Tag: data science

Understanding the data will help us understand the danger

Dr Linda McIver

In Australia right now, both Sydney and Melbourne are battling outbreaks of the delta variant of covid, and wondering why it just doesn’t seem to lie down and die the way the virus did in Australia last year. There are many reasons for our current problems – too, too many of them political – but a lot of it comes down to the R0 value, or how contagious this variant is.

Last year’s variant was not nearly as contagious – some estimates say delta is three times as contagious as that one – and so it was easier to put down. But what is R0, and why do we care?

R0 is known as the “basic reproduction number”, and simply represents how many people an infected person will infect. So an R0 of 2 means that each person with covid spreads it to 2 other people. That means the infection doubles at every step, and that’s bad. 1 person infects 2 others, they spread it to 4, who spread it to 8, etc. At the 6th step, we’re already at 64 new people being infected. Left unchecked, at the 10th step we’re at 1024 new people infected.

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1024

Delta has an estimated R0 of 5. Every person with delta will likely spread it to 5 other people. That means 1 case becomes 5, those 5 infect 25 more, those 25 infect 125 more, and it escalates alarmingly fast. Left unchecked, at the 10th step we’re at nearly 2 MILLION new people infected.

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If we graph the first 6 steps, we can see how an R0 of 2 compares with an R0 of 5.

New infections for R0 of 2 versus R0 of 5. R0 of 5 is a red line jumping sharply upwards, to over 3000 by step 6. The blue line for R0 of 2 barely registers on the graph, it is relatively low and flat.

Or, to put it another way, check out these little coronaviruses. Each step is 5 times the step before.

5 steps of transmission of the delta virus, with an R0 of 5. Each little covid image represents one new person infected

The good news is that we can effectively modify the R0. The delta variant’s R0 is only 5 if people are interacting in a way that allows the virus to jump from person to person. This is why contact tracing can be so effective – if we can identify all of the people who have had contact with an infected person BEFORE they become infectious themselves, then we can prevent the virus from spreading any further. If we can drop the R0 to under 1, meaning that, on average, people infected with delta are infecting less than one other person, then we can get it under control.

Contact tracing works if we can identify every single person an infected person has been near enough to to transfer the virus. The trouble is that delta makes that jump so quickly and easily that contact tracing needs to identify everyone an infected person has walked past in the street, especially if they were unmasked. This is obviously not achievable. Plus, even the most dedicated of us sometimes forget to check in, so that is not 100% reliable either.

Wearing masks helps, but only if they are properly fitted, worn properly, and washed or replaced regularly.

This is why we need strict lockdowns. Because lockdowns reduce the number of people we each come into contact with, thus effectively reducing the R0, and limiting delta’s opportunities to spread.

If we don’t get the R0 down, we can’t control the spread of the virus. It’s as simple as that.

And if you’re wondering why I stopped my little covids at step 5 in the picture above, it’s because step 6 is too big to fit on the screen.

Step 6 is 3125 new cases – too many to display onscreen (this is not the full set)

Once again, our safety relies on our leaders (and the public) understanding exponential growth. It’s not actually complicated, but few of us have ever needed to know how it works. Now, more than ever, we need to build our data literacy, so that we can understand the danger we are in.

For more reasons why we need to be data literate, and how to teach our kids to change the world, check out Raising Heretics, pre-order now, or buy online in all the usual places from August 1st.

Vaccine hesitancy is a logical consequence of the way we teach science.

Dr Linda McIver

Vaccine hesitancy could literally kill, yet it’s a logical consequence of the way we teach science.

We tend to think Science is about facts and right answers. This is absolutely the way we teach it, but it’s the opposite of what Science really is. We learn the periodic table, the arrangement of subatomic particles around a nucleus, the equations for force and motion, and how to name the components of a cell. We teach with experiments where known inputs are treated with a known process, producing a known outcome. Kids who don’t get the “right” answer either fake their results or copy from their neighbours. This is not an education in Science, it’s an education in confirmation bias – in seeing what we expect to see.

Science is actually a way of exploring and understanding the world, and of solving problems. By its very nature science deals with uncertainty, and constantly proves itself wrong as new information becomes available.

Scientific theories are based on the information we have right now. Sometimes we can’t see, measure, or understand enough to explain a phenomenon fully, but we have a model we think is right, and it’s right enough to help us understand some parts of the way the world behaves.

