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Risk and Ethics: Social Benefits Vs. Societal Risks

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Title: Risk and Ethics: Social Benefits Vs. Societal Risks


1
Risk and Ethics Social Benefits Vs. Societal
Risks
  • Engineering 124
  • March 18 20
  • W.E. Kastenberg

2
Historical Perspective
...the appearance of disease in human
populations is influenced by the quality of air,
water, and food the topography of the land and
general living habits.

the ancient-Greek physician Hippocrates in his
treatise Air, Water and Places


We Athenians in our persons, take our decisions
on policy and submit them to proper discussion.
The worst thing is to rush into action before
the consequences have been properly debated. We
are capable at the same time of taking risks and
estimating them before hand. Others are brave
out of ignorance But the man who can most truly
be accounted brave is he who best knows the
meaning of what is sweet in life, and what is
terrible, and he then goes out undeterred to
meet what is to come.

From Pericles Funeral Oration in Thucydides
History of the Peloponnesian War (started in
431 BC)
3
What Is Risk?
Risk 1. Possibility of loss or injury.
2. A dangerous element or factor. 3.
The chance of loss. 4. A person or
thing that is a specified hazard.


Safe 1. Freed from harm or risk. 2.
Secure from treat of danger, harm
or loss. 3. Affording safety from
danger.
4
Risk Analysis
  • What are the risks imposed by various
    technologies? (Risk assessment)
  • Are these risks acceptable? (Risk valuation)
  • Can these risks be reduced? (Option generation)
  • How can the options be evaluated? (Cost/benefit)

5
Risk Assessment
  • Risk assessment asks three questions
  • What can go wrong ?
  • How likely is it to happen?
  • What are the consequences?

6
Risk (Sequences and Consequences)
Consequence
Exposure
Event
Acute Effects
Acute
Latent Effects
Accidental Release
Latent Effects
Chronic
Chronic Release
Chronic
Latent Effects
7
Examples of Risk Measures
  • Consequence or Hazard Measure of Risk
  • Acute Fatalities Early Deaths/ Year
  • Cancer Death Latent Deaths/ Year
  • Contaminated Land Acres Lost/ Year
  • Contaminated Water Concentration in Drinking
    Water or Wells Closed/ Year
  • Economic Loss Lost/ Year
  • Genetic Effects Mutations/ Year
  • Teratogenic Effects Birth Defects/ Year
  • Neurological Disease Illness/ Year
  • Species Loss Species Loss/ Year
  • Core Melt Events/ Year

8
Quantifying the Risk of Accidents
  • Risk - the expected value of an undesirable
    consequence
  • i ith sequence
  • fi frequency of occurrence
  • xi consequence of undesirable
    event

9
How Does Risk Assessment Work?
  • What are the risks from driving an automobile?
  • There are 15,000,000 accidents per year, 1 in 300
    of which result in death, there are 250,000,000
    people

10
Fault and Event Trees
11
Risk From Toxic Chemicals
  • Risk is a function of exposure toxicity
  • How much of the toxic material is the individual
    going to be exposed to?
  • What amount of toxic material is likely to cause
    an adverse health effect?
  • Location and strength of source (Qij)
  • Model the spread of the plume (Xi)
  • Model the exposure to human or other species
    (Eij)
  • Model the dose response relationship (DRi)

12
Risk Assessment
  • Hazard identification uses toxicology (cell,
    tissue and animal tests) and epidemiology
    (population data and field samples)
  • Exposure assessment includes determination of
    sources, environmental concentrations, exposure,
    dose, and uncertainties

13
Framework for Risk Assessment
Transport and transformation
Source/ Inventory

Emission
Exposure Events
Risk Mitigation
Dose/Response
Biokinetics
Uptake
Risk Characterization
Response
Dose
14
A Multimedia, Multiple Pathway Exposure Model
15
Environmental Fate and Transport
16
Inter-media Transfers
  • Air and soil to food

17
Multiple Exposure Pathways
Inhalation
Activity Patterns
Dermal
18
Mechanism of Action
  • Whether a compound reaches a target tissue
    depends on
  • Absorption through the GI tract, lung, or skin
  • Distribution in the body
  • Biotransformation
  • Excretion

19
From Animal Experiments to Human Risk Factors
  • Advantages
  • Carefully controlled conditions
  • Mostly closed systems
  • Limitations
  • High doses required
  • Extrapolation to humans
  • Important conditions
  • Concentration or dose
  • Time to tumor measured
  • Maximum tolerated dose
  • What do we get from these experiments?
  • No effects
  • Threshold of effect
  • Dose-response models

20
Dose Response Models
  • Dose-response models can be classified according
    to how they represent risk at low doses
  • R(d) const x dm
  • m 1 is linear
  • mgt1 is sublinear
  • mlt1 is supralinear

21
Low-Dose Models
  • Statistical models--tolerance distribution models
  • Probit (sub-linear) - a lognormal tolerance model
  • P(d) a b log(d)
  • Logit (sub-linear) - characterizes tolerance
    distribution with a logistic function
  • P(d) 1/ (1 exp(-a b log(d) )
  • Weibull - Time to tumor distribution
  • P(d) 1 - exp(-a - bdm)

