When Greece was sucked into the financial crisis in 2008 and later was on the verge
of defaulting on its loans,
many economists weighed in on strategies to recover.
They used data to advocate for their plans including various cuts to government spending.
But a typo in a spreadsheet rocked the economics world when a mistake in one of these influential
studies was uncovered by a graduate student.
A few countries were accidentally excluded in a pivotal calculation, and the fix completely
changed the findings.
That’s one big oops!
Researchers are people too: we all make mistakes.
We hope that a typo doesn’t hurt anyone, but when we’re working on big problems,
innocent mistakes unfortunately can have big consequences.
On the bright side, since the economists shared their work with others transparently, the
mistake was ultimately found.
Some data mistakes we forgive, others we’re less quick to forget.
And occasionally mistakes aren’t so much mistakes at all as just, you know, straight-up lies.
I’m Jessica Pucci and welcome to Study Hall: Data Literacy presented by Arizona State University
and Crash Course.
There’s a lot of conflicting information out there in the world — even put out by
the same person or group!
In research, one day you’re in, and the next you’re out.
This almost constant new information is actually part of the beauty of science: it’s an iterative process.
One study doesn’t provide the final answer on any big scientific question.
Researchers act on the data and information that they currently have, even if they know
it isn’t complete.
This means that researchers update their findings as more information comes in or as they get access
to new data.
The biggest data mistake we can make here is being too stuck in our ways, rather than
continuously learning.
Like at the start of the COVID-19 pandemic in 2019, it took time and multiple studies
to figure out how the novel virus spreads.
Different symptoms were revealed at different times.
Distancing and mask-wearing recommendations changed as new research was done.
Still, many, many estimates of how prevalent the virus is and its transmission rate in
various countries continue to make their way into the news.
And data concerning the long-term effects will only be revealed months or years later
As users of these findings, we need to be willing to stay tuned and update our knowledge.
We can stay vigilant for poor research methods while admitting that just because a result
changes over time,
that doesn’t necessarily mean the original research was bad.
It’s a lot to be constantly taking in new facts and recommendations.
But there are a couple of concrete things you can do to keep track of all of that unfolding research.
First, you can get your news from reliable sources with good track records for talking
to established experts and synthesizing that information well.
In articles in these outlets, the evidence a journalist uses is clearly relevant to the
topic and comes from someone whose background shows they’re an expert.
Suppose someone with a PhD in astronomy is quoted about something to do with stars.
Astronomy, stars — no red flags yet.
But if they’re commenting on a diabetes treatment, we need to dig deeper.
Organizations that have editing or fact-checking teams also get a bonus reliability badge since
they have an extra system in place to catch misleading information as stories are written.
But we also need to be careful about the role of our own beliefs.
Reliability doesn't mean something lines up with our own views.
Part of being data literate means being willing to update our views based on new evidence
or information,
not just blindly trusting results we already agree with.
Second, we can do our own fact-checking by using lateral reading and reading “across”
sources to help us evaluate whether a particular source is credible.
If a particular finding is cited by several outlets or is endorsed by a professional society,
then we have evidence the finding has been vetted by experts.
In the COVID-19 case, the Johns Hopkins Coronavirus Resource Center brings together up-to-date
data on cases, deaths, and testing on its website.
Similarly, the World Health Organization consolidates advice for the public and for leaders across
the globe.
We can always double check if they agree with the latest COVID report we’ve read.
The urgency of flashy headlines and the latest crisis as we read through news topics can
make us vulnerable to organizations or people who intentionally spread false information.
When we’re tempted to react based on an alarming story, we should pause and take a breath.
Deep breath...
This is another opportunity to exercise our healthy skepticism and fact check against
multiple sources.
And yes, researchers (and journalists) make mistakes, especially if they’re under a
time crunch.
We should point out mistakes — like commenting on a Study Hall video when we get a fact wrong
so we can fix it —
but we can remember the team is only human and be empathetic.
If researchers and journalists are transparent about their mistakes, we can give them the
benefit of the doubt.
But, unfortunately, there's the occasional researcher who responds to financial or career
pressure by committing data fraud.
For example, in 2005, workers raised the alarm that an oral cancer study done by a researcher
who worked at their hospital seemed fishy.
The patients in the paper were later found to be completely made up.
This led to a further inquiry into the researcher’s other papers and it turned out that even his
dissertation had made-up data.
So how is data fraud revealed?
Sometimes it just involves going to the data source.
Like the two Berkeley graduate students studying political persuasion who uncovered a completely
made-up study in 2015.
When they tried to use the same methods and contacted the survey company that had supposedly
helped the researcher, the survey company had never heard of him.
Other times it takes a little more digging.
Let’s see another data detective at work in the Data Point.
I bet you didn’t think of a data scientist as a fraud detector, but there are plenty
that play that exact role.
In fact, someone just gave this data fraud detector expert a tip that the latest cancer
study from Dr. Fibsalot might not have as rosy an outcome as its paper claimed.
This whistleblower gave the original spreadsheets of data to the fraud detector,
so now it’s time to investigate.
First they look for anything obvious.
Did any patients mysteriously disappear between the start of the trial and the end?
Maybe their outcomes didn’t make the cancer treatment look so hot.
