The Justice Algorithm

Why do we need judges? Why do we leave important decisions to flawed, biased, distracted, even corruptible human beings? Couldn’t A.I. do this job so much better now? So much more cleanly, precisely – without all that messy, human subjectivity? Couldn’t we just submit the evidence to a great big algorithm to determine guilt or innocence? Or, if that involves too much ambiguity, at least to determine appropriate sentencing, taking all appropriate factors into consideration? Shouldn’t there be a single right answer that can be reached in each case?


 

After all, we have, in most jurisdictions, a constitution that lays out our moral and societal values and guiding legal principles. Of course, these aren’t specific to any situation, so we also have a set of laws that dictate very clearly what’s allowed and what isn’t. Admittedly, we keep having to make new ones to keep up with a changing world, but they’re as comprehensive and up-to-date as our political systems allow. We know what kinds of things are crimes, and if someone’s done one, they’ve done one.

 

Now, it’s true that the laws can’t cover every particular instance that would constitute each type of crime, but that’s where we can lean on decades and decades of legal precedent. We’ve seen it all at this stage – we’ve been meting out justice for centuries, after all!

 

All we need is the constitution, our sets of laws, and the body of legal precedent to parameterise our algorithm. Then we can just input the facts of the case and the machine should churn out the correct verdict and sentence. I suppose there might be some disagreement on what constitutes a “fact”. But we could handle that by attaching a certainty parameter to each one. Shouldn’t be a problem.

 

Admittedly, the considerations for sentencing get a bit complex. There are obviously a lot of factors to take into account, when thinking about what sentence someone deserves. The good news, though, is that body of legal precedent is so extensive that it’s very unlikely we’ll encounter a new factor we haven’t seen before. Once we have the weights for each factor included in our algorithm, we should be all set!

 

I mean, maybe there would be some scenarios where the weight you put on one factor would depend on the context of some other factors. But that shouldn’t be a problem either. We’d just need to include some second-order weights to tweak the first-order ones based on those kinds of contextual factors. And, I guess, maybe some third-order ones. But, honestly, how many combinations could there be? With all of legal history at our disposal, we should be able to match all possible contingencies, right? Shouldn’t we? Our program might get a little unwieldy, admittedly, but it’s not like we don’t have the spare compute and energy capacity. And I guess it might take quite a while to churn through all the necessary computations, but, hey, who’s in a rush?

 

And if an algorithmic program – some set of computational steps that we run through in series – can’t handle the complexity, maybe we could turn to a machine learning model that we could just train on loads of past cases. We’d just need to define the right information to give it and then let it figure out the weights for itself. It’d just be a great big exercise in pattern recognition. Again, once beyond some level of training, the past should be a certain guide for all future cases.

 

The important thing is that this process should be deterministic – no messy subjectivity or variability! Just clean computation. Each set of facts (weighted by certainty) and relevant circumstances (weighted by context, in ways that are themselves weighed by context) will reliably produce a single outcome.

 

That obviously doesn’t mean that there will be some kind of infinite look-up table containing a verdict and sentence for every conceivable combination of facts and circumstances, of course – that would be absurd! It’s just that our conditioning on the past would be so comprehensive that the result of the computations for any new scenario would be effectively fixed in advance. You’d just have to run the algorithm to find out what it is. The answer would already sort of “exist”, in potentia, as the philosophers say. The Algorithm will have spoken. By preprogramming or pretraining our machine, we should be able to fit any new scenario and know exactly what the right answer is! Huzzah!

 

 

 

 

I’m hoping by now that most readers will have detected just the subtlest note of sarcasm in the preceding arguments. And some of you may have figured out that I’m using the specific case of the legal system as an allegory for cognition more generally. (Though we’ll return to the Justice Algorithm itself at the end).

