Use and Abuse of the Precautionary Principle
ISIS submission to US Advisory Committee on
International Economic Policy (ACIEP) Biotech. Working Group, July 13, 2000
The precautionary principle is accepted as the basis of
the Cartegena Biosafety Protocol agreed in Montreal in January 2000,
already signed by 68 nations who attended the Convention on Biological
Diversity Conference in Nairobi in May, 2000. The principle is to be
applied to all GMOs whether used as food or as seeds for environmental
release.
The precautionary principle states that when there is
reasonable suspicion of harm, lack of scientific certainty or consensus
must not be used to postpone preventative action. There is indeed
sufficient direct and indirect scientific evidence to suggest that GMOs
are unsafe for use as food or for release into the environment. And that
is why more than 300 scientists from 38 countries are demanding a
moratorium on all releases of GMOs (World Scientists
Statement and Open Letter to All Governments).
The precautionary principle is actually part and parcel
of sound science. Science is an active knowledge system in which new
discoveries are made almost every day. Scientific evidence is always
incomplete and uncertain. The responsible use of scientific evidence,
therefore, is to set precaution. This is all the more important for
technologies, such as genetic engineering, which can neither be controlled
nor be recalled.
Dr. Peter Saunders, Professor of Applied Mathematics at
King's College London, co-Founder of ISIS, has written an article which
shows how the precautionary principle is just codified common sense that
people have accepted in courts of law and mathematicians have adopted in
the proper use of statistics. It begins to clarify how scientific evidence
is to be interpreted in a socially responsible way which is also in accord
with sound science.
Key Words: scientific evidence, burden of proof, statistics, p values, law
Use and Abuse of the Precautionary Principle
Peter T. Saunders, Mathematics Department, King's
College, London.
There has been a lot written and said about the
precautionary principle recently, much of it misleading. Some have stated
that if the principle were applied it would put an end to technological
advance. Others claim to be applying the principle when they are not. From
all the confusion, it is easy to mistake it for some deep philosophical
idea that is inordinately difficult to grasp (1).
In fact, the precautionary principle is very simple. All
it actually amounts to is this: if one is embarking on something new, one
should think very carefully about whether it is safe or not, and should
not go ahead until reasonably convinced it is. It is just common sense.
Too many of those who fail to understand or to accept
the precautionary principle are pushing forward with untested,
inadequately researched technologies, and insisting that it is up to the
rest of us to prove them dangerous before they can be stopped. The
perpetrators also refuse to accept liability; so if the technologies turn
out to be hazardous, as in many cases they have, someone else will have to
pay the penalty
The precautionary principle hinges on concept of the
burden of proof, which ordinary people have been expected to understand
and accept in the law for many years. It is also the same reasoning that
is used in most statistical testing. Indeed, as a lot of work in biology
depends on statistics, misuse of the precautionary principle often rests
on misunderstanding and abuse of statistics. Both the accepted practice in
law and the proper use of statistics are in accord with the
common-sensible idea that it is incumbent on those introducing a new
technology to prove it safe, and not for the rest of us to prove it
harmful.
The Burden of Proof
The precautionary principle states that if there are
reasonable scientific grounds for believing that a new process or product
may not be safe, it should not be introduced until we have convincing
evidence that the risks are small and are outweighed by the benefits.
It can also be applied to existing technologies when new
evidence appears suggesting that they are more dangerous than we had
thought (as in the case of cigarettes, CFCs, greenhouse gasses and now
GMOs). Then, it requires that we undertake research to better assess the
risk and that in the meantime, we should not expand our use of the
technology and should put in train measures to reduce our dependence on
it. If the dangers are considered serious enough, then the principle may
require us to withdraw the products or impose a ban or a moratorium on
further use.
The principle does not, as some critics claim, require
industry to provide absolute proof that something new is safe. That would
be an impossible demand and would indeed stop technology dead in its
tracks, but I do not know of anyone who is actually demanding it. The
precautionary principle does not deal with absolute certainty. On the
contrary, it is specifically intended for circumstances where there is no
absolute certainty.
What the precautionary principle does is to put the
burden of proof onto the innovator or perpetrator, but not in an
unreasonable or impossible way. It is up to the perpetrator to demonstrate
beyond reasonable doubt that it is safe, and not for the rest of society
to prove that it is not.
No one should have any difficulty understanding that
because precisely the same sort of argument is used in the criminal law.
The prosecution and the defence are not equal in the courtroom. The
members of the jury are not asked to decide whether they think it is more
or less likely that the defendant has committed the crime he or she is
charged with. Instead, the prosecution is supposed to prove beyond
reasonable doubt that the defendant is guilty. Members of the jury do not
have to be absolutely certain that the defendant is guilty before they
convict, but they do have to be confident they are right.
There is a good reason for adopting a burden of proof
that assumes innocence until proven guilty. The defendant may be guilty or
not, and may be found guilty or not. If the defendant is guilty and
convicted, justice has been done, as is the case if innocent and found not
guilty. But suppose the jury reaches the wrong verdict, what then?
That depends on which of the two possible errors was
made. If the defendant actually committed the crime, but found not guilty,
then a crime goes unpunished. The other possibility is that the defendant
is wrongly convicted of a crime, in which case an innocent life is ruined.
Neither of these outcomes is satisfactory, but society has decided that
the second is so much worse than the first that we should do as much as we
reasonably can to avoid it. It is better, so the saying goes, that "a
hundred guilty men should go free than that one innocent man be convicted".
In any situation in which there is uncertainty, mistakes will be made. Our
aim is to minimise the damage that results when mistakes are made.
Just as society does not require the defendant to prove
innocence, so it should not require objectors to prove that a technology
is harmful. It is for those who want to introduce something new to prove,
not with certainty, but beyond reasonable doubt, that it is safe. Society
balances the trial in favour of the defendant because we believe that
convicting an innocent person is far worse than failing to convict someone
who is guilty. In the same way, we should balance the decision on hazards
and risks in favour of safety, especially in those cases where the damage,
should it occur, is serious and irredeemable.
The objectors must bring forward evidence that stands up
to scrutiny, but they do not have to prove that there are serious dangers.
It is for the innovators to establish beyond reasonable doubt that what
they are proposing is safe. The burden of proof is on them.
The Misuse of Statistics
You have an antique coin that you want to use for
deciding who will go first at a game, but you are worried it might be
biased in favour of heads. You toss it three times, and it comes down
heads all three times. Naturally, that does not do anything to reassure
you, until someone who claims to know something about statistics comes
along, and informs you that as the "p-value" is 0.125, you have
nothing to worry about. The coin is not biased.
Does that not sound like arrant nonsense? Surely if a
coin comes down heads three times in a row, that cannot prove it is
unbiased. No, of course it cannot. But this sort of reasoning is too often
being used to prove that GM technology is safe.
The fallacy, and it is a fallacy, comes about either
through a misunderstanding of statistics or a total neglect of the
precautionary principle - or, more likely, both. In brief, people are
claiming that they have proven that something is safe, when what they have
actually done is to fail to prove that it is unsafe. It's the mathematical
way of claiming that absence of evidence is the same as evidence of
absence.
To see how this comes about, we have to appreciate the
difference between biological and other kinds of scientific evidence. Most
experiments in physics and chemistry are relatively clear cut. If you want
to know what will happen if you mix, say, copper and sulphuric acid, you
really only have to try it once. If you want to be sure, you will repeat
the experiment, but you expect to get the same result, even to the amount
of hydrogen that is produced from a given amount of copper and acid.
In biology, however, we are dealing with organisms which
vary a lot and never behave in predictable, mechanical ways. If we spread
fertiliser on a field, not every plant will increase in size by the same
amount, and if you cross two lines of corn not all the resulting seeds
will be the same. So we almost always have to use some statistical
argument to tell us whether what we observe is merely due to chance or
reflects some real effect.
The details of the argument will vary depending upon
exactly what it is we want to establish, but the standard ones follow a
similar pattern. Suppose, that plant breeders have come up with a new
strain of maize, and we want to know if it gives a better yield than the
old one. We plant each of them in a field, and in August, we harvest more
from the new than from the old. That is encouraging, but it might simply
be a chance fluctuation. After all, even if we had planted both fields
with the old strain, we would not expect to have obtained exactly the same
yield in both fields.
So what we do is the following. We suppose that the new
strain is the same as the old one. (This is called the "null
hypothesis", because we assume that nothing has changed.) We then
work out the probability that the new strain would yield as well as it did
simply on account of chance. We call this probability the "p-value".
Clearly the smaller the p-value, the more likely it is that the new strain
really is better - though we can never be absolutely certain. What counts
as 'small' is arbitrary, but over the years, statisticians have adopted
the convention that if the p-value is less than 5% we should reject the
null hypothesis, i.e. we can infer that the new strain really is better.
Another way of saying the same thing is that the difference in yields is
'significant'.
Note that the p-value is neither the probability that
the new strain is better nor the probability that it is not. When we say
that the increase is significant, what we are saying is that if the new
strain were no better than the old, the probability of such a large
increase happening by chance would be less than 5%. Consequently, we are
willing to accept that the new strain is better.
Why have statisticians fastened on such a small value?
Wouldn't it seem reasonable that if there is less than a 50-50 chance of
such a large increase we should infer that the new strain is better,
whereas if the chance is greater than 50-50 - in racing terms if it is "odds
on" - then we should infer that it is not.