22
Low Dose Models
  • Stochastic or hit models
  • One-hit model - Derived from Poisson statistics
  • P(d) 1 - exp(-a - b d)
  • Multi-stage- Based on the stage theory of cancer
    as first proposed by Armitage and Doll
  • P(d) 1 - exp(-a - b1 d - b2 d2 - b3 d3
    ...)
  • Linearized Multi-stage model--the b1 parameter is
    replaced by its upper confidence limit.
  • Biologically-based cancer models

23
Hazard Quotient Models
  • Used primarily for non-cancer health endpoints
  • Define safe dose
  • ADI allowable daily intake, mg/day
  • RfD Reference dose, mg/kg/d
  • Risk is based on a hazard index HI Dose/ADI
  • Often calculate RfD based on animal studies

24
Benchmark Dose
  • RfD NOAEL / (UFs x MFs)
  • UF uncertainty factor MF modifying factor
  • NOAEL no observed adverse effects level
  • LOAEL lowest observed adverse effects level
  • Modifying factors include
  • Interspecies adjustment,
  • Subchronic to chronic,
  • LOAEL to NOAEL

25
EPA Hazard Identification
  • Physical-chemical properties and routes and
    patterns of exposure
  • Structure -activity relationships
  • Metabolic and pharmacokinetic properties
  • Toxicological effects
  • Short-term tests - in vitro and in vivo
  • Long-term animal studies
  • Human studies
  • Weight of evidence

26
EPA Weight of Evidence
  • Group A - Human carcinogen based on sufficient
    human evidence
  • Group B1 - Probable human carcinogen based on
    limited human evidence and sufficient animal
    evidence
  • Group 2B - Probable human carcinogen base on
    insufficient or no human evidence and sufficient
    animal evidence
  • group C - Possible human carcinogen based animal
    evidence
  • Group D - Not classifiable
  • Group E - Evidence of non-carcinogenicity for
    humans

27
Ethical Basis for Risk Management
  • Ethics based on a universal set of rules and
    principles after Descartes (1596-1650)
  • John Locke (1632-1704) rights ethics.
  • Immanuel Kant (1724-1804) duty ethics.
  • Jeremy Bentham (1748-1832) and John Stuart Mill
    (1806-1873) utilitarianism.

28
Qualitative Safety Goals
  • Individuals bear no significant additional risk
    to life and health
  • Societal risks to life and health from nuclear
    power plant operation should be comparable to or
    less than the risks due to electric generation by
    competing technologies and should not be a
    significant addition to other societal risks

29
Quantitative Safety Goals
  • Risk to the average individual in the vicinity of
    a nuclear power plant of prompt fatalities that
    might result from reactor accidents should not
    exceed one-tenth of one percent (0.1 percent) of
    the sum of prompt fatality risks resulting from
    other accidents to which members of the US
    population are generally exposed
  • The risk to the population in the area near a
    nuclear power plant of cancer fatalities that
    might result from nuclear power plant operation
    should not exceed one-tenth of one percent (0.1
    percent) of the sum of cancer fatality risks
    resulting from all other causes

30
Utilitarianism
  • Engineering and technological decision making,
    for the most part, are based on derivatives of
    utilitarianism.
  • A basic tenant of utilitarianism is the greatest
    good for the greatest number.
  • This gives rise to economic determinism as
    manifest in cost/benefit and risk/benefit
    analyses.

31
Cost/Benefit and Risk/Benefit
  • Insurance how much am I willing to spend each
    year to insure my house, car, life and for what
    amount?
  • Energy what risks am I willing to take for the
    benefit of 1,000 MWe among a coal, natural gas,
    oil or nuclear power plant?
  • Medical how many lives can I save by inoculating
    all children against polio (or having all women
    over the age of 40 have a yearly mammogram) and
    at what cost and risk?

32
Drawbacks of Utilitarianism
  • Only the total good, and not its distribution
    among people, is relevant to moral choice.
  • Difficulty in attempting to quantify the greatest
    good.
  • Utilitarianism tends to be anthropocentric.
  • Utilitarianism judges by consequences rather than
    actions.

33
Societal Values and Acceptable Risk
  • Quantitative safety goals for nuclear power
    plants (0.1 of background acute and latent
    fatality risk).
  • Hazardous facilities on the order of 10-6 per
    year.
  • ALARA (for example 1000/person-rem averted).
  • Remediation of contaminated sites (acceptable
    excess lifetime cancer risk).

10-4
10-6
34
Risk Analysis Has Become an Important Tool in
  • Remediation of Superfund sites.
  • Assessing space missions.
  • Improving safety at chemical process plants.
  • Improving safety at plants that generate
    electricity (fossil fueled and nuclear).
  • Setting insurance rates.

35
Our Basic Premise
  • Risk analysis my be limited in its accuracy and
    completeness when attempting to evaluate and
    manage the risks of emerging technologies.

