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Thinking About Risk - Denise Caruso - PICNIC '10


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How can societies encourage innovation in the life sciences, and still protect the public from unreasonable risks? Those who design or modify living organisms have long claimed there is little or no risk to what they do. As a result, billions (at least) of genetically engineered plants, animals and bacteria have been released into the wild.

But terms like “bioengineering” and “biotechnology” imply a knowledge of the mechanics of living things that is simply untrue by any reasonable standard.

Scientific discoveries are constantly changing our fundamental understanding of biology. Whatever declarations of safety were made based in the past, no one actually knows what the wild and the engineered are eventually going to make of each other – or maybe, eventually, of us.

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Thinking About Risk - Denise Caruso - PICNIC '10

  1. 1. THINKING ABOUT RISK DENISE CARUSO Senior Research Scholar, Department of Engineering and Public Policy, Carnegie Mellon University Author, Intervention: Confronting the Real Risks of Genetic Engineering and Life on a Biotech Planet Executive Director, The Hybrid Vigor Institute Former Technology columnist, The New York Times ... ©
  3. 3. MORALIST We have a moral imperative to save the planet! It is wrong to play God! ©
  4. 4. REALISTS HAVE ISSUES ... With assumptions about the science With the dismissal of precedent With the outdated Industrial Age methods used to make risk decisions ©
  5. 5. THREE KINDS OF RISK Unprecedented events, scientific innovations — scientists don’t know or can’t agree Infectious disease, toxins — need instruments and scientific training Biking, driving, operating heavy machinery (Source: John Adams)
  6. 6. THERE IS A BETTER WAY But you will have to wait until the end of my talk to find out what it is. It’s very exciting! ©
  8. 8. My view of biology is, ‘We don’t know shit.’ - Craig Venter, 2000 This from the guy who is trying to patent the first artificial life form. Thank you for sharing! ©
  9. 9. Human genome ~25,000 genes WHY DO I HAVE MY MOTHER’S NOSE ... BUT NOT A TAIL ? Mouse genome ~25,000 genes
  10. 10. WHY IS AN EAR NOT A HEART? (Differentiation)
  11. 11. HOW — AND WHY?? — DOES PUBERTY START? (It’s called time-based signaling)
  12. 12. DNA EXPLAINS SIMILARITIES, NOT DIFFERENCES Human genome, ~25,000 genes * Mouse genome, ~25,000 genes ** Arabidopsis (plant), 25,000 genes * Before the human genome was sequenced, estimate was 100,000 ** 99 percent are the same as humans ©
  13. 13. ‘We don’t know [even more] shit’ now. Seriously. ©
  14. 14. ENCODE Study, 2003-2007 Goal was a ‘parts list of all biologically functional elements’ in 1% of the human genome — they couldn’t do it Genes operate in ‘complex, interwoven networks’ ‘Reshaped our understanding of how the human genome functions’ and ‘poses some interesting mechanistic questions’
  15. 15. ‘INTERESTING QUESTIONS’ INDEED Billions of transgenic organisms have already been released, based on the ‘parts list’ assumption
  17. 17. STORY ON GM TREES IN THE NEW YORK TIMES: ‘Genetically engineered trees [may] arouse even more controversy than GM crops ... That is partly because [1] many people have an emotional attachment to forests that they do not have to cornfields. Moreover, because[2] trees live longer than annual crops and generally can spread their pollen farther, there are concerns that unintended environmental effects may spread and persist longer ... than in crop fields. ©
  18. 18. WHAT KINDS OF EFFECTS? Deep interdependence between trees, plants, animals, insects, bacteria, water, soil, sunlight ... So complex that scientists cannot predict effects ‘Emotional attachment’?
  19. 19. BIOLOGY IS NOT PHYSICS Prediction is the foundation of engineering — AND OF SAFETY. If you cannot predict its behavior over time, you cannot declare a product is safe.
  20. 20. WHAT IF YOU BUILT A BRIDGE ... ... and one day, it just started to reproduce? What kind of engineer would you be, if you couldn’t predict that?
  21. 21. PERSISTENCE AND RESISTANCE Antibiotics – overprescribed as ‘miracle drugs’ resistance is persistent, i.e., ABR cannot be reversed DDT – overused as a ‘miracle pesticide’ -- bugs became resistant, remains in ecosystem for 10-15 years after application Transgenic crops globally – 77% of soybeans, 49% of cotton (90% in U.S., Australia and South Africa), 26% of corn, 21% canola  80-fold increase  from 1996 to 2009 ©
  22. 22. ISSUE 2. IGNORING PRECEDENT AMNESIA + DÉJÀ VU = We have forgotten this before.
  23. 23. KUDZU (amnesia) That big lump in the middle is a house.
  24. 24. GM SUPERWEED (déjà vu) Herbicide-resistant soy and cotton crops transferred their HR genes to the giant pigweed. Infestations abound. Howard F. Schwartz, Colorado State University
  25. 25. GM COTTON (DÉJÀ VU) Pink bollworm has developed resistance to the pesticide protein produced by Monsanto’s GM cotton.
  26. 26. GM MAIZE International Journal of Biological Sciences: New health effects linked with eating 3 strains of GM maize. ‘Mostly associated with the kidney and liver, the dietary detoxifying organs ....’
  27. 27. NILE PERCH v. GM SALMON (Amnesia) (Déjà vu in waiting?)
  29. 29. ISSUE 3. OLD METHODS DO NOT WORK FOR NEW TECHNOLOGIES INDUSTRIAL AGE   Little uncertainty – systems well described, risk can be calculated   Change relatively predictable in terms of cause and effect   Unintended consequences minimal, largely controllable   BAN or APPROVE, assuming reliable data INFORMATION AGE   Radical scientific uncertainty – no reliable data to calculate risk   Change unpredictable, cause and effect often unknown or misattributed   Unintended consequences cannot be controlled   NEED DIFFERENT RULES
  32. 32. NEW TECHNOLOGY REQUIRES A DIFFERENT APPROACH ... What do the risk experts say?
  33. 33. THEY SAY, ‘COLLABORATE’ ‘ANALYTIC-DELIBERATIVE PROCESS’ Abandons the single-analyst, Industrial Age model All the relevant experts and stakeholders are at the table Combines analysis where data is available, and deliberation about uncertainty when it is not
  34. 34. FOR A SPECIAL KIND OF PROBLEM 1. When lots of different dimensions can be affected by the outcome 2. Where there’s scientific uncertainty — where there’s not enough science to know how things will turn out 3. When people disagree about benefits or outcomes, and they may change if they’re given new information. 4. No single authority can be trusted to know all the answers. 5. Where a decision must be made before “getting certain”
  36. 36. IN GENERAL, SCIENTISTS HATE THIS IDEA They think that laypeople are ignorant, and have zero tolerance for risk.
  37. 37. BUT IF THAT WERE TRUE ... The pharmaceutical industry would have gone bust long ago.
  38. 38. ANALYSIS + DELIBERATION CAN ELIMINATE BIAS AND INFLUENCE ... ... both of which I have left out of this talk, for the sake of brevity and sanity
  39. 39. AND OUTCOMES IMPROVE  By questioning scientific judgments and assumptions with fresh eyes.  By challenging each other’s biases.  By calling out critical uncertainties that are invisible to or ignored by experts.  By helping scientists come up with new research agendas to answer specific risk questions.  Collaborating on risk decisions uses the variety of human experience as a positive force —for society and for science.
  41. 41. IF YOU WANT TO KNOW MORE ...Denise Caruso is the founder and executive director of the nonprofit Hybrid Vigor Institute. Her most recent work aims to augment traditional risk analysis with new methods that are better suited to innovations in science and technology. She is presently working on projects in the area of emerging infectious diseases. Also a veteran technology analyst and journalist, Caruso has more than 20 years experience assessing the converging industries of digital technology, telecommunications and interactive media. Her perspectives have been featured in The New York Times, where she served for five years as Technology columnist, as well as in the Wall Street Journal, Columbia Journalism Review, WIRED, MSNBC, and her own newsletters, Digital Media and Technology & Media. Intervention: Confronting the Real Risks of Genetic Engineering and Life on a Biotech Planet challenges two of the most sacred tenets of modern society, innovation and technology, from the perspective of the unique risks they present. Using genetic engineering as its model, it paints a vivid picture of the scientific uncertainties that biotech risk evaluations dismiss or ignore, and lays bare the power and money conflicts between academia, industry and regulators that have sped these risky innovations to the market. Intervention champions an alternative method for assessing the risks of technology, developed by the world's top risk experts, that can eliminate such conflicts, help regain public trust in science and government, and drive research and development toward more useful, safer products. The nonprofit Hybrid Vigor Institute was founded in 2000 to stimulate more and better collaboration between experts. Its work has been supported by the U.S. government, public and private foundations, and individuals. More information on the Institute and its work is available online, at Confronting the Real Risks of Genetic Engineering and Life on a Biotech Planet INTERVENTION DENISE CARUSO INTERVENTIONDENISECARUSO(hv)i