Hi, there this is Qian.
It is my great honor to share with you my Ph.D. dissertation, titled “Profiling AI as a Material for UX Design.”
This dissertation centers around one question
What makes human-AI interaction seem particularly difficult to design?
Some say it's because AI algorithms are complex
They rely on big data; They make unpredictable errors.
But HCI routinely grapples with complex, resource-intensive technologies
that make unpredictable errors
What makes AI unique?
Or is it (unique)?
To what extent can existing HCI methods and knowledge
address HAI (human-AI interaction) problems?
Throughout my Ph.D.
I wanted to explore this question
And to do so, I designed various kinds of AI systems across application domains
And here is my synthesis
and my initial attempt in articulating whether, when, and how human-AI interaction is uniquely difficult to design
Think about the lifetime of an AI system, say, a recommender
It first gets trained on some training data
and then it continues to learn from new user-generated data post-deployment
and when the user uses it
it takes in new run-time inputs
and provides personalized and context-aware outputs
Now, of course, the user-generated data (generated) across these contexts
are really a small fraction of the user needs and desires
and thought process and behaviors that we are dealing with here
in this context, against (the backdrop of) this framework
we can see that when we are designing a particular moment of human-AI interaction
we are really dealing with many (different) stages of interactions
that occur over time with different groups of users
we are dealing with many moving parts here
People say that the most remarkable technologies are (those) like electricity
they do exactly what the users expect them to do
such that they fade into the background of people's everyday lives
but in (designing)AI
it's already kind of difficult to match algorithmic capabilities and user needs at large
it is really difficult to fine-tune
and orchestrate every moving part in this diagram
in order to create an AI that fades into the background of people's everyday lives
How can we innovate and prototype AI systems like this
and account for their errors and unintended consequences?
I think these are the core challenges of human-AI interaction design
Now this framework also gives us some hope
It gave me at least some new perspectives into
how we can tackle these challenges (of designing human-AI interaction)
for example, in clinical AI
clinical AI systems typically do not learn from live user-generated data
therefore clinical AI is actually a great entry point to study
human-AI interaction issues like explainability and (human-AI) collaboration
we are dealing with fewer moving parts here
now when it comes to designing AI systems that continue to learn (from) and evolve with users' (interactions)
we are already seeing new design methods and processes (emerging) in the industry
and in coping with these new challenges around designing AI as "living" systems
as systems that continue to evolve and co-live with users
it is in this context that
I wrote this dissertation. It describes this framework
a number of real-world case studies like AI-assisted writing and clinical AI projects
as well as the observation of this new practice
hopefully this dissertation can serve as a useful reference
for the many AI and HCI researchers moving forward. (Thank you!)