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The Precautionary Principle: Why We Need a
Global Moratorium on GE Foods & Crops

Subject: ISIS paper: The Use and abuse of the precautionary principle
Date: Fri, 14 Jul 2000 Biotech Activists (biotech_activists@iatp.org)
Posted: 07/14/2000 By M.W.Ho@open.ac.uk
============================================================
ISIS submission to US Advisory Committee on International Economic Policy
Use and Abuse of the Precautionary Principle
The Burden of Proof
The Misuse of Statistics
The Anti-Precautionary Principle
Conclusion
Notes and references

ISIS submission to US Advisory Committee on International Economic Policy
(ACIEP) Biotech. Working Group

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 <www.i-sis.org> ).

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.

Dr. Mae-Wan Ho
Director
Institute of Science in Society
C/o Dept of Biological Sciences
Open University
Walton Hall Milton Keynes
MK7 6AA, UK

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

1. See, for example Holm & Harris (Nature 29 July, 1999).
2. 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.
3. Cannell, M.E. et al (1999). Theoretical and Applied Genetics 99
(1999) 772-784.

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