reading this book about mis-use of maths/stats recently, i think we can go further in condemning the inappropriate approach taken in some justice systems to decide whether a guilty person receives a custodial sentence or not.
The purpose of locking someone up (and other stronger sentences) is complex - it can be to act as a disincentive to others; it can be to protect the public from that person re-offending; it could be a form of societal revenge; and it might (rarely) be an opportunity to re-habilitate the offender.
So we have a Bayesian belief system in action, and we have a feedback loop. But we better be really careful about i) the sample of inputs to the system and ii) the sample of outputs....and not forget these are humans, and capable of relatively complex and highly adaptive behaviours.
So what could be wrong with the input? (sigh, where to start) -
people who commit crimes are drawn from a subset of society, but people who are caught are drawn from a biased subset - firstly, they're probably less well educated, or dumber, or both, because they get caught. secondly, they're probably from a socially disadvantaged group (racial minority).
people who are found guilty are also the subject of selection bias (and people who get away with it, are party to survivor bias too) - juries have re-enforced the bias in the chance they are caught.
people who are sentenced acquire new criminal skills - this may make them less likely to get caught if they are just poor, but more likely if they are dumb.
So in there' I count at least 4 ways that a decision system that looked at re-offending rates, and properties of the person found guilty, would be building in positive feedback that will lead to more and more people being incarcerated, with less and less justification.
occasionally, external changes (accidental natural experiments) perturb the system and make this more obvious - in the film documentary, the House I live in , the absurd war on drugs is shown to be massively counter-effective - near the end, the huge bias that this has set against african americans starts to wane, simply because of the move in the poor white working class of america into making and consumption of crystal meth (so brilliantly portrayed in Breaking Bad - suddenly, the odds stacked against on group, multiplied by re-enforced prejudice 3 or 4 times over (indeed, one more time for the 3 strikes rule), hit lots of "trailer trash"....
An interesting research task would be to run a model inference tool on the data and see how many latent causes of bias we can find - maybe my 3,4 or 5 is not enough.
truly the world is broken, when it comes to evidence based decision making!
The purpose of locking someone up (and other stronger sentences) is complex - it can be to act as a disincentive to others; it can be to protect the public from that person re-offending; it could be a form of societal revenge; and it might (rarely) be an opportunity to re-habilitate the offender.
So we have a Bayesian belief system in action, and we have a feedback loop. But we better be really careful about i) the sample of inputs to the system and ii) the sample of outputs....and not forget these are humans, and capable of relatively complex and highly adaptive behaviours.
So what could be wrong with the input? (sigh, where to start) -
people who commit crimes are drawn from a subset of society, but people who are caught are drawn from a biased subset - firstly, they're probably less well educated, or dumber, or both, because they get caught. secondly, they're probably from a socially disadvantaged group (racial minority).
people who are found guilty are also the subject of selection bias (and people who get away with it, are party to survivor bias too) - juries have re-enforced the bias in the chance they are caught.
people who are sentenced acquire new criminal skills - this may make them less likely to get caught if they are just poor, but more likely if they are dumb.
So in there' I count at least 4 ways that a decision system that looked at re-offending rates, and properties of the person found guilty, would be building in positive feedback that will lead to more and more people being incarcerated, with less and less justification.
occasionally, external changes (accidental natural experiments) perturb the system and make this more obvious - in the film documentary, the House I live in , the absurd war on drugs is shown to be massively counter-effective - near the end, the huge bias that this has set against african americans starts to wane, simply because of the move in the poor white working class of america into making and consumption of crystal meth (so brilliantly portrayed in Breaking Bad - suddenly, the odds stacked against on group, multiplied by re-enforced prejudice 3 or 4 times over (indeed, one more time for the 3 strikes rule), hit lots of "trailer trash"....
An interesting research task would be to run a model inference tool on the data and see how many latent causes of bias we can find - maybe my 3,4 or 5 is not enough.
truly the world is broken, when it comes to evidence based decision making!