Wait, this row looks familiar.
Didn’t the patient a little further up have the exact same birthday?
That could be possible, but let’s make a note.
Wait, there’s another person with that same birthday.
That seems weird.
By the end of the spreadsheet they’ve counted 15 people born on October 31, 1979.
Spooky?
Or fraud?
Let’s keep digging.
The fraud detector turns to the data recorded during the study.
A lot of these outcome numbers seem awfully round to be real measurements.
We’d expect to see lots of decimals in something like tumor size, but lots of these numbers,
especially for patients with positive outcomes,
are whole numbers ending in point 00.
And for larger numbers, mathematicians have figured out there are properties that dictate
how many of each number we should expect to see as the first digit.
Even the insurance data for these patients looks weird — too many leading 1s and 2s
in the prices charged and prices paid by insurance claims.
Imaginary bills perhaps?
The fraud detector hasn't conclusively proved anything of course, but this is plenty of
evidence to report Dr. Fibsalot for potential data fraud.
He’ll have to share more findings and patient information to prove he’s legit.
We don’t have to be a formally trained fraud detector to keep an eye out for some of these oddities.
Those grad students and colleagues who discovered real-life data fraud did it because they looked
at the data, paid close attention,
and asked good questions when things looked a little fishy.
So when we’re digging into the data behind a news article and searching for detail on
how the study was designed or possible confounders,
we can also keep an eye out for data that looks too, well, clean.
These “data done bad” stories aren’t the norm, but they do pop up prominently when
they are exposed.
We should trust experts in general — because they’re experts for a reason! — but as
always, healthy skepticism can’t hurt.
Especially because even well-intentioned researchers can still do harm.
Like with algorithm bias, which is when algorithms give results that reflect hidden biases in
society, or give inaccurate predictions based on limited datasets.
Bias isn't inherently bad, it's something we all have.
Yes, me too!
But we have to pay attention to bias because it can lead to treating certain groups of
people unfairly.
So algorithm bias can lead to serious problems related to discrimination.
Take the medical test for how well your kidneys are working.
Since the kidneys’ job is to process waste in our bodies,
the formula used in the test involves measuring either the level of waste product released
from the body or the level that remains in our blood.
But the humans that designed this testing algorithm didn't use only waste measurements.
They also wanted demographic info like age, gender, and race to weigh into the results.
What's okay for an 8-year-old, for example, might be unhealthy for a 90-year-old.
In this kidney-testing algorithm, it’s built in that a Black patient gets points added
to their score based on outdated assumptions that Black patients have higher kidney function.
And this has serious, discriminatory health consequences.
For example, to get on the kidney transplant waitlist you have to get below a certain kidney
function score.
If a Black patient’s score is wrongfully inflated by this algorithm, it can be harder
for them to get appropriate care.
Fortunately, a group of doctors are on a mission to investigate algorithmic bias and improve
medical tests like this one so they stop perpetuating discrimination.
Demographics also play a role in how much you pay for insurance.
The Markup, a nonprofit newsroom that investigates technology, found that an insurance company’s
algorithm that determined pricing per person was producing unfair new rates.
People already paying high rates got an even higher adjusted rate.
And these customers were disproportionately middle-aged, nonwhite, males.
Algorithm bias isn’t limited to just medicine and money.
Many researchers have studied algorithms related to the justice system and found them wanting.
Like an algorithm that supposedly predicts criminal activity has the power to send police
officers to crime “hotspots.”
Any bias is then compounded and we create a positive feedback loop as more arrests are
added to the data from those “recommended” locations.
Training data for other types of algorithms can be biased too.
Like all those algorithms that are used for translation, audio captioning, or even virtual
assistants like Siri and Alexa.
They’re typically trained on language data from “classical sources” like the Bible
or standard English over more varied dialects.
You may have heard the phrase “garbage in, garbage out.”
Well the same is true of bias.
If training data is biased, outcomes are likely to be biased.
In the policing scenario, bias leads to a self-fulfilling prophecy where you find crime
wherever you happen to be patrolling, fair or not.
And in the language case, bias leads to tools working less well for those who don’t speak
the dominant language or style.
We can put pressure on researchers to use representative training data,
algorithm builders to reveal the inner workings of algorithms that affect people’s lives,
and users of these algorithms to audit them for fairness.
Researchers have a lot of responsibility, but we do too.
Using our data literacy skills to look out for data mistakes can help stop unexpected,
harmful consequences of how data is used.
Today we learned about research as an iterative approach.
Some updates in our knowledge are good; we’re acting on new information and updating our
sense of the truth.
Other data changes are the results of mistakes.
We learned about intentional data mistakes (like fraud) and unintentional data mistakes,
like biased algorithms that have unfair outcomes.
Either way we must keep an eye out for when data can be used to harm others.
Next time we’ll take a step back and see how our newfound data literacy fits in with
other more traditional literacies.
Thanks for watching Study Hall: Data Literacy, which is produced by Arizona State University
and the Crash Course team at Complexly.
If you liked this video and want to keep learning with us here in Study Hall, be sure to subscribe.
You can learn more about ASU and the videos produced by Crash Course in the links in the description.
See you next time!