together.
Besides, I think I'd be much more worried that a jury could influence each other and bias its decision than about am algorithm making unfair predictions.
It also performs as well as their simpler model. But they only evaluated the simple model on data from a single county. There's not enough evidence to determine whether that model would perform as well on other data sets. I would presume, perhaps wrongly, that more work has been done to ensure the generalizability of COMPAS, the results of which work militated against the adoption of a simpler algorithm. Possibly because the algorithm has to avoid racial disparities that might creep up in counties with a different demographic mix. Even if the simpler model performs equally well across many jurisdictions, that merely suggests that a simpler non-proprietary algorithm would be better. But we're still choosing between algorithms, not discrediting the idea of algorithmic sentencing altogether.
As an aside, do you have a source for the cost comparison, because I didn't see anything in the paper?
And as an initial matter, I would point out that there is no judicial procedure which relies on algorithmic sentencing or bail decisions without a judge reviewing the decision. COMPAS is merely used to produce a recommendation for a bail amount which a judge then has to review and approve. Defendants are still afforded a hearing where they can object and raise any extenuating circumstances. So to the extent that judicial discretion can address systemic biases, it already does.
And I certainly recognize the value of judicial discretion. Mandatory minimums alone demonstrate how prioritizing the punitive and retributive goals of the judicial system over simple human mercy can amplify the negative effects of racial and other social biases.
However, my experience has been that the legal field has a strong systemic bias against empirical or statistical techniques. Essentially anything that involves math. There's a running, tired joke at every law school that students pursue JDs because they couldn't get a good score on the math GREs (the LSATs in contrast involve no math).
And to clarify since I am at risk of confusing what we mean by the term bias: I am not denying that the legal profession is susceptible to the social biases you mention, but rather I would argue those biases are impossible to address until the deeper, methodological biases in how the legal profession pursues objective truth are addressed.
Algorithmic sentencing and bail setting software can assist there in two ways. First, it ensures the collection of extensive, objective, and standardized data that can be used to evaluate the potential systemic biases you are concerned about. A single, consolidated, structured database with enormous amounts of information about each individual defendant is a gold mine for researchers. Simply put, we cannot even begin to resolve our biases without first identifying them. Software like COMPAS can help (though I'm not sure if it does in this case because I don't think they provide public access to the data).
Second, the introduction of this software gives lawyers a chance to get used to machine learning algorithms and other advanced statistical tools. They're powerful techniques and need to become a larger part of the standard lawyer's tool kit.
I don't think COMPAS is the terminal destination for sentencing software. In fact, I really hope otherwise. But, as a profession, I think we need to start taking steps to modernize our practices, one little step at a time. Each individual step may be imperfect at first, but so long as we maintain judicial discretion and careful oversight, these tools will be a boon for American justice.
And in response to your last question, I don't have a citation on hand. I've read it somewhere before. Basically they replace a professional clerk or low level prosecutor in the DA's office who prepares a bunch of data and biographical information for the bail recommendation to the judge. It ends up being about one FTE a year and the software costs less than that to license.
You seem to appreciate the problems with potential biases, and your point about data collection is well taken, though I would note it doesn't require application of algorithmic sentencing. I would, however, urge you to continue to think about the difficulty of automating the sentencing decisions made by a human, from a mathematical perspective - the problem amounts to performing factor analysis by simply throwing away a large number of factors that are difficult to measure. It is not clear to me that that method will be able to capture those difficult parts of the distribution that people intuitively refer to as justice.
I also urge you to continue to think about the difficulty of optimising that automated sentencing procedure, which requires you to select parameters to optimise for. This problem faces similar difficulties - whereas a human might update their judgment on a wide range of observations (potentially including everything they experience), for an automated procedure we typically have to choose a handful of parameters based on some expert intuition about what is important. The choice of those parameters can be critical, and can expose feedbacks that would be naturally corrected by humans.
The previous two points are often dismissed as merely being difficult challenges, rather than fundamental problems with the techniques, and, whilst I am inclined to agree with that perspective, they are difficult challenges, and the decisions that such systems end up making can (and often do, in my experience) have consequences that weren't expected.
This leads to my political reason for being cautious about these systems, which is motivated precisely by the fact that they can be cheaper, at least when one is only making a comparison based on decisions per dollar, or something along those lines. I am happy for these systems to be used by informed practitioners as a mechanical aid to their decision making, but cost savings are such an evergreen issue that I fear pressures to dispense with the human and "make do" with the automated system will become too strong to bear (for the politicians, not the lawyers).
Ultimately one can imagine a system that is constantly improved over time (an expensive prospect) could result in more objective sentencing - my concerns are that uncaring politicians will exploit the availability of the techniques to cut costs, and that once those costs are cut it will be difficult to justify the (relatively) increased expenditures necessary to fund improvements. I don't have your insider's perspective, but I suppose overall I struggle to accept that saving costs in this way is one of the most necessary reforms to the American justice system.
I find it weird that an algorithm doing better or same than humans is called 'uninspiring'. Maybe people have too high expectations of what AI can do ?
As it happens, however, I agree with you - people do have unrealistic expectations of what can be achieved with AI. Replacing humans is, in many domains, a more difficult prospect than might be imagined, because so much of what we do is implicit. Suggesting that a particular problem is "solved" because a system achieves non-significant parity on a handful of measures is short-sighted to say the least.