We can see this in the way our understanding of covid19 has evolved. At first we thought it was transmitted by droplets, so that unless you were in the direct path of someone’s sneeze or cough, the main risk was touching an infected surface. As we learned more, our understanding developed. We now know that it is very easily transmitted by aerosols – in other words, virus particles can hang in the air in such quantities that we easily breathe them in and become sick.
This explains why transmission rarely happens outdoors, and why ventilation is key when we’re indoors. It also explains why hotel quarantine is so problematic – because even if there is no air transfer between rooms, an infected person walking through a corridor can leave that corridor so contaminated that it’s infectious for some time afterwards. It also explains why masks (when properly worn) are so effective at preventing transmission.

We’ve also seen our understanding of vaccines and their side effects evolve. And the fact that the story keeps changing – from “Astrazeneca is safe for everyone” to “it’s safe for everyone over 50” and now “it’s safe for everyone over 60” – makes people nervous. But it’s this rapidly changing information that should give us comfort and confidence. This is Science doing its job – adapting our understanding according to new information.

I once interviewed Cameron Neil, who at the time was head of the Fair Trade Association of Australia and New Zealand. We were talking about the fact that it’s hard to buy ethically, because the information we have keeps changing. Neil’s response is an ethical approach to consumption, but it also encapsulates an intelligent approach to Science: “With the information available to me today I make the best choice I can, knowing full well that I may get information tomorrow that means the choice I made was the wrong one, and I’ll have to do better next time.”

When it comes to vaccines, of course, we crave certainty. No-one wants to take something that might harm them. We want to know with absolute clarity what the best thing is that we can do for our health. The fear and uncertainty in the community around the Astrazeneca vaccine is palpable. Yet according to Hassan Vally, an epidemiologist at La Trobe University, the risk of dying from a blood clot due to the vaccine in Australia is 0.5 per million, while the risk of dying in a car accident in Australia in any given year is 28 per million. Compare that with the risk associated with taking aspirin or other non steroidal anti-inflammatories (NSAIDS), which is 24.8 deaths per million people, or a staggering 153 per million users of those drugs. This is where a different understanding of Science could help us.

If we truly understood how Science worked, the rapidly changing information would give us confidence that our understanding was getting better and better. If we taught Science as an exploration of the unknown, and a constantly developing set of theories, rather than a fixed set of hard facts, we would be far better prepared to understand the constantly evolving picture of covid19 and its vaccines.

It’s really hard to teach kids critical thinking skills when your toolkit is questions that all have right answers, curricula full of facts and straightforward procedures, and textbooks that leave kids floating on an uneasy sea of factoids, memorisation, and perfectly neat examples tied up with a bow.

Imagine if we taught Science by exploring the world. By trying to solve problems that have no textbook answers, where students have to rigorously test and evaluate their own work (and the work of others) in order to be confident of their results, because they can’t just look up the answer, and have the teacher mark it right or wrong.

If we grew up with this basis, knowing Science as an evolving, developing discipline, rather than a bunch of facts pinned to the unchanging pages of a textbook, we would know that our changing understanding of covid19 and its vaccines is not a threat. It’s what’s keeping us safe.

Read more about the way we teach science, and about teaching our kids to be critical thinkers, in Raising Heretics: Teaching Kids to Change the World.

Teaching STEM is more important than robots

Dr Linda McIver

This is an excerpt from Raising Heretics: Teaching Kids to Change the World, which is due out on August 1st.

I founded the Australian Data Science Education Institute in 2018 because I wanted to show kids that they are capable of working with technology, that it is relevant to them, and that they don’t have to look like Sheldon from the Big Bang Theory in order to learn to program.

It’s well known that the technology industry has a diversity problem when it comes to women, but lack of diversity goes way beyond gender. By trying to increase the number of women and girls in STEM, we are only tackling the easy part – though it’s actually not that easy, judging by the sheer volume of women in STEM programmes and the persistently stubborn failure of the numbers to actually shift.

The problem is that we consistently attract the kinds of people to tech that are already there. We are missing big chunks of the population – boys included. Boys who don’t see themselves as nerdy, or who don’t see the point of tech. Girls who don’t see it as relevant to them. Non binary and gender queer kids who don’t see themselves as represented or welcome in any of the tech programmes available to them.

