Hi, I’m Chengyuan Xu, and along with my coauthors Kuo-Chin Lien
and Tobias Höllerer, we're excited to present some highlights of our work:
“Comparing Zealous and Restrained AI Recommendations
in a Real-World Human-AI Collaboration Task”.
Systems powered by AI have achieved human-level performance
in some domains, but we still can't trust
fully autonomous AI in high stakes tasks.
So forming a human-AI team is a great strategy
to achieve complementary team performance.
Where the combined team performance surpasses what either
the AI or the human could achieve individually.
Take video anonymization, for example.
We need to first locate and then blur every human face in each
frame to protect people's identities.
Detection model can do it quickly, but missing one
face in one frame could reveal the person's identity.
It is also too time consuming for humans to annotate from scratch.
This is a perfect scenario for human-AI collaboration.
In this work, we conducted a large scale user study
to investigate the potential of leveraging the tradeoff
between precision and recall in AI recommendations
to harness the complementary strengths of humans and AI with the goal
of improving human-AI team performance in realistic settings.
To do this,
we proposed a high-recall, zealous AI and a high-precision restrained AI.
For the face annotation task.
We evaluated the two
AI-powered tools with 78 professional annotators.
We also learned how prior human-AI collaboration experience
can affect user skills should they lose access to AI in the future.
So how will the two AIs perform in a real world annotation task?
The zealous AI, which would prioritize
recall, will suggest more detections,
even the low-confidence ones to avoid missing any real faces.
Higher recall in this task means a higher percentage
of all faces in the scene correctly detected and anonymized,
minimizing the number of missed faces or the so-called false negatives.
In a collaboