[MUSIC PLAYING]
DI DANG: I'm Di Dang, a new host on "Centered."
My focus is how to design with emerging technologies,
like artificial intelligence.
And we're in Toronto today to talk to BenchSci,
a startup using AI to help research scientists discover
new drug treatments faster and more efficiently.
So let's take a look.
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Matan, thank you for being here.
So tell me about BenchSci.
MATAN BERSON: So BenchSci is trying
to solve a particular problem for scientists
in the biomedical and research space.
So what BenchSci is doing is we've got our AI technology
to read through the millions of papers
out there in the scientific literature,
just like a PhD biologist would, and show
which antibodies have worked successfully in experiments.
There's always papers coming out.
The AI technology is able to scale across all the papers
that we have access to, read through and take out
that data, and the relationships of the data within the papers,
and make it presentable to scientists.
We try and present things as objective as possible
so that the scientist makes the decision.
DI DANG: It's not the AI making the decision.
It's you, the scientist, the end user making the decision.
MATAN BERSON: Yeah.
Using the AI more as a tool to help them
find the literature that they need.
And again, it's still down to them
to make that decision of, oh, does this antibody look good
in this experimental context?
Will this work for me?
They're trying to understand essentially,
can they trust the data that is being presented to them?
DI DANG: When working with AI, it's
critical that you understand your user's mental model.
In other words, understand how they
think that AI-driven product or feature works so that it
can make design decisions that support
that evolving mental model.
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VIOLENA PIETROBON: My name is Violena,
and I am a postdoctoral researcher
at the University of Toronto.
So I work in cancer research.
So we are trying to optimize drugs.
We are trying to understand why certain drugs work better
than the other.
DI DANG: How does BenchSci enter the picture?
What do you use BenchSci for?
VIOLENA PIETROBON: It's very quick,
because the search is already performed by this platform.
So I don't waste time, and I just check all these papers.
So what I love is to get all the information in a small space.
So the layout is very efficient because it provides me
all the formation I need.
I just click here, and I literally--
I get everything I need.
I check the quality of the image.
I check the publication, the authors, the titles,
if I want to read more.
And if I'm interested, I already have the name of the product
here, just here.
So you can see, in one screen, there
is the entire information.
Yes, there is everything.
DI DANG: How do you think BenchSci impacted
your work in other ways?
VIOLENA PIETROBON: I can investigate other aspect
that I would not even have considered before,
because it was not possible in terms of time.
And this is very important, because science
is made by discovery, and sometime, the discovery--
they come from small things, or things that you cannot even
imagine.
DI DANG: Unexpected.
Yeah.
VIOLENA PIETROBON: Exactly.
You don't imagine it.
DI DANG: BenchSci uses artificial intelligence
to search, extract, and display the relevant information
for Violena's research work.
In other words, AI augments the capabilities of researchers
so that they have more time to focus on what they do best--
creating drug treatments that work.
Why is artificial intelligence, why
is machine learning a good fit for solving this problem?
JEREMY SEATH: When I think about why AI is a unique way
to tackle the problem that we're solving,
I think it's the only way to tackle the problem that we're
solving, and it's really an issue of scale.
DI DANG: Totally understandable that these research scientists
would have a lot of concerns around how
much to trust the results that they're
seeing on your platform.
There may be trade-offs between saving time and efficiency,
but also the accuracy and comprehensiveness of everything
that's available to them.
How do you address that concern?
JEREMY SEATH: We acknowledge that our platform's not
perfect.
Our product's certainly not perfect.
And there will be cases where they
might see a false positive piece of information.
We're very transparent about that.
We give them access to a PhD scientist
to report any of that, and we're very, very quick
to respond to it.
It garners a lot of trust with people
when they can flag something for you about your product,
and they can actually see results
a few weeks later when you've gone and addressed
that for them.
We believe that our users are experts in their own domain,
and we've designed in such a way that we don't
want to overstep that domain.
We don't want to make decisions for them.
We're an information aggregator.
So even something as simple as when
we show a list of products, like product results, when somebody
enters the search terms they want,
how do we actually rank those antibodies?
We just show the products by the number of figures
that we have for them.
So we don't--
DI DANG: Objective.
JEREMY SEATH: Yeah, very objective.
We're not trying to say one product is
better than the other.
All we're saying is, there's more data on this product
than the next product.
And we leave it to them to actually dig
in and see what that might mean for their particular
experiment.
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DI DANG: For AI-driven products, it's especially critical
that you make design decisions that
help your users calibrate their trust in what
your AI product can do.
On one hand, you don't want them to not understand
where these results or predictions are coming from,
and so they don't trust your product or feature at all.
But on the other hand, you also don't
want them to blindly over-trust what AI can do for them.
Rather, you want to strive for a happy medium, where users
both know what the AI may be better at,
but also where their own skills and capabilities may
be a better fit.
CASANDRA MANGROO: I head up the science team.
So our job as a science team, a team of PhD scientists
who are trained in various areas of scientific research,
is to ensure the comprehensiveness and integrity
of the data that's on the platform.
DI DANG: We've talked about the importance of data sources
to user trust.
You mentioned earlier that the interface, the design itself
is also really important in terms of earning the end user's
trust.
Can you tell me more about that?
CASANDRA MANGROO: We had to put as much effort
into building our interface as much
as we did in building the data and the algorithms.
[CHUCKLING]
So a scientific user is looking for a certain thing
at a certain time.
But there are many different things
they could be looking for, so we had
to account for all the different ways scientists can actually
search this information, and show them
that information at the appropriate time
without overwhelming them with all the data that we have.
So as you can imagine, our knowledge graph
that consists of all the data that we've
collected with our algorithms has
way more information than we want to show
all at once on the platform.
So we had to account for all the different ways the users will
search this information.
So design teams and science teams
work very closely together.
So part of the design process is really just first starting
with the ideation.
Do we have a new feature that we want to build?
How is it going to integrate with our current data?
What is the data structure that we have currently in place
on our knowledge graph to allow that particular feature
to be then integrated into the platform?
DI DANG: It's important that UX, product,
and data and engineering roles work
hand-in-hand to ensure your data can support
the design of AI-driven features that
are grounded in user needs.
As BenchSci has shown us, it's critical to build user trust
through the design of your AI-driven product.
For more pointers on making AI product decisions,
check out the People + AI Guidebook
at pair.withgoogle.com.
As product creators, we have a responsibility
to create things that are focused
on the human experience--
things that are centered.