If we had true diversity in technology and Data Science, we’d have a range of ethnic and cultural backgrounds, as well as people with a wide range of physical abilities. We’d have people on our design teams that are mobility compromised, vision impaired, with allergies, with varied gender identities and sexualities, with every possible skin tone and body shape. We’d have people who act differently, dress differently, think differently, and have different needs. I have headphones that don’t work well with long hair, for goodness’ sake! Guess who was on that design team?

This lack of diversity is bad for the technology industry, but it’s even worse for the rest of us, because technology is changing the shape of our world at an alarming rate, and we currently have very little say in our own future. Companies like Uber and Doordash are radically changing our working conditions and eliminating hard won entitlements and protections, while Facebook and Youtube spread misinformation and encourage radicalisation, all in the name of keeping people on their platforms and maximising their profits. Our world is being directly shaped by technology companies that are working in ways we don’t understand and have no control over.

Meanwhile we see human resources companies using AI to filter job applicants, claiming that their system eliminates “human bias”, without admitting the possibility that it introduces new forms of machine bias. We see “predictive policing” algorithms being used to predict crime and target particular communities in disturbing ways. We see a rush towards machine learning and artificial intelligence systems for their own sake, rather than for problems they can legitimately solve, and we have a wholly unwarranted confidence in the accuracy, reliability, and objectivity of their output.

It turns out that diversity in the technology industry is only a small part of the reason why teaching all kids Data Science and STEM skills matters. The big part is that we need a technology and data literate population who are trained to think critically and creatively, and, in particular, trained to believe that they can solve problems. That’s the world we need to build. And the foundation stone of world building has to be education.

We have a choice. We can train kids to be obedient process followers who don’t rock the boat, or we can train them to be challenging, critical and creative thinkers who ask difficult questions and come up with innovative solutions to our worst problems.

Above all, we need people who are prepared to be heretical.
Who ask “why?”
Who ask “how can we be sure?”
Who ask “what have we missed?”
Who ask “how can we do better?”
Who ask “who are we hurting?”
Who ask “how can we fix this for everyone?”
Who ask “how will we know how well it works?”

These questions are often heretical. By asking them, I’ve sometimes made my bosses very unhappy. They make people uncomfortable. But they are crucial to building an ethical, sustainable, positive future for all of us.

___

Pre-order Raising Heretics now:

https://events.humanitix.com/raising-heretics-pre-orders-and-book-launch

How far we’ve come

Dr Linda McIver

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In February 2018 the Australian Data Science Education Institute was born. The last two years have been a whirlwind of speaking tours, teacher workshops, and resource development. I’ve barely had time to look around. So it’s time to celebrate everything we’ve achieved. We planned to celebrate our second birthday, but a little distracted by everything we had going at the time, our celebration was scheduled March. Like many events, the celebration was cancelled by covid 19. And, like a lot of educational organisations, March and April were spent frantically adapting materials for online learning.

Now that online learning has begun, and ADSEI has some breathing space, it seems like an excellent moment to pause and reflect on how far we’ve come in the last two years.

As Founder and Executive Director, in 2017 I reluctantly decided to leave classroom teaching to achieve my larger goal of ensuring that every student has the opportunity to become data and technology literate, and to develop data science skills in the context of authentic projects with real world impact. I looked around to find the organisations working in this space so that I could join them and make this happen.

I looked.

And I looked.

And I looked.

Eventually it became clear to me that there was no organisation with population-wide data literacy as its primary goal, and so the Australian Data Science Education Institute was born. I was told that small organisations could not be successful, and what I was doing was hopeless. But it seemed to me that what I was doing was vital.

I’ll admit, I imagined that technology companies in Australia would fall over themselves to support the enterprise, which is not the way it has worked out. In ADSEI’s first year we were awarded a grand total of none of the grants we applied for – not surprising, since we had no track record at the time. Despite that, in that first year we achieved a lot.

In April of 2018 I did a speaking tour, thanks to YOW! Conferences, in Brisbane, Sydney, and Melbourne, to talk about our mission.  In May I delivered an invited talk at YOW! Data, called Kids Can Change the World with Data.

I started working with the Education Group at CSIRO to develop the Educational Datasets lesson plans, working with CSIRO scientific datasets to create Data Science projects that could be run in schools.

I worked with Martin Richards at Educational Services Australia to develop Data Science projects for the Digital Technologies Hub, based on data from the West Australian Marine Science Institute, together with Luke Edwards from Pawsey Supercomputing Centre. We did lessons with data on Humpback Whales and Turtles, and there’s one still coming on Dugongs. I’ve also developed projects for Digital Technologies Hub on Renewable Energy, Biodiversity and Mini-beasts, among others.

