From: Olie Lamb (email@example.com)
Date: Mon Aug 14 2006 - 22:43:36 MDT
Unfortunately, many elements critical to morality - such as pleasure,
suffering, satisfaction of goals - are inherently awkward to quantify.
Think about the classic stupid question: "On a scale of 1 to 10, how
much does it hurt?"
Hence, one of the earlist areas of utilitarian work, felicific
calculus (go look-see), has been pretty thoroughly caned for its lack
of real-world applicability. Sure, it's useful for illustrating
certain concepts, but it's one helluva challenge to weigh things
And you can't easily get a lot of (informed) subjective valuations, either...
("Would you rather lose a finger or an eye?" "Um... *guesses*...eye?"
*Tries removing each to verify*...)
That said, it's something that health providers and triage sometimes
/try/ to do... but not thoroughly. F'rinstance, compare the evidence
for cost-per-life-saved for road safety campaigns vs the
cost-per-life-saved from doing HIV testing on donated blood (road
safety generally much cheaper, but people get scared by HIV), or, say
the cost-per-life of organ transplants vs the cost-per-life-saved for
On 8/15/06, Anthony Mak <firstname.lastname@example.org> wrote:
> Dear all,
> Does anyone know any work or paper people have done in
> the past or present about how to measure (quantify) morality?
> The motivation is, for a learning system to learn how to be
> moral, I believe it is necessary to have an objective function
> to measure how "moral" the machine already are, so that
> a machine learning alogorithm can work. Are there any
> works for example in philosophy where people attempted
> to device scheme/method/framework to measure morality?
> At the moment, I can only imagine using some questionaire
> type query to attempt to measure a person's "moral IQ",
> be it a normal person or machine person.
> PS. I guess another approach is to try to find all the +ve
> and -ve effects from an agent's actions and try to sum them
> Any reference to papers or books or other source will be
> extremely helpful.
> Anthony Mak
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