Lecture 6.4 - Case studies from science: trust and distrust of science by laypeople

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Philosophy, Science and Religion: Philosophy and Religion

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The University of Edinburgh

Philosophy, Science and Religion: Philosophy and Religion

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Philosophy, Science and Religion mark three of the most fundamental modes of thinking about the world and our place in it. Are these modes incompatible? Put another way: is the intellectually responsible thing to do to ‘pick sides’ and identify with one of these approaches at the exclusion of others? Or, are they complementary or mutually supportive? As is typical of questions of such magnitude, the devil is in the details. For example, it is important to work out what is really distinctive about each of these ways of inquiring about the world. In order to gain some clarity here, we’ll be investigating what some of the current leading thinkers in philosophy, science and religion are actually doing. This course, entitled ‘Philosophy and Religion’, is the second of three related courses in our Philosophy, Science and Religion Online series, and in this course we will ask important questions about the age-old debate between science and religion, such as: • What kind of conflicts are there between religion and science? • Does current cognitive science of religion effectively explain away God? • If there is a God who has made us so that we can know him, why do some people not believe? • Is belief in science also a kind of fundamentalism? • What makes us good at getting, giving, or sharing, knowledge? Is this different when it is religious knowledge? The first course in the Philosophy, Science and Religion series, 'Science and Philosophy' was launched early in 2017 and you can sign up to it at any time. The third course —‘Religion and Science’—will be launched early in 2018. Completing all three courses will give you a broader understanding of this fascinating topic. Look for: • Philosophy, Science and Religion I: Science and Philosophy https://www.coursera.org/learn/philosophy-science-religion-1/ • Philosophy, Science and Religion III: Religion and Science Upon successful completion of all three courses, students will: (1) Understand the main parameters at stake in the current debate between science and religion. (2) Have some familiarity with the relevant areas of science that feature in the debate—including cosmology, evolution, and the neurosciences—and will have begun to engage with them conceptually. (3) Have encountered key philosophical approaches to the interface between science and religion, and will have had the opportunity to engage them in practice. (4) Have embarked constructively in cross-disciplinary conversations. (5) Have demonstrated an openness to personal growth through a commitment to dialogue across intellectual and spiritual boundaries. You can also follow us on Twitter at https://twitter.com/EdiPhilOnline and you can follow the hashtag #psrmooc

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Epistemic Virtues and Vices in Science and Religion

In this lecture, Professor Mark Alfano discusses the role of epistemic virtues and vices in science and religion. The lecture has three main sections. First, Alfano distinguishes four types of epistemic virtues and vices. Source virtues such as honesty make someone an excellent primary source of knowledge. Receiver virtues such as intellectual humility make someone an excellent recipient of knowledge provided by sources. Conduit virtues make someone an excellent conveyor of the knowledge they receive from others to third parties; these dispositions might include a willingness to gossip carefully in order to protect others from a sexual predator, as well as the virtues that journalists try to embody. Echo virtues make someone an excellent sounding board for others. Along the way, Alfano mentions various vices that can attach to people in the role of source, receiver, conduit, and echo. In the second part of the lecture, Alfano uses the notions of source, receiver, conduit, and echo virtues to make sense of scientific collaborations and trust in science by laypeople. In section three, he shows that unless we have unreasonably high credence in very long chains of conduit virtues, we should not accept testimony in favour of miracles or divine revelation.

Lecture 6.1 - Overview of the lecture4:06

Lecture 6.2 - Introduction to epistemic virtues and vices17:05

Lecture 6.3 - Case studies from science: scientific collaborations4:57

Lecture 6.4 - Case studies from science: trust and distrust of science by laypeople9:49

Lecture 6.5 - Case studies from religion: miracles7:24

Lecture 6.6 - Case studies from religion: transmission of revelation7:38

Conheça os instrutores

Dr Orestis Palermos
Research Explorer
School of Philosophy
Dr Mark Harris
Senior Lecturer in Science and Religion
School of Divinity
Professor Duncan Pritchard
Professor of Philosophy
University of Edinburgh
Dr J Adam Carter
Researcher
Epistemology - Philosphy
Dr Mog Stapleton

The second case study related to science has to do not with collaborations

among scientists but in lay reactions to scientific research.

How should ordinary people think about science reporting or

about the actual articles that are published by scientists?

Should their motto also be 'take no one's word for it?'

Should they be citizen scientists and build their own Large Hadron Collider?

Or do they just sometimes have to take things on faith or at least on trust?

How can they respond in a epistemically virtuous way to the reports of scientists?

This is a really important topic that relates not

only to conspiracy theories about, for instance,

the safety and effectiveness of vaccines but various other kinds of lay reactions

to scientific research including denial of

the evidence for anthropocentric climate change,

denial of evidence for neo-Darwinism,

acceptance of homeopathy, denial of the connection of

citizens gun-ownership with deadly violence and so on.

We need to have some way for scientists to communicate effectively with

laypeople without just turning scientists into

priests whose word has to be taken as gospel.

One problem then is that on the one hand,

laypeople do need to be skeptical about scientific research.

But on the other hand,

they need to be not so skeptical that they can just believe whatever they want,

no matter what the evidence suggests.