I’ve championed Data Science on Q&A, including a bonus question on sugar, and delivered a keynote for the Education track at Pycon AU. I’ve had an opinion piece in Fairfax newspapers, a profile piece in the Australian Financial Review BOSS magazine, and been a guest on ABC Radio Sydney, Stemiverse, Fizzics Ed, and PonderingDan Podcasts. I’ve run a CSIRO STEM Professionals in schools webinar on Data Science Education, a panel for YellowFin BI, and other talks that weren’t recorded.

Going very meta, in April 2019, I taught Pawsey Supercomputing Staff how to teach teachers about Data Science and STEM.

I ran teacher Integrated STEM and Data Science workshops with the support of Google Australia in Melbourne hosted by Thoughtworks, and in Perth hosted by Pawsey Supercomputing Centre.

I joined forces with Science Under Sail to run teacher workshops in regional Queensland, in Bundaberg, Innisfail, and Mackay, on Seagrass Citizen Data Science, thanks to a grant from the Great Barrier Reef Foundation.

I’ve worked with the Victorian Department of Education and Training to develop 8 Connected STEM packages for Foundation to year 10, covering topics as diverse as schoolyard litter, mindfulness programs, conservation, solar energy, and evaluating the quality of science journalism. When Covid19 hit and schools started teaching online, I adapted 6 of those 8 packages for remote teaching (they should appear on the Victorian Education Department’s FUSE website shortly, in the meantime if you would like a preview so you can give me feedback, send an email!). These resources have supporting videos to make them easier to deliver.

I’ve developed data science explainer videos around the data of covid19, including ones on Exponential Growth, Why we need to flatten the curve, how the death rate is calculated, and other timely topics, which you can see on my Youtube Channel.

I’ve delivered talks about the Sceptical Data Scientist, a keynote on Raising Heretics, for the NSW ICT Educators Conference, a talk at Hopper Down Under about Using Data Science to Engage Kids with STEM, and countless talks at meetups and conferences around Australia.

I’ve worked directly with schools, giving talks, running workshops, and developing curriculum. I’ve talked with countless businesses about why Data Science Education is crucial. And I’ve started writing a book – my manifesto! – about exactly that.

That’s just an overview of the last two years, but it’s a lot. We still have an enormous amount to achieve, but we’ve come a long way from the blank canvas we had at the beginning.

You can support us by donating at givenow.com.au/adsei, and you can download resources from the ever increasing set on this website.

Share our resources, like and share our videos, talk about us on the socials, and join in our Data Science Education hack days (a virtual hack day coming soon!) to help us develop materials to engage kids with Data Science and Technical skills.

Our long term goal is to put ADSEI out of business by ensuring that this is the way teachers teach and curricula are developed, but we have a long way to go.

We are so grateful to the organisations listed above who have supported us on our journey.

Our society’s response to the pandemic in which we find ourselves has made it incredibly clear that we need a data literate population in order to chart our future in these extraordinary waters. ADSEI is dedicated to making that possible.

Raising Heretics

Dr Linda McIver

This is the text of my keynote from the NSW ICT Educators Conference in Sydney earlier this year.

I am Dr Linda McIver, founder and ED of the ADSEI, a registered charity dedicated to putting students in the driver’s seat of our data driven world.

Today I want to talk to you about the importance of heresy.

I’m going to take you through the place of heresy in science, as well as the phenomenon known as survivorship bias, and how these relate to the extraordinary claims being made in the field of AI, and then I’m going to talk about how I’m aiming to fix it all.
Much of our Science, Technology, Engineering, and Maths education starts from a foundation of facts and known answers. This teaches our kids that the point of STEM is to Get The RIght Answer, whereas the actual point of real world STEM disciplines is to fix things, understand things, and solve problems. In this talk I will show why I founded the Australian Data Science Education Institute, why we’re dedicated to raising Heretics, and why Heresy is something we desperately need right now, both in the Data Science industry and the world as a whole.

First of all, let’s define our terms. Heresy is an opinion profoundly at odds with what is generally accepted. And heresy has been crucial to our scientific development.
Let’s talk about some historical scientific heresies:

  • In the 1840s Ignaz Semmelweis came up with the radical heresy that doctors washing their hands before (and after) surgeries prevented disease. Prior to this doctors went from autopsies to childbirth without washing their hands or even changing their clothes. And they wondered why people died. The idea that this could cause disease was considered so ludicrous that it took decades for the idea of washing hands to be accepted. By the way Semmelweis was so ridiculed and pilloried that his colleagues committed him to an asylum where he was beaten and died.
  • In a well known heresy, Galileo Gallilei so outraged the church with the idea that the earth revolves around the sun, rather than the other way around, that he was accused of literal heresy and committed to house arrest. He only narrowly escaped death.
  • In 1912 Alfred Wegener began to publicly advocate the idea that the continents moved over time – what became known as continental drift. He, too, was widely ridiculed, and did not live to see his ideas finally vindicated.
  • More recently, Marshall and Warren’s original paper on ulcers being caused by bacteria rather than stress was rejected and consigned to the bottom 10% of submissions. Barry Marshall eventually drank helicobacter pylorii – the bacteria that causes ulcers – to prove it, thus inducing an ulcer which he then cured with antibiotics.

It seems like heresy is a pretty dangerous business!

In fact a lot of scientific breakthroughs have been considered heretical. Especially in medicine!

Now let me digress for a moment to tell the story of the WWII planes that were examined for bullet holes to work out where to armour them. Researchers figured that the places with the most holes – the wings and the fuselage – needed the most armour. They found no holes on engines or fuel tanks so they figured they didn’t need armouring… until statistician Abraham Wald pointed out that the planes they were studying were the ones that made it back. The planes needed armour where none of the planes had holes, because clearly the planes that had holes in those other places (the engine and the fuel tanks, btw) were the ones that DIDN’T COME BACK.

I love this story, because it’s a classic example of the obvious conclusion being dead wrong. In similar results, the introduction of helmets in WWI resulted in more head injuries being treated in the field hospitals, so the first reaction was “stop using helmets!”… what data was missing?

Both of these are examples of survivorship bias – where there is a chunk of data missing from the study. In these cases it’s literally survivor bias because it fails to take into account those who don’t make it back.

Have you heard of HireVue? They’re a Human Resources Tech company that uses artificial intelligence to select or reject candidates in job interviews based on… well, nobody actually knows.

They say it’s a machine, therefore it’s without bias. And we could laugh and snort, but over 100 companies are already using it, including big companies like Hilton and Unilever.

According to Nathan Mondragon, HireVue’s chief industrial-organizational psychologist, “Humans are inconsistent by nature. They inject their subjectivity into the evaluations, But AI can database what the human processes in an interview, without bias. … And humans are now believing in machine decisions over human feedback.”
Which is really just all kinds of disturbing when they make all sorts of claims for it, but can’t actually explain how it is making its decisions.

They say that the system employs “superhuman precision and impartiality to zero in on an ideal employee, picking up on telltale clues a recruiter might miss.”

Of course, HireVue won’t tell us how their algorithm works – in part to protect trade secrets, and in part because they don’t really know…

I am fairly confident I’m not the only one who finds that an incredibly disturbing idea.

Luke Stark, a researcher who studies emotion and AI at Microsoft, describes this as the “charisma of numbers”. If an algorithm assigns a number to a person, we can rank them objectively right? Because numbers are objective. And simple. What could possibly go wrong, reducing a complex and multifaceted human being to a simple numerical rank? (Helllooo ATAR – Australian Tertiary Acceptance Rank…)

I think Cathy O’Neil sums it up beautifully: Models are opinions embedded in mathematics, and algorithms are opinions formalized in code. It’s incredibly important that we dispel this pervasive myth that algorithms are unbiased, objective statements of truth.

This whole HireVue system is a textbook example of survivorship bias: looking only at the people who made it through the same hiring process that we are now calling fatally flawed… and thinking we can predict ideal new hires with only that data. It completely ignores the people who didn’t make it through the initial processes, who might have been amazing.

It also highlights an issue I’ve seen raised again and again in works like “Weapons of Math Destruction”, “Made by Humans,” and “Automating Inequality” – that people believe in numbers, computers, and algorithms over other people, even when they’ve been explicitly told those systems are broken. And I have a story about that, too.
Niels Wouters, a researcher at Melbourne University, some years ago designed a system called Biometric Mirror. It was a deliberately simple, utterly naive machine learning system that took a picture of the user’s face and then claimed to be able to tell a whole lot about the person, just from that picture.