 

What prompted this analogy is a recent debate with Robert Sapolsky, in which he argued (based on his book “Determined”) that we have no free will in any moment – that our behavior is always, well, determined. The idea is that our brains and minds are shaped by all kinds of prior causes, from millions of years of evolution, to our own genetics and brain development, to all the experiences that have happened to us over our lifetimes.

 

All of those factors have been shown individually to have an influence on our patterns of behavior. No one disputes that. But Sapolsky makes the much stronger claim (without evidence, frankly) that the collective effect of all those prior causes doesn’t just influence but completely determines our behavior. Not just our patterns of behavior – our actual behavior in every specific situation we ever find ourselves in across our whole lives.

 

The question is: where does that leave you? What is there for you to do in this process? Under this view, your brain just constitutes a great big preconfigured algorithmic structure that is going to churn through its inputs (based on the available information about the world outside and your current states and motivations) and spit out a single, definitive answer, no matter how novel the situation. The equations will always have a unique solution.

 

To spell out the analogy, evolution would be loosely equivalent to the constitution, and, together with your own genetics and brain development, could effectively give you some general predispositions and behavioural principles. These would, however, not be very specific to any given situation. As described in my book Innate, they would just be a set of basic tunings of decision-making parameters (like risk aversion or reward sensitivity or novelty salience – that kind of thing). Those tunings don’t have any “content” – they’re not about anything in particular. Your individual history would be like the more specific laws and the huge body of legal precedent – everything that you have learned from the past that can be used to guide your behaviour in the future.

 

The problems with this view of deterministic decision-making are, I hope, obvious from the analogy. We can’t just run any set of new information through a totally predefined cognitive algorithm, because the parameters of the algorithm are always massively context-dependent. And we can’t prestate all the relevant first- and second- and third-order weights because the space of possible combinations across all scenarios we might encounter is effectively infinite and unknowable in advance. This kind of combinatorial explosion makes the problem computationally intractable.

 

That’s not to say that a lot of our behaviour isn’t fairly automatic. In many familiar or simple scenarios, we know what to do based on a simple set of habits and heuristics. No thinking required. But once things get more novel and more complex, we can’t just submit the information to a preconfigured algorithm. We have to figure out how the algorithm should be configured. We have to work out how to weigh up all the various factors, on the fly, in real time. In other words, we have to do exactly what judges do – make a judgment!

 

Not just in real time, either – we have to do it in good time. And we don’t have infinite compute or unlimited energy – we just have our limited brain, selected for efficiency, not precision. Indeed, in many cases, it’s not like there actually is a right answer, waiting to be found. Given the limited information that an individual has at any moment, and all the potentially conflicting goals over which they are trying to optimise, simultaneously, there will often be a range of possible actions with indistinguishable predicted utility. This is the well-known principle of “bounded rationality”. We can’t always reach a definitive solution – sometimes we just have to pick one without knowing in advance if it will prove to have been the best choice. We’re not omniscient and we’re not clairvoyant. 


To navigate through a world that is constantly throwing up novel scenarios, we need to do work to judge the relative salience of different factors, relative to our suite of current goals. We need to infer not just what is out in the world, but which elements matter to us – what we should care about and pay most attention to and weight most heavily, given our current state, our ongoing projects, whatever behavioural agendas we are pursuing, and so on. In short, we need to make decision-making computationally tractable. John Vervaeke and various colleagues have called this process “relevance realisation” and have presented some biologically plausible ways in which it can be achieved, allowing organisms to make their way pragmatically in an open-ended world.

 

This will inevitably involve lots of heuristics, rough estimates, and other means of satisficing – that is, trying to satisfy the myriad demands on behaviour, based on all kinds of competing considerations, in a reasonable amount of time with limited resources. We don’t even try to reach a right answer, because there isn’t one – we just have to reach one that is good enough. 

