Hi, I am Yi-Shyuan Chiang from NTHU and today I would like to share our paper about user system communication
with a focus on smart home routine assistants.
We can now preset the routines for smart homes,
but what if homes get smarter?
What if they can just do things for us?
That would be amazing, right?
With machine learning, it is possible.
But adopting machine learning means that machines need to learn from their uses.
In other words, users need to provide feedback to better the system.
Users not providing ground truth will lead to systems giving out wrong predictions.
Past study has shown that when intelligent systems are not giving out right predictions,
users would get made, angry and eventually want to abandon the system.
In order to have smoother experiences and interactions, systems may need to explain
why SHRAs make those false predictions or urge users to provide feedback.
Either way, user communication is crucial That is why we are interested in knowing how
systems should communicate with their users.
More specifically, how should SHRAs communicate with users?
For the scope of this study, SHRAs are “ Agents that predict residents’ intended actions
on connected home appliances based on their routines and automatically perform those actions
for them.”
We’ve invited 20 participants, 10 male, 10 female, to enact different designs and
situations.
By leveraging user enactment, we are able to gain insight into people’s thoughts and
preferences more efficiently.
The study took place in a mock-up living room with SHRAs stimulated in Wizard of Oz style.
In total, we’ve explored 7 different dimensions of SHRAs as shown in the slides.
In a typical round of enactment, participants would be assigned with a script, for example,
“it’s a hot summer day, and you want to turn on the fan.”
After reading the assigned script, the participants would be asked to take the actions they deem
appropriate such as walk toward the fan in order to turn it on.
We tested out different situations where the predictions are right or wrong.
After each round of enactment, we would follow up with questions regarding their thoughts
at the moment.
Besides follow up questions we also interviewed participants at the end of every study.
Transitions: RQs We aimed to answer 3 research questions with
our study, which are: How would people prefer to communicate with
SHRAs?
What do people want to know when communicating with SHRAs?
How do people perceive and assess incorrect automated acts caused by false predictions?
What would they do to better SHRAs?
Due to the time limit, I would share three main findings and four design spaces for future
smart home designers in this presentation.
Findings #1
The first part of our findings would be people’s preferred communication style, whether a SHRAs
should ask for permission, notify only or have no notification at all?
There’s no definite answer, but we found four factors that would affect people’s
preferences.
The first factor is “the perceived possibility of being a wrong act”, if the SRHA has high
confidence it won’t need to ask.
On the other P10 commented that “Low confidence means it is not sure, and it should ask me.”
Participants’ natural preferred level for user control also plays a part, some naturally
prefer to have more control over the system, P7 has told us that “just turned on the
TV without asking me, [and] I found it quite disrespectful”, while others are perfectly
fine for SHRAs to do things automatically.
The third factor would be the cost of an automated act, if the system were to turn on risky appliances
such as gas, according to P16 SHRAs should ask for permission.
Other contextual factors would also affect participants’ preference for communication
system, P4 told us that weather could be one factor, “If it was hot outside, it would
be nice to have it automatically turn on AC for me, but if it were cold, it would be
annoying.”
Finding #2 The second part of our finding is about “confidence,”
how it is interpreted and what would affect people’s desire to learn about it.
Inspired by the common use of “confidence” in some consumer products and prior research
, we’ve used confidence as our communication prompt and tested out a variety of combinations.
We manage to locate factors that influence peoples’ desire to learn about confidence
at the moment, which are “curiosity about the system, perceived opportunities to interfere
with automated acts and the correctness of automated actions.
and Participants’ availability in the moment.”
We didn’t define “confidence” since we would like to know what people’s natural
interpretations are.
Though most are very similar such as “Probability, possibility, likelihood, accuracy, certainty,
the strength of association, and statistics.”
But there are also some interesting ones such as “how sensitive the system is” or “a
kind of desire or urge.”
It is not surprising that participants’ inherent curiosity about the system would
play a part in their desire.
Some, like P14, for example, are naturally indifferent, saying that “as long as it
turns [the appliance] on for me, it is enough, I don’t care much about how confident it is.”
While some still would want to know about confidence, P12 told us that “I feel better
if I know the system at least is confident in what it does.
If it does not show confidence, I would wonder why it even automates it in the first place.”
The second factor is the perceived opportunities to interfere with automated acts, P1 noted,
“If the system had already done it, it was unnecessary [at the moment] to know its confidence
level.”
The correctness of automated actions and Participants’ availability at the moment would also influence
people’s desire to learn about confidence.
P15 said, “I don’t want to learn about confidence now since I just got off work.
All I want to do is relax.”
Finding #3 Our last main finding would be how people
react when SHRAs err.
People usually would go through four stages, though some would skip the correct phase.
(Stage 1: Assess) Participants would first assess whether they
are correct, whether the exact appliance they want to turn on is actually turned on at the
moment.
If SHRAs’ prediction is wrong they would further assess whether the act is appropriate.
It is interesting that participants did not necessarily see incorrect predictions be inappropriate.
P8 mentioned that as long as the false prediction made sense in the wider context of whether
he foresaw himself turning the same appliance on later, he felt comfortable with it being
wrong.
(Stage 2: Diagnose) In the diagnose stage, participants tended
to diagnose SHRA actions’ correctness by speculating about what information the system
had collected, how it had predicted their intentions, and why the automated action was
chosen.
(Stage 3: Correct) Some participants would then move one to the
correct phase, such as by telling the SHRAs that its confidence is too high or too low.
Participants’ availability was a key factor here, though P2 said, “Since the prediction
was wrong, I would like to fix it now.”, we also have P15 explained, “It is annoying
to have multiple prompts for feedback in a short period of time [...]. I‘d rather provide
feedback all at once later.”
(Stage 4: Improve) As well as wanting to give the SHRA real-time
feedback, many participants mentioned that they would like to improve its prediction retrospectively.
We’ve found four different improve strategies.
The configuration approach could be subdivided into two types.
First, despite confidence being computed by the SHRA itself, many participants perceived
it as modifiable and wanted to suggest the ‘correct’ level of confidence the system
should have computed.
For example, P1 said, “it said that it had 20% confidence.
[...] the confidence level should have been higher.
I wish there was some way to adjust the confidence level upward.”
The second type was to reconfigure the measurements and thresholds the participants thought the
SHRA had adopted to estimate their intentions.
The second strategy was to teach the SHRA their behaviors.
For example, P16 said that if the system erred, “I would want to start re-training it.
I could only train it by repeating my current gestures and giving the same feedback.”
For those who adopt the simplifying method, they would rearrange the physical configuration
of the area, so that the relation between home appliances and furniture would be more
distinguishable.
As P5 commented, “I should place the AC remote further [from the TV remote], so the
system can detect it better.”
The last strategy would be participants complying with SHRAs,
they would comply adapt to the existing patterns that they perceived the system had learned from them.
It should be noted that we’ve found some participants did not expend effort on improving
the system, either because they thought doing so was the sole responsibility of product
developers, or because they regarded themselves as unable to improve it.
Either way, their general reluctance should be noted by the future SHRA designers.
# Design Spaces I would conclude this presentation with four
design spaces inspired by our findings.
(Onboarding) During the onboarding phases, SHRAs should
first check the household’s existing AI-related knowledge and mental models.
It is vital to do so since such mental models would affect their future interactions with
the system.
SHRAs should allow householders to customize the system to their liking, such as to tailor
how they prefer SRHAs to communicate with them and how details explanations should be etc.
Tutorials on how to appropriately give feedback
and improve their systems, how much training they need should also be provided.
(At Routing Prediction and Execution) The SHRA should ask for permission to perform
actions when it is first introduced into the household, since receiving regular feedback
is important to better the quality of its predictions.
Plus it can remind its users that it is still learning.
The key is to make households more comfortable with the system’s learning curve.
As its number of successful learning experiences increases, the SHRA may gain the trust of
the members of its household.
Once this stage is reached, it can ask the household members if it should continue asking
for their permission before performing those automated actions.
(Occasional Tips, Tricks and Quick Facts) Users do not always pay close attention to
onboarding tutorials and have the tendency to skip those materials.
So it would be suggested to occasionally sneak in brief tips, tricks, and quick facts about
what information they detect, how they learn, and how users can help them improve SHRAs’
predictions while the systems are loading etc.
(Administration) Finally, we would suggest SHRAs to have easy to dashboards
to allow the members of households to review all records of
automated actions and predictions, along with explanations for them.
Dashboards should also allow the users to modify all preferences they had previously set,
and allow more advanced configurations such as parameters for prediction and for
deciding whether to execute an act.
Conclusion
In this paper, we’ve provided insights in regards of how
and what should SHRAs communicate with users, located four stages of reactions when interacting
with SHRA and different types of improving strategies and suggested four design spaces
for future SHRA designers.
Please refer to our paper if interested in more findings regarding how users interact with intelligent systems.
Thank you so much for listening.