Hi, I’m Nur Yildirim and I’ll be presenting our work on: “Creating Design Resources
to Scaffold the Ideation of AI Concepts”.
Designing AI products is challenging.
Research shows that most AI products fail pre-deployment due to selecting and working
on the wrong problem.
Current resources, such as human-AI guidelines and design patterns mostly help with prototyping
and designing the thing right.
Whereas practitioners ask for resources that support early phase ideation to discover what
is the right thing to design with AI.
One challenge is that designers struggle to understand what AI can do, and they tend to
envision AI systems that can’t be built.
Data scientists on the other hand, find it difficult to elicit what users need, so they
tend envision AI systems that no one wants.
There is this gap.
Recent work highlighted that AI capabilities and examples might help, but we know little
about what these resources might look like and how they can be used to ideate AI concepts.
We set out to tackle this challenge by engaging in a research through design process.
We conducted three design experiments, where outcomes of each experiment informed the next
one.
First, we looked at how can we create a resource that captures AI capabilities?
We then asked, how do we integrate this into the design process?
And that revealed additional challenges, which led us to ask, how can we blend user-centered
and tech-centered innovation approaches?
So starting with the first design experiment: Can we develop resources that capture AI capabilities?
How do we go about doing that?
If you look at existing resources on AI, most of these detail how AI works, instead of describing
what AI can do.
Building on the best practices, we took an example based approach.
We looked at common AI product features, such as spam filter, text generation, meeting summarization,
medical imaging analysis and so on.
And we analyzed these bottom up to derive high level capabilities.
Take Biometric security example.
Here AI is detecting if there is a face in this image, and then it’s identifying if
this is Jane’s face.
Those are two separate capabilities.
So we listed each unique capability for each AI example.
Then we abstracted these to arrive at the high level capability.
Detecting a face and detecting a voice is ultimately about detecting a person.
So all of these led to Detect as the high level capability.
We’ve done this for 40 AI examples across 14 different domains, spanning consumer facing
and business to business applications.
We worked our way through a large sankey diagram to gradually abstract more than 200 unique
capabilities,
which led us to eight high level capabilities: these are Estimate, Forecast, Compare, Detect,
Identify, Discover, Generate and Act.
Once we had this collection of examples and a breakdown of individual capabilities, the
question then became:
How do we integrate these into the design process?
When and how this resource might be useful?
We started out by sketching some communicative forms.
We created a table listing all the capabilities, their synonyms, definitions, and examples
where they typically show up.
We also created a set of slides that provide an overview of each capability.
Next, we designed a pilot study to assess these.
We created two design briefs: a rental vacation service and a ride hailing service.
We prepared a Figma board, detailing each brief, persona, user journey and the pain
points as well as the available data sources.
We asked design students to ideate AI-enabled features, without the slides for the first
design brief, and with the slides for the second design brief.
We found that using the resource helped ideate using a broader range of AI capabilities,
but the quality of ideas didn’t seem to improve.
Most ideas were not buildable, often because they required near-perfect AI performance
to be useful.
So on a surface level, our pilot study failed.
But it surfaced AI model performance as a key consideration for ideation.
With this in mind, we went back to our collection of examples and captured the level of AI performance
as well as the level of expertise needed for each task.
For example, workout detection can detect and count the number of steps and it doesn’t
have to be super accurate to be useful.
Having some count is better than having no count.
Whereas medical imaging, identifying cancer requires a highly specialized expertise, and
it has to have near-perfect performance to be actually useful.
Once we mapped all the examples, we realized that more than half used moderate AI performance
to create value.
There were few examples in the real world where AI had to have an excellent performance
to be useful.
We think of the Task Expertise-AI Performance matrix as AI’s opportunity space.
When ideating, students seemed to focus on the right hand side - where ideas required
near-perfect model performance to be useful, as opposed to covering this entire space.
We also noticed that following a user-centered approach, that is identifying pain points
before considering what AI can do, unintentionally limited the ideation towards the upper right
corner where there is high uncertainty for people.
From design experiment 2, we learned that capabilities and examples alone are not enough.
And innovators should also consider situations where moderate AI performance can create value.
All of this made us realize that we needed a new design process for AI, one that blended
user-centered and tech-centered approaches, such as matchmaking.
So we started to explore how we can simultaneously consider both AI capabilities and user needs.
In Design Experiment 3, we incorporated our resource into a healthcare project where we
had data science and clinician collaborators and our design team worked to facilitate ideation.
We had a rich dataset coming from nearly 40 hospitals, and we wanted to explore what are
some AI opportunities to help clinicians in the Intensive Care Unit.
We conducted two ideation workshops.
The first one followed a traditional user-centered design process, where we probed clinicians’
pain points and asked the team how AI can help.
In the second workshop, we first reviewed a subset of AI capabilities and examples,
and then asked clinicians if they recognized a situation where an AI capability might be
useful.
Most of the examples we curated had moderate AI performance.
For example, we showed a spam filter classifying email as spam or not spam, and asked if classifying
things as urgent or non-urgent, or important or unimportant would be useful.
We assessed the ideas using an impact-effort matrix.
The ideas from the first workshop were mostly difficult to build, and only about half was
valuable.
The second workshop, on the other hand, led to low effort-high impact ideas.
Clinicians were able to take an example and transfer it to their own context.
For example, predicting if a patient is busy to help assign nurses, or predicting and pre-ordering
medications from the pharmacy based on patient trends.
We also moved away from envisioning ideas that required high AI performance to ideas
where moderate AI performance could be valuable, which overall led to a better coverage of
AI’s design space.
Moving forward, we need more research to understand how to innovate with AI.
Designing new AI features for an existing product might require a different design process
compared to exploring potential value in a novel dataset or even designing a dataset
with domain stakeholders from scratch.
Future research should explore these new innovation processes along the spectrum between user-centered
and tech-centered design and see what kind of design resources could support these.
For more information, please check our full paper and visit the project website to access
the resources.
Thank you.