The system spat out a rating of ethnicity, gender, sexual preference, attractiveness, trustworthiness, etc. And Niels created the system to start a conversation with people about how transparently ludicrous it was to believe a system that does this. So he set up booths where people would come, have a photo taken, and read all of this obviously false information about themselves, and then have a conversation about trust and ethics and the issues with Artificial Intelligence. So far so good. A noble goal. But there are two postscripts to this story that are horrifying in their implications.

First of all, Niels would overhear people walking away from the display, having had the conversation about how obviously false the “conclusions” drawn by the system were, saying “But it’s a computer, it must be right, and it doesn’t think I’m attractive…”

And secondly, after speaking publicly about all of the issues with Biometric mirror, Niels was contacted by HR companies wanting to buy it…

So here is where we start to make the connection between education and the tech industry.

One of the problems in Data Science is that we often don’t have a lot of time to challenge even our own results, never mind anyone else’s. The rush to data riches (Step 3, Profit!) means we don’t really have time to be cautiously sceptical. We get a result, report it, and move on to the next dataset. And people are all too willing to believe in those results.

When I asked a group of data scientists if they had ever had to release/report on a result that they felt hadn’t been fully tested, that they couldn’t bet their lives on, around half put the hands up. And then when I asked how many hands would have gone up if it had been anonymous, the other half put up their hands.

So all of the discoveries I mentioned in the first half of this talk were made by people being sceptical. Challenging the status quo. Questioning accepted wisdom. By people who were quite prepared to examine new evidence and consider that “what everybody knows” might be wrong. Of course, we need educated heretics, so that our scepticism is rational and fact based, rather than denialism and wishful thinking, which is what we are seeing quite a lot of now. So education is clearly key.

But let’s consider STEM education. We mostly teach Science, Technology, and Maths in schools as a matter of facts and known outcomes (and, yes, I know there’s one more letter in STEM, but we rarely, if ever, actually teach any Engineering) .

Consider the average school Chemistry experiment. We take known substances, apply a known process, and achieve an expected outcome. What do kids who don’t get the results they expect do then? Do they go back and try to find a reason for their results? Do they ask questions and challenge outcomes?

Nope. they don’t have time for that, and they get no credit for it. They copy their friends’ results. Or they simply adjust the results to get the outcome they expected to get. Marks are allocated for the expected results. For the right graph.

Occasionally we’ll run a prac with unknown reagents and ask the students to identify the inputs. But here, again, marks are for the correct answer.

But this isn’t science education. This is an education in confirmation bias. In finding what you are supposed to find. In seeing what you expect to see. It is the exact opposite of the way science should work. Science should be about disproving theories. And you only accept a theory as plausible when you have tried your hardest to disprove it, and failed.

Maths is much the same. The emphasis is on correct answers and known outcomes. On cookie cutter processes that produce the same result every time.

Technology education is often even worse. With a severe shortage of teachers with programming skills, we tend to default to education using toys. Drawing pretty pictures. Making robots follow lines. Writing the same code. Producing the same output.

What if we could teach with experiments where we don’t know the answers?
Well with data, we can easily do that. Can we find a dataset that hasn’t been fully analysed and thoroughly understood? I could probably hit a dozen with a bread roll from where I’m standing.

How do you mark it, then, when you don’t know the right answer? You mark the process. You mark the testing. You ask the students to test and challenge their answers really thoroughly. You give points for their explanation of how they know their answer is right, for how they confirmed it by trying their hardest to prove it wrong.

It has been said, most famously by Grace Hopper, that the most dangerous phrase in the English language is “we’ve always done it that way”. Now, more than ever, we need people who challenge the status quo, who come up with new ideas, who are prepared to be heretical.

By teaching Data Science in the context of real projects, where the outcome isn’t known, we can actually teach kids to challenge their own thinking and their own results. We can teach them to think critically and analyse the information they’re presented with. We can teach them to demand higher standards of validation and reproducibility.

The trouble with this is that it requires a significant amount of setup work. Finding the datasets isn’t hard, but making sense of them can be really challenging – for example when I downloaded a vote dataset from the AEC and tried to find someone who could explain to me how the two dimensional senate ballot paper translated to a one dimensional data string, I literally couldn’t find anyone at the AEC who knew. I mean… presumably there is someone! But I couldn’t find them. It took me hours and hours to make sense of the dataset and design a project that would engage the kids, and give them room to stretch their wings and really fly.

