Hi everyone. I am Changhoon Oh,
a postdoc researcher in the Human Computer Interaction Institute
at Carnegie Mellon University.
I’m going to talk about “Understanding How People Reason About Aesthetic Evaluations of Artificial Intelligence.”
AI technologies are applied to fields that are very subjective
and can be interpreted differently by different people.
However, the application of AI algorithms to such creative but highly subjective domains raises
questions about whether the various people or groups surrounding them can commonly understand them
or sufficiently agree on them.
Both subjective domain expertise, as well as AI expertise,
could result in entirely different interpretations and understandings of the output of AI algorithms.
In light of this,
we tried to investigate how different users reason about the results of an AI algorithm
discuss human-computer interaction and user experience considerations in the
design of AI infused user interfaces in subjective field.
So the questions we want to ask are:
Can users regard the results of AI in subjective domains as interpretable?
Can they feel that the judgments made by AI algorithms are reasonable?
What difference does expertise make when users interact with AI?
users interact with a I can users narrow the difference between their thoughts
Can users narrow the difference between their thoughts and AI predictions through constant interaction with AI?
How do users want to control and communicate with AI algorithms in subjective domains?
So, we designed a research prototype, AI Mirror,
a user interface that tells users the algorithmically predicted aesthetic scores of photos
based on a deep neural network model.
With AI Mirror, users can take or select a picture to be evaluated by the AI.
Then, AI Mirror shows the aesthetic score of the picture on a 10-point scale.
In the design of AI Mirror, we utilized Google’s Neural Image Assessment,
an AI algorithm to predict the aesthetic quality of images.
To understand how users interact with the system, we designed a user study.
In recruiting participants, we sought to balance the following three groups:
AI/ML experts, photographers, and the general public.
We recruited a total of 18 participants, with the same number of participants for each group.
In the study, Using AI Mirror, the participants took photos or selected photos from the photo album.
Then we asked participants to give the expected score for the aesthetic evaluation of the photo they had just taken or selected.
Then, AI Mirror showed the user the aesthetic score of the picture on a 10-point scale.
Then we asked participants whether they found AI Mirror’s aesthetic evaluation score interpretable.
We also asked them whether AI Mirror’s aesthetic evaluation score was reasonable.
They rated these questions on a 5-point Likert scale.
Participants were able to perform the task as many times as they wanted.
Through the procedure, in each trail,
we were able to collect the data with various variables such as
Expected score, Difference, Perceived interpretability, Perceived reasonability and Trial number.
In addition, we conducted a qualitative study to gain a deeper and more detailed understanding of users’ thoughts.
While performing the tasks, the participants could freely express their
thoughts about the tasks.
After all tasks were completed, we conducted semi-structured interviews.
In the quantitative analysis,
we conducted statistical analysis of the six types of data collected from each participant.
group, trial number, expected score, difference, perceived interpretability, and perceived reasonability
and tried to identify significant relationships among and differences between these variables.
We used panel analysis as the main analysis method,
which is specialized for analyzing
"multidimensional" data collected "over time," and from the same individuals.
Here are the results of our study.
From the quantitative analysis, we firstly identified that trials had a significant influence on difference.
That is, as the n umber of trials increased, difference significantly decreased,
which means that
as users continued to interact with the AI,
they reduced the difference between their expected scores and the AI’s scores.
In the analysis on perceived interpretability,
we observed that difference had a significant influence on perceived interpretability.
which means that
users had difficulty interpreting AI scores when there was a big difference between their evaluations
and those of the AI.
In addition, we identified that
group had a significant effect on perceived interpretability, especially for photographers.
The photographer group showed a higher level of interpretation
of the aesthetic scores evaluated by the AI compared to the AI/ML experts.
Although it was not a significant difference,
the general public also showed a higher level of interpretation than the AI/ML experts.
In the analysis of perceived reasonability,
we observed that difference had a significant influence on perceived reasonability, which means that
users did not think the AI score was reasonable
when there was a difference between their thoughts and those of the AI.
We also identified that group had a significant effect on perceived reasonability in the case of photographers.
The photographers gave higher reasonability scores than AI/ML experts did.
Although it was not a significant difference,
the general public also gave higher reasonability scores than AI/ML experts did.
Now, let's take a closer look at the users’ thoughts through the qualitative research results.
First, While interacting with AI Mirror,
the participants showed distinctive characteristics according to their group.
In particular, we observed that the vocabulary they used reflected their expertise.
AI/ML experts commonly used words that reflected specialized knowledge of ML,
such as “algorithm,” “dataset,” “training,” “model,” and “feature,”
which were never mentioned by the other groups.
The photographers interpreted and inferred the results of the AI using their expertise in photography.
They often mentioned important elements of photography,
such as “light,” “color,” “moment,” “composition,” and “distance,”
and camera controls, such as “focus,” “aperture,” and “lens.”
The general public group took pictures in the way that they typically take pictures
without specific professional knowledge.
They mainly took pictures of their favorite objects from among those prepared for the experiment
or chose pictures of famous landmarks or beautiful landscapes from the photo album.
Next, we identified that
participants adopted their own personal strategies to infer the AI’s principles of evaluation.
When participants took pictures, they tried to experiment with the AI by making slight changes to the pictures.
They changed the background color of an object or the composition of the same object.
Also, participants applied their speculations about how AI works to different cases.
They continued their testing by using similar examples.
They wanted to know whether the criteria they had identified could be applied to
other photos with similar characteristics but different objects from the photos
they had taken or identified so far.
We also identified that participants tried to find new standards
that they had not seen so far by choosing completely different pictures from the photo album.
After finding a certain way to get a high score,
some participants additionally attempted to look at new types of photos.
Finally, regarding users’ perceptions on the interpretability and reasonability of the AI algorithm,
the participants wanted to actively communicate with AI Mirror.
Participants recounted interacting with the AI as a positive experience,
describing the process as interesting, fun, and enjoyable.
Nonetheless, most participants stated that they also felt negative emotions during the interaction.
When their expected scores differed significantly from those of the AI,
especially when they were rated very poorly by the AI,
participants felt embarrassed, unhappy, and frustrated.
In such a situation,
the absence of communication between users and AI can be considered
the main cause of the negative emotions.
Participants commonly expressed a desire to communicate with the AI.
They wanted the AI to explain not only the calculated scores but also the detailed reasons.
Based on the findings so far,
I’d like to discuss design considerations of our study.
The first one is “Consider what domain view the AI system’s algorithm in the subjective domain contains
-who annotated what data and what criteria were reflected
-and provide the user with the information based on it.”
The second one is
“Provide interactions with a variety of examples and appropriate explanations
so that users can fully understand and appreciate the principles of AI."
User interactions, in turn, can be an important source of quality data, especially in subjective areas.
Lastly,
especially in AI-infused systems in subjective and creative fields,
it is very important for users to develop their ideas and reflect them in the system.
The system should be designed so that it is not static but dynamic
and users and AI can adapt to and understand each other through two-way communication.”
That ends my talk. Thank you for your time.