36
Emerging Technologies
  • Biotechnology
  • Information technology
  • Nuclear technology
  • Energy technology global climate change
  • Nano-technology

37
Limiting Assumptions of Risk Analysis
  • A focus on the factuala quantification of the
    undesirable consequences of technology such as
    human health effects and environmental
    degradation.
  • Does not focus on the axiologicalthe evaluation
    of the unintended impacts of technology on the
    manner in which we live psychologically,
    socially and spiritually.

38
Limiting Assumptions of Risk Analysis
  • The paradigm or context that defines the culture
    of risk analysis is linear and dualistic.
  • The quantification of risk is based on
    reductionism which leads to an objective search
    for causal links or causal chains.
  • The management of risk is based on a set of
    universal rules (quantitative safety goals) or
    principles (cost/benefit analysis).

39
The Newtonian-Cartesian Paradigm
  • Atomistic leads to reductionism or
    fragmentation.
  • Deterministic leading to cause and effect.
  • Subject/object dualism observation does not
    affect the system being observe The laws
    governing a systems behavior can be deduced from
    objective empirical observations (objectivism).

40
Insufficiency of the Risk Paradigm
  • The interplay between technology, society and the
    environment has always been nonlinear.
  • In the past, however, the consequences of
    technology were geographically local and/or they
    were observable in real time.
  • This gave the impression that a linear paradigm
    was an accurate worldview.

41
Insufficiency of the Risk Paradigm
  • Modern technologies can have global impacts
    (ubiquitous) that may be irreversible and/or can
    be imperceptible with time.
  • Hence their consequences have large spatial
    domains and/or either very short or very long
    temporal scales so that nonlinear effects become
    dominant.

42
Complex or Nonlinear Systems
  • Holism the whole system cannot be described by a
    knowledge of its parts alone.
  • Chaotic small changes in input can lead to large
    changes in output and/or there may be many
    possible outputs for a given input.
  • Subjectivism some aspects of the system may not
    be describable by objective means.

43
Uncertainty
  • Aleatory uncertainty in data.
  • Epistemic uncertainty in models.
  • Indeterminacy many possible outcomes for a given
    input.
  • Ignorance we dont know what we dont know.

44
Ambiguity
  • Ambiguity refers to the variability of
    (legitimate) interpretation based on identical
    observation or data assessments.
  • Differences in interpreting factual statements
    about the world.
  • Differences in applying normative rules to
    evaluate the state of the world.

45
What Is a System?
  • A set of objects (parts, objects, components or
    subsystems).
  • The attributes of the objects (mass, volume,
    temperature, charge, etc.).
  • A set of relationships between the parts and a
    set of relationships between the attributes.
  • (The set of objects defines a boundary around the
    system which may be physical or conceptual.).

46
General Systems Theory
  • The system is not only a whole, but also a part
    within a larger whole.
  • The system has permeable boundaries.
  • The system is self organizing.
  • The system behavior is stochastic or chaotic and
    may achieve equilibrium through a trial and
    error process.

47
Emergent Property or Quality
  • When we say, the whole is greater than the sum
    of its parts, we mean that there is an emergent
    property or quality that the whole possesses that
    is not found in the parts.
  • For example, when hydrogen and oxygen come
    together to form water, we have the property of
    wetness.

48
Self-similarity
49
Shape Preserving Bisections
A1A0/2
A0
A1A0/2
...
Ai A0/2i
A2A0/4
A2A0/4
50
Community-level Self-similarity
Prob(?) a
Ai Ai-1/2
? is randomly chosen from , , ,
,
Ai-1
51
Serpentine Flora Species-Area Relationship
z 0.21 r2 gt 0.999 a -log2(z) 0.86
52
Elements of a Research Agenda
  • Characterizing risk should be a decision driven
    activity, directed at informing choices and
    solving problems.
  • Managing risk requires a broad understanding of
    the relevant consequences and impacts.
  • Risk characterization is the outcome of an
    analytic-deliberative process.

53
Decision-driven Activity
  • Do our emerging technologies create axiological
    resonance or dissonance within the society within
    which they are to be implemented?
  • In order to answer this question, we require
    research aimed at expanding the decision making
    activity from a set of universal rules and
    principles to one that is also contextual or
    situational based.

54
Broad Understanding of Risk
  • Research to expand the paradigm of risk
    characterization from reductionism to holism.
  • Consideration of both the undesirable (factual)
    consequences and the unintended (axiological)
    impacts.
  • Discovery of emergent properties or qualities of
    complex systems.
  • Inclusion of qualitative as well as quantitative
    consequences and impacts.

55
Analytic-deliberative Process
  • Epistemological dialogueexperts consider factual
    assessment.
  • Reflective dialoguepolicy makers, scientists and
    stake-holders consider risk management.
  • Participatory dialogueinclusion of public
    citizens and focused on societal values and
    ethical considerations.
  • Dialogueallowing the emergence of possibilities
    that were unthinkable prior to the dialogue.
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