The only reason I was able to commit that kind of time is that I was only teaching part time, so I used my own time to build these engaging projects. In year 10 we did projects on climate, on elections, on microbats. In year 11 we worked with scientists to solve their computational and data needs, in fields like marine biology, conservation ecology, neuroscience, astrophysics and psychology. The possibilities are truly endless.

But a teacher with a full time load doesn’t have the capacity to take on that kind of extra work. It’s just too time consuming, even if they have the skills to start with.

So that’s why I created the Australian Data Science Education Institute, or ADSEI. To develop project ideas and lesson plans that empower kids to explore data and become rational sceptics. To develop their data literacy, critical thinking, and technical skills in the context of projects they really care about. And also to provide professional development training to teachers right across the curriculum – not just in Digital Technologies – to integrate real data science into their teaching. To use data to make sense of the world.

At ADSEI we have created projects where kids use real datasets to explore the world. To solve problems in their own environments and communities, and most importantly: to measure and evaluate their solutions to see if they worked. We’ve got projects that do things like:

  • calculate how much carbon is embodied by the trees on their school grounds and then do various comparisons of the school’s carbon emissions from electricity.
  • construct a set of criteria for good science journalism and then evaluate a bunch of different sources according to those criteria and visualise the results
  • analyse the litter on the school grounds, find ways to fix it, and then analyse it again to see if they worked
  • record and analyse the advertising they see around them in a week and explore its impact on their behaviour
  • use solar energy production & power usage data to explore a household’s impact on the environment
  • use the happiness index data to explore world differences in measures like income inequality and social support
  • use data from scientific observational studies to learn about whales, turtles, climate, and more

When I was teaching Computer Science at John Monash Science School in Melbourne – a school for kids who are passionate about science, who you might be forgiven for assuming were already engaged with tech – we started by teaching them with toys. We had them draw pretty pictures, and program robots to push each other out of circles. And the number one piece of feedback we got was “This isn’t relevant to me. Why are you making me do this? I’m never going to use it.”

When we shifted to teaching the same coding skills – variables, if statements, for loops, etc – in the context of Data Science, using real datasets and authentic problems, that feedback disappeared and instead we heard “this is so important. This is so useful. I’m using this in my other subjects.” and the number one thing I live to hear when teaching tech: “I didn’t know I could do this!”

So not only does teaching tech skills in the context of data science teach the kids that STEM skills empower them to solve problems and find out more about their own world, it gives them the motivation to succeed. To actually learn the skills and put them to good use.

And make no mistake, motivation is the single most important factor in learning.

So Data Science empowers students to learn technical and other STEM skills in the context of real problems. It gives them the capacity to create positive change in their own communities – and to prove that they have. It teaches them to communicate their results.

And most importantly, it teaches them that this is something they can all do.

And that point is crucial, because at the moment we have hordes of students – even at a high performing STEM school like John Monash – believing that tech is not something they can do. Not something that interests them. Not something that’s relevant to them.

Which means that we are continuing to get the same kinds of people choosing to go into tech who have been choosing it for decades now. We are actively perpetuating the stereotypes, because those stereotypes are now so strong that everyone believes that only those types of people should or can go into tech.

One of my friends who works in data science recently met someone who, on learning her occupation, literally said to her: “You work in tech. So, are you on the spectrum?”
Because if I ask you to picture a computer scientist, or a data scientist, chances are you will imagine a young white male who is on the spectrum.

Current figures suggest that women make up as little as 15% of the Data Science industry.
And it’s lack of diversity in the tech industry that leads to systems like the HireVue AI – because there are not enough voices in the room prepared to say things like “Um, have we really thought this through?” or “What are the ethical issues with doing that?”

It also leads to tech solutions that work beautifully for the types of people represented on the development team, but that have serious limitations for everyone else.

And lest you think that women simply aren’t cut out for tech, and there isn’t actually any bias in the field, allow me to remind you of the 2016 study of open source code on github that found that code submitted by a woman was more likely to be accepted than code submitted by a man, but only if the woman’s gender was not identifiable from her github id.

ADSEI’s work isn’t going to turn every student into a data scientist. But it will give kids the option of being data scientists, who wouldn’t have had it otherwise. Because they will understand the power of data science, and they will know that it’s something they can do. And that is phenomenally empowering.