No, and the reason why not is simple: it's a question of
the burden of proof. Remember that statistics is about taking decisions in
the face of uncertainty. It is serious business recommending that a
company changes the variety of seed it produces and that farmers should
switch to planting the new one. There could be a lot of money to be lost
if we are wrong. We want to be sure beyond reasonable doubt, and that's
usually taken to mean a p-value of .05 or less.
Suppose that we obtain a p-value greater than .05, what
then? We have failed to prove that the new strain is better. We have not,
however, proved that it is no better, any more than by finding a defendant
not guilty we have proved him innocent.
In the example of the antique coin coming up three heads
in a row, the null hypothesis was that the coin was fair. If so, then the
probability of a head on any one toss would be 1/2, so the probability of
three in a row would be (1/2)3=0.125. This is greater than .05, so we
cannot reject the null hypothesis, i.e. we cannot claim that our
experiment has shown the coin to be biased. Up to that point, the
reasoning was correct. Where it went wrong was in claiming that the
experiment has shown the coin to be fair.
Yet that is precisely the sort of argument we see in
scientific papers defending genetic engineering. A recent report, "Absence
of toxicity of Bacillus thuringiensis pollen to black swallowtails under
field conditions" (2) is claiming by its title to have shown that
there is no harmful effect. Only in the discussion, however, do they state
correctly that there is "no significant weight differences among
larvae as a function of distance from the corn field or pollen level".
A second paper claims to show that transgenes in wheat
are stably inherited. The evidence for that is the "transmission
ratios were shown to be Mendelian in 8 out of 12 lines". In the
accompanying table, however, six of the p-values are less that 0.5 and one
of them is 0.1. That is not sufficient to prove that the genes are
unstable, or inherited in a non-Mendelian way. But it certainly does not
prove that they are, which is what is claimed.
The way to decide if the antique coin is biased is to
toss it more times and record the outcome; and in the case of the safety
and stability of GM crops, more and better experiments should be done.
The Anti-Precautionary Principle
The precautionary principle is such good common sense
that one would expect it to be universally adopted. Naturally, there can
be disagreement on how big a risk we are prepared to tolerate and on how
great the benefits are likely to be, especially when those who stand to
gain and those who will bear the costs if things go wrong are not the
same. It is significant that the corporations are rejecting proposals that
they should be held liable for any damage caused by the products of GM
technology. They are demanding a one-way bet: they pocket any gains and
someone else pays for any losses. It's also an indication of exactly how
confident they are that the technology is really safe.
What is baffling is why our regulators have failed and
continue to fail to act on the precautionary principle. They tend to rely
instead on what we might call the anti-precautionary principle. When a new
technology is being proposed, it must be permitted unless it can be shown
beyond reasonable doubt that it is dangerous. The burden of proof is not
on the innovator; it is on the rest of us.
The most enthusiastic supporter of the
anti-precautionary principle is the World Trade Organisation (WTO), the
international body whose task it is to prevent countries from setting up
artificial barriers to trade. A country that wants to restrict or prohibit
imports on grounds of safety has to provide definitive proof of hazard, or
else be accused of erecting false barriers to free trade. A recent example
is WTO's judgement that the EU ban on US growth-hormone injected beef is
illegal.
Politicians should constantly be reminded of the effects
of applying the anti-precautionary principle over the past fifty years,
and consider their responsibility for allowing corporations to damage our
health and the environment, which could have been prevented. I mention
just a few: mad cow disease and new variant CJD, the tens of millions dead
from cigarette smoking, intolerable levels of toxic and radioactive wastes
in the environment that include hormone disrupters, carcinogens and
mutagens.
Conclusion
There is nothing difficult or arcane about the
precautionary principle. It is the same sort of reasoning that is used in
the courts and in statistics. More than that, it is just common sense. If
we have genuine doubts about whether something is safe, then we should not
use it until we are convinced it is all right. And how convinced we have
to be depends on how much we need it.
As far as GM crops are concerned, the situation is
straightforward. The world is not short of food; where people are going
hungry, it is because of poverty. There is both direct and indirect
evidence to indicate that the technology may not be safe for health and
biodiversity, while the benefits of GM agriculture remain illusory and
hypothetical. We can easily afford a five-year moratorium to support
further research on how to improve the safety of the technology, and into
better methods of sustainable, organic farming, which do not have the same
unknown and possibly serious risks.
Notes and references
- See, for example Holm & Harris (Nature 29 July, 1999).
- Wraight, A.R. et al, (2000). Proceedings of the National Academy of
Sciences (early edition). Quite apart from the use of statistics, it
generally requires considerable skill and experience to design and carry
out an experiment that will be sufficiently informative. It is all too
easy to fail to find something even when it is there. Our failure to
observe it may simply reflect a poor experiment or insufficient data or
both.
- Cannell, M.E. et al (1999). Theoretical and Applied Genetics 99
(1999) 772-784.
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