 

There is thus no reason to think that all these cognitive processes will be deterministic, or even algorithmic, in the strict sense of that word (implying a predefined procedure following a set of discrete operations carried out in series, usually completely reproducibly). There are, in most situations, lots of things we could do and lots of ways we could do them. In fact, the only way that these cognitive-level processes could be deterministic is if the underlying neural and physical processes were completely deterministic. And we know that they’re not.

 

Brains are wet, messy, noisy places, full of tiny, jittery components. That inherent randomness of low-level goings on is both a challenge and an opportunity. It means, first of all, that the future evolution of the system is not fixed by the current microscopic state and the laws of physics – lots of things could happen. The challenge for the organism is to make happen what it wants to happen. But this low-level under-determination also provides an opportunity: to evolve control systems that can, by virtue of their macroscopic configuration, constrain how the system evolves.

 

In my book Free Agents, I lay out the case for this view and describe the evolutionary trajectory that led to ever-increasing agency along our own lineage. One important element of this framework is that the workings of the system are causally sensitive to the meanings of neural patterns, rather than the low-level details of their momentary instantiations.


If this view is on the right track – if the neural computations are causally sensitive to semantic content, rather than detailed syntax, and those semantics relate to organism-level concepts, and all that information is integrated in a hugely contextually interdependent way, and is used to direct behavior over nested timescales, in ways that cannot be either algorithmically or physically pre-specified, based on criteria configured into the circuits derived from learning, which embody reasons of the organism and not any of its parts, then I would say that *just is* the organism – as an integrated self with continuity through time – deciding what to do.

 

To me, this picture matches the phenomenology of making decisions under conflict much better than the idea that our brain just spits out an answer. We really have to think about things – it’s effortful, even exhausting sometimes. And that kind of deliberation is a process, extended in time – one that we actually have a window on. We can consider whether the reasons we’re operating with are good reasons, whether the information we have is trustworthy or consistent, even whether our motivations are the types of motivations we think we should have. (There’s much more on that final point in the book). The point is that these are not just processes happening within us – we ourselves, as selves, are very actively making judgments.

 

 

 

 

Now, I promised to return to the idea of the Justice Algorithm. I offered it as an argument in absurdum – it’s clearly such a simplistically reductive idea in the context of the legal system that it prompted the intuitions I wanted in relation to natural cognition. Welp, seems the joke’s on me! AI systems are already in use in many criminal justice systems, using individuals’ profiles on hundreds of metrics to make predictions on risks of flight, recidivism, violence, sexual offenses, and so on, which are then used to guide decisions on bail, sentencing, parole, admission to rehabilitation programs, and other important outcomes.

 

At present, those AI algorithms are not making any decisions by themselves – they’re just providing information that judges or other workers in the justice system can use to help guide decisions. There are, however, huge questions over whether that information is useful, and, more importantly, over whether its application is fair or actually codifies all kinds of biases. I’ll let the experts speak to those issues, in the articles listed below. In the meantime, I can only hope that we are not heading towards a future where “Computer says jail”.

 

 

 

A ‘black box’ AI system has been influencing criminal justice decisions for over two decades – it’s time to open it up

https://theconversation.com/a-black-box-ai-system-has-been-influencing-criminal-justice-decisions-for-over-two-decades-its-time-to-open-it-up-200594

 

AI is already being used in the legal system – we need to pay more attention to how we use it

https://theconversation.com/ai-is-already-being-used-in-the-legal-system-we-need-to-pay-more-attention-to-how-we-use-it-205441

 

AI is sending people to jail—and getting it wrong

https://www.technologyreview.com/2019/01/21/137783/algorithms-criminal-justice-ai/

 

Artificial intelligence (AI), data and criminal justice

https://www.fairtrials.org/campaigns/ai-algorithms-data/?gad_source=1&gclid=Cj0KCQjwsoe5BhDiARIsAOXVoUvuuEFhKSmGxzcM4eFAM-a34CxqOkxFiE674gM_guXNjHveRboBMBUaArwiEALw_wcB

 

 

 

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