Risk Communication Challenges for Nanomaterials: A Taxonomy (Typology) within a Risk Analysis Framework Prof. Jennifer Kuzma, co-PI Associate Professor, U of MN NIRT ITox Meeting August 28, 2008

Outline Challenges in risk communication from a risk analysis standpoint Context and framing possibilities Discussion of integrating concepts across disciplines- -risk analysis, risk communication, science, public policy, and public engagement Ties to NSF-funded research

Risk Communication goals exchange among informedindependentRisk communication is an exchange of information about risk among decision makers, stakeholders, and the public which is intended to supply people with the information they need to make informed and independent judgments about risk –Morgan, G. et al Risk Communication: A Mental Models Approach. Cambridge, MA: Cambridge University Press. (p. 4) Not a “deficit model”, but Enabling model Advice and answers Number Context and Framing

A Risk Analysis Framework

Risk communication should be the hub of policy Powell and Leiss 1997 Mad Cows and Mother’s Milk: The Perils of Poor Risk Communication

A Risk Policy Problem Powell and Leiss 1997

Risk Communication Challenges Powell and Leiss 1997

An (outdated) model of risk communication Knuth, B.A. (1990). North American Journal of Fisheries Management. 10(4): Message modulators Credibility of messenger Cultural, social, and operational factors Channels of communication Can cause distortion and unintended messages Filters

IRGC report Where do nanomaterials fit? Depends on type of nanomaterial—product dependency? High ambiguity—more need for deliberation (context and framing approach to risk communication) IRGC 2006 Advice and answers, numbers, context/framing?

Environmental Risk Assessment for Nanomaterials Monitoring, Adaptive, Feedback, Guiding Force for Risk Research

Steps to risk communication or deliberation (Morgan et al 2002) Create expert model (influence diagram) –Diagram allows representation of knowledge of experts from diverse disciplines –“The influence diagram allows a quick, visual check of the factors that must be covered when evaluating audience information needs” Caseman and Morgan (2008) Conduct open-ended (mental-model) interviews –People’s beliefs about hazard and risk in their own terms –How well do mental models correspond to expert model in influence diagram –Identify issues Conduct structured initial interviews –Explore issues –Larger groups Draft risk communication or deliberation method –Neutral voice –Which knowledge gaps need filling Evaluate communication or deliberation method with individuals selected from target population –“Lay evaluation”

Expert Model of risk--puzzle Morgan 2005 Risk Analysis 25(4):

Toxic Effects Magnified

Equity in risk discussions Expert vs. Not Expert Barriers, filters, gaps, and different mental models Can we level the playing field so that we get to true differences in attitudes rather than differences in reception and understanding of information?

Gaps in Information Even Experts do not have the information Any individual study about risk does not put the puzzle together How to communicate with stakeholders and laypersons about risk based on one or just a few pieces of the puzzle?

Possible approaches to test Map expert model onto layperson model –Follow standard Morgan et al. approach Map expert studies into expert models: –Forgo “advice, answers,” and “numbers” for “context and framing” –Use expert framing of risk for nanomaterials to type individual pieces of information –Visual and contextual translation for not-expert audience

Possibilities to Explore for Better Risk Communication Objective risk –Database of studies mapped into risk assessment (and risk analysis) framework Levels to database based on user (part of U of MN NSF CEIN proposal) –For starters, use what, when, who, why, where questions to enhance communications about risk (and toxicology or dose- response) Subjective risk –Listen and learn –Incorporate concerns and values into risk analysis framing of problems –Deliberative democracy. Public engagement approaches

Puzzling together risk Context and Framing: Information comes in bits and pieces How can we enhance risk communication for individual studies? U of MN CEIN grant proposal 2007

Clearinghouse of EHS and Risk Studies Taxonomy of EHS information in Risk Analysis framework –Level 1: Public, Educators, Stakeholders –Level 2: EHS and other interested experts –Level 3: Nanomaterial manufacturers –Level 4: Nanoinformatics A possible communication tool? –Web-based information and framing tool for other printed or verbal materials Research Risk Assessment Risk Analysis Context and Framing

Typology of questions about risk

Trust-credibility Address subjective risk component (deliberation, engagement) But recognize that subjective (social?) and objective (epistemic?)dimensions of risk are not that distinct –Fischhoff, B., S.R. Watson, and C. Hope Defining risk. Policy Sciences 17: –Kuzma and Besley Ethics of Risk Analysis and Regulatory Review: From Bio to Nano. Nanoethics in press, online. Prevent biased (and exaggerated) communication of individual study results –E.g. GE Corn and Monarch butterfly story, Losey article and first round of field trials (see Pew Initiative on Food and Biotechnology report, 2002) “Neutral” communication bodies for objective information and subjective risk discussions

Ethics of Risk Analysis and Regulatory Review: From Bio to Nano Kuzma and Besley 2008, Nanoethics

Factors in Risk Comparisons & Perception “Risk” not necessarily equal to the # of fatalities Experts perceive differently Laypersons –Benefits, Trust, Affect Important –Product dependent for nanofood –Siegrist 2007, 2008 “Thus, disagreements about risk should not be expected to evaporate in the presence of evidence” (Slovic et al 1990) “Risk Perception Factors” –Natural/Man-Made –Ordinary/catastrophic –Voluntary/Involuntary –Delayed/Immediate –Controlled/Uncontrolled –Old/New –Necessary/Luxury –Regular/Occasional Rasmussen, Slovic, Fischhoff, et al. 1990, in Readings in Risk

Stages of Risk Communication Given “fuzziness” of risk and risk analysis itself, trust, and perception factors (S. Priest) Need to move beyond stages, separation of objective/subjective To Integration, Enabling, Contextual, Analytical-Deliberative process (NRC 1996) Morgan et al. 2002

NSF-NIRT Grant 1. What factors are most significant in affecting public perception of the risks of applied nanosciences? 2. What, if any, relationship exists between the modes of public deliberation, sources of information (e.g., use of new media), and the effects of new information on perceptions of the risks of applied nanosciences? refine anddevelop key variables and instruments (stage 1)refine and develop key variables and instruments (stage 1) determine the contribution of variables to perceived risk—Delphi rounds (stage 2) –Will framing help level playing field between experts and non- experts? elucidate the effect of civic engagement and new media on risk perception (stage 3) verify key variables related to risk perception—focus groups agrifood nano (stage 4), outreach to the public (stage 5).

Other NIRT—Oversight Lessons for Nanotechnology Evaluation of six historical models National Science Foundation NIRT Grant SES (Wolf, PI; Kokkoli, Kuzma, Paradise, Ramachandran, Co-PIs).

Other NIRT—Lessons for Nanotechnology Evaluating Oversight Models for Nanobiotechnology Experts asked to rank how six case studies of oversight have performed on scale of on 28 criteria National Science Foundation NIRT Grant SES (Wolf, PI; Kokkoli, Kuzma, Paradise, Ramachandran, Co-PIs).

Relationships among criteria p<0.0016

Relationships: Attributes of GEOs oversight to public confidence as an outcome p<0.05 R=approx. 0.5 for all Hypothesis: public input is important for outcome of public confidence in oversight systems.

Messages and Synergies among NSF-NIRTs and fields of investigation Consider attributes of oversight as possible factors in perception of new technologies (risk perception too) Finding relationships among attributes of oversight and outcomes such as public confidence, health and safety, and environmental impacts.

Thank you NSF NIRT grant Intuitive Toxicology and Public Engagement (# ) (PI: David M. Berube, co-PIs, Dietram A. Scheufele,Jennifer Kuzma, Kevin Elliott, Pat J. Gehrke, V. Colvin). Contact info, J. Kuzma – ,