A recent paper by Dan Kahan and colleagues on the topic of

Motivated Numeracy shows what we have in mind here.

Kahan defines numeracy as the ability to

understand graphs and figures and the results of scientific research.

He has the worry that people only apply

their numeracy when the results support what they already believe.

And that would suggest that people are suffering from what's known

as confirmation bias in the psychological literature that they

trust the evidence when it tells them what they want to

hear and that they distrusted or that

they refuse to process it

effectively when it tells them something they didn't want to hear.

Kahan presented participants the results of a hypothetical drug study,

a study on the effectiveness of a new skin cream for healing rashes.

You can see one example of what he showed participants here on the slide.

This is a contingency table which means that the numbers

represent the number of people in each cell.

So for instance, 223 people did use the new skin cream and got better,

whereas 75 people used the skin cream and got worse.

107 people did not use the skin cream and got better,

whereas 21 did not use the skin cream and got worse.

What Kahan asked people to do was to study this table and decide whether the results

indicate that the skin cream makes people get better or makes people get worse.

Is it effective or not?

It's actually kind of a tricky question.

What you need to do is calculate the probability of getting better or worse in each row.

So that means you look first only at patients who did use the skin cream.

There are 298 of them.

223 of them got better,

75% in other words.

Then we look at the second row and we see that there are

128 people and 107 of them got better, meaning 84%.

So which row would you prefer to be in?

Well, you should prefer to be in

the second row unless you like having rashes because you have

a better chance of having a rash get

better if you don't use the skin cream than if you do.

Most people who don't look at this carefully though have

a lot of difficulty in picking out the correct answer.

And that means that they really need to exercise

their numeracy in order to get the correct answer on this question.

Now, whether a hypothetical cream is effective at relieving

rashes doesn't have to do with people's prior beliefs or at least not much.

So, Kahan then showed people the same contingency table and

instead of rashes getting better and worse based on application of a skin cream,

the question had to do with gun control.

This is a very controversial issue in the United States where people

on the political right conservatives and Republicans tend to think that

gun control leads to more violence whereas people on the political left and

Democrats tend to think that gun control leads to a decrease in crime.

What you can see here are the four conditions that

participants could have seen when they took Kahan's study.

The one at the top right, condition B,

is the one that we just discussed,

and the other three are very minor modifications.

So at the top left, condition A,

all we do is switch whether the rash gets worse or better.

We switch the columns but the numbers remain the same.

And then, in condition C and D,

instead of the rash and cream,

we're looking at crime and gun control.

So, this means that if you're just reading

the numbers and not paying attention to the political issue,

you should be able to get the answer right just as

easily in condition B as in conditions A,

C and D. But if what's happening is that people are only

exercising their numeracy when it

will give them the conclusion that they already agree with,

we should see a difference between these conditions.

And in fact, this is what Kahan found.

So start with the top right graph,

these are people who are high in numeracy as measured by an independent test

and they're the ones who took

the version of the study that had to do with the skin treatment.

As you can see, Liberals represented by

the blue lines and Conservatives

represented by the red lines are pretty much indistinguishable.

The x axis here represents how frequently they got the right answer and

the y axis represents how many of them got the answer correct at that level.

And as you can see where the Conservatives might

be just slightly less effective at getting the right answer,

it's more or less indistinguishable.

The low numeracy condition,

the top left, shows that yes, as we would expect,

people who are lower at numeracy get the answer correct less

often and once again Conservatives and Liberals are pretty much indistinguishable here.

However, if we go to the bottom left where we're talking about the gun ban,

we find that low numeracy conservatives

have a huge divergence from the previous conditions.

So if the evidence suggested that crime increased,

then they got it right most of the time.

But if the evidence suggested that crime decreased,

they got it wrong almost the entire time,

about 15% of them got it right.

By contrast, low numeracy Liberals

basically couldn't tell what the right answer was and they were more or less guessing.

They got the correct answer about 40% of the time.

Then, we can look at the most depressing graph here,

the one for high numeracy individuals who are reasoning about the gun ban.

And we can see that

high numeracy Conservatives get the correct answer almost every single

time when crime increases but they look like low numeracy people when crime decreases.

You can see there that the peak is around 15%.

By contrast, high numeracy Liberals get

the correct answer when crime decreases almost every time,

about 80%, but they get the answer wrong when crime increases.

What this suggests is that people only exercise their numeracy,

their intellectual abilities and epistemic virtues when it suits them,

when the results of doing so will support what they already think.

And this suggests that trust and distrust in science by laypeople is going to be

a very large problem unless the lay population can

be led to exercise their own capacities more effectively.

So to summarize these two case studies,

scientists need various source, receiver,

conduit and echoic virtues and laypeople

need various receiver and echo virtues as well.

Moreover, scientists need laypeople to have various receiver and echo virtues.

So that means that there's a second order requirement for scientists.

Similarly, laypeople need scientist to be trustworthy.

Otherwise, they really should be skeptical of everything that scientist have to say.

And that means that there's a kind of interdependency among scientists and

laypeople and that no one particular group can be

held responsible for failures of scientific communication.

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