Knowledge, belief, and recklessness in criminal law

Are the courts too willing to use an unsound definition of the mens rea requirement of knowledge?

A workable definition of knowledge is belief that is true. This has been used in cases and legal texts: for example, AP Simester and WJ Brookbanks, Principles of Criminal Law(4th ed, Brookers, Wellington, 2012) at [4.5]; David Ormerod, Smith and Hogan’s Criminal Law, (13th ed, Oxford University Press, Oxford, 2011) at [5.25]–[5.27], both cited in R v Banks [2014] NZHC 1244 at [39]. However, knowledge is more complex a concept than that, as discussed by Bertrand Russell in Chapter 13 of his The Problems of Philosophy (1912), which is freely available at http://www.gutenberg.org. A person does not really know something if the justification (that is, the reason) for the claimed knowledge, is false. Russell’s account of knowledge can be read with Edmund Gettier’s famous and brief 1963 paper “Is Justified True Belief Knowledge?”, available at http://fitelson.org/proseminar/gettier.pdf, which has generated a large body of entertaining discussion.

A true belief is not knowledge when it is deduced from a false belief. For example, the defendant may say, “I knew it was cocaine because it was some of what John gave me”, when in fact it was cocaine given to the defendant by someone else. The defendant believed that it was cocaine, on grounds that might have been strong if they were true. The reasonableness of the grounds depends on the likelihood of John having supplied the drug. If the grounds were weak, it would be more accurate to say that the defendant merely suspected that the drug was cocaine. This illustrates how a defendant may wrongly claim to have known something when it was only believed or suspected. True belief is also not knowledge if it is arrived at by fallacious reasoning. The defendant may claim to have known that a substance was cocaine because John supplies cocaine and John supplied this substance. The fallacy is that John may also supply other substances. Although the substance was indeed cocaine, the defendant’s belief would not normally be described as knowledge because it was a product of the mistaken assumption. The defendant had a belief, perhaps a belief on strong grounds if John nearly always supplied cocaine, but although the defendant thought it was knowledge, it was really just probable opinion. But if probable opinion is sufficiently probable to result in a firm belief, it can be correct to call it knowledge. This is different from the lesser probability required, for example, for the familiar “reasonable grounds to believe” needed in the search context, where an applicant for a warrant need only have the belief, not knowledge, that the relevant thing is at the place to be searched.

If a defendant admits in a police interview to having had knowledge of circumstances which were required to exist for there to be an actus reus, the police would be unlikely to challenge the defendant’s grounds for that claim of knowledge. But in the interval between interview and trial, when there would usually be some opportunity for philosophical reflection, a defendant may realise that the circumstances were more uncertain than they were thought to have been, and that the claim of knowledge was an exaggeration. This defendant may decide that what was perceived was a probability, not a certainty, and that the state of mind was suspicion rather than belief.

However, the law does tend to conflate the concepts of knowledge and belief. Whether it is correct to do so may be questioned. For example, see Kerr v R [2012] NZCA 121, which concerns knowing a purpose of future use of the relevant thing. The court, by interpreting knowledge as including belief (or, more accurately, as including belief that was false), in reality read in to s 12A(1) of the Misuse of Drugs Act 1975 the phrase “or believing”, referring to common law instances of knowledge being equated with belief (at [14]-[16]), the definition in the New Zealand Oxford Dictionary ([17]), and perceived difficulties with excluding belief ([18]-[19]). With respect, it should be noted that online Oxford dictionary definitions of  “know” do not refer to belief, and definitions of  “believe” do not refer to knowledge. Further, the Court’s difficulty with the concept of knowing a future event is beside the point, because what s 12A requires is knowledge of a present purpose (“is to be used”). The cases cited are from other contexts, but relevantly include where a substance was supplied to an undercover officer who had no intention of using it illicitly - raising the unaddressed issue of whether an investigatory technique should influence the definition of an offence - or receiving, where it was thought necessary to read in belief if knowledge was limited to what the defendant actually saw. This ignores the wider dictionary definition of know as to be aware of through observation, inquiry, or information. The real difficulty in Kerr was that Parliament had not anticipated that the police would use undercover officers to pretend to defendants that they were going to put items to illicit use, and the legislation should have specified "knowing or believing" in defining the mental elements of the offence.

Those matters aside, it may be that recklessness, which requires, as one of its elements, perception of a risk of the relevant criminality occurring if the defendant acts, is not amenable to the Russell - Gettier analysis. It is the perception, not the grounds for the perception, of the risk, that matters here. For the purposes of attributing criminal responsibility, where attention is on the harm threatened by the defendant, it is the perceived risk, not a known risk, that is a component of recklessness. The defendant’s perception of the probability of the relevant criminal outcome is sufficient, and if “knowledge” of the risk is referred to, it should be understood here in the loose sense of risk perception.

Take a coin, any coin

It is good to see an article on Bayesian reasoning with conditional probabilities in the current issue of the Times Literary Supplement: “Thomas Bayes and the crisis in science” by David Papineau (June 28, 2018).

As Professor Papineau points out, Bayesian analysis is used in many fields, including law.

One of the difficulties in discussing Bayesian reasoning, or indeed any complex subject, is that clear and simple points can become obscured by technical terms.

It took me a while to get to grips with Professor Papineau’s coin-tossing illustration. What it is designed to illustrate is an error of reasoning that is, apparently, found in too many published scientific studies. Essentially, the error involves drawing a conclusion from too little information. But Professor Papineau's illustration is of one test, not of an experiment involving a statistically significant number of test results.

If you take a coin – any coin – and toss it five times, and if you get five heads, how likely is it that the coin is biased? Pretend that you do not have special coin-tossing skills that allow you to determine the result of a toss. Also pretend that it doesn't occur to you to just keep tossing the coin to see what proportion of the sequences of five-tosses give results of five-heads.

After only a little reflection you realise that an unbiased coin will, on average, produce five-heads once every 32 times the five-toss sequence is carried out. One in 32 gives a probability of 0.03, approximately. The probability of getting five-heads from an unbiased coin looks very low, and you might be tempted to conclude that, therefore, there is a probability of 0.97 that the coin is biased.

Apparently, a significant number of scientific studies have been published in peer-reviewed journals, reporting conclusions arrived at through that sort of reasoning.

Bayesian analysis, if you are able to do it, will quickly show you that such conclusions are ridiculous, or, as Professor Papineau says, “silly” or “nonsense on stilts”.

If you are a lawyer, you might have to convince a judge or jury that an apparently obvious conclusion, reached by a respected expert, is wrong. It is far from easy to do this, and that may be why Bayesian analysis is taking so long to be routinely applied in courtrooms.

Fundamentally, the probability of getting five-heads if the coin is not biased, is not the same as the probability of the coin not being biased if it produced five-heads. The probability of A, given B, is not the same as the probability of B, given A.

My favourite way of illustrating this is to say: the probability of an animal having four legs, given that it is a sheep, is not the same as the probability of it being a sheep, given that it has four legs. The first tells you something about sheep, the second something about quadrupeds.

We know something about an unbiased coin: about three per cent of the times it is tossed five times it will produce a sequence of five-heads. But what do we know about a coin that has produced a five-head sequence? Is it biased or unbiased? If it is biased, how biased is it? Does it always produce five-heads or only some proportion of the times it is tossed five times? Is a biased coin commonly found or is it rare? Those things need to be known in calculating the probability that the tossed coin which produces a five-head sequence is biased.

At the risk of over-explaining this, let’s ignore - just for a moment - the rarity of biased coins and consider possible results of 100 five-toss sequences for a biased, and an unbiased, coin:

                                    Biased             Unbiased

            Five-heads       25                      3

             Other               75                     97

These results give three per cent of the results for the unbiased coin showing five-heads. The biased coin was, in this example, biased in such a way that it showed five-heads 25 per cent of the time and any other result 75 per cent of the time. So, of the five-heads results, three were from the unbiased coin and 25 from the biased coin, so the percentage of five-heads results that were from the biased coin is 25/28 times 100, or 89.3 per cent. Assuming you were equally likely to have tested either of the two coins, the probability, after one five-toss of the coin sequence, of the tossed coin being biased, given the five-head result, is approximately 0.89, which would not be regarded scientifically as significant proof of bias. Conventionally, for a significant conclusion that the coin was biased the conclusion could only be wrong no more than 5 per cent of the time.

This is not to say that the result is of no use. It does tend to prove the coin is biased. The strength of its tendency to prove bias is the likelihood ratio: the ratio of the probability of five-heads, given the coin is biased (from the above table this is 0.25) to the probability of five-heads, given the coin is unbiased (0.03), a ratio of 8.3 to 1. On the issue of bias, the result should be reported as: whatever the other evidence of bias may be, this result is 8.3 times more likely if the coin is biased than if it is not biased. The other evidence may be from a survey of coins which measured how often we can expect to find biased coins.

Now suppose that such a biased coin is only found once in every ten thousand coins, and that all biased coins have the same bias. The probability of a randomly chosen coin you have tossed and got a five-heads result being biased is, when you do the calculation using Bayes' formula, 0.0008. Eight occurrences in ten thousand. Much lower than the 0.97 probability (97 occurrences in 100) of the coin being biased that might have been reported in a peer-reviewed journal.

Again, this is not as surprising as it may seem at first glance. There may be only one biased coin in 10,000 coins, and one occurrence of five-heads from a biased coin in 40,000 coins (using the one-in-four frequency in the table), but, in round figures, there will also be 1200 occurrences (three per cent) of five-heads from unbiased coins in those 40,000 coins. This is why, on this occurrence of biased coins, a five-head result is much more likely (1200 times more likely) to be from an unbiased coin than from a biased one.

Only a very brave judge or juror would bet a significant sum that a coin which when tossed produced a five-head sequence was not biased. The bets would go the other way and those significant sums would most probably be lost.

And, as an afterthought: if you feel estimating prior probabilities is a bit haphazard, the Bayesian formula can be turned around to tell you what priors you would need in order to get in the above example P(the coin is biased) = 0.95. You would, before doing the experiment, need to be convinced to a probability of about 0.70 that the coin was biased. This sort of approach is discussed in a paper by David Colquhoun (available courtesy of The Royal Society Publishing). If, as a lawyer, you want an easy introduction to Bayesian reasoning, see my draft paper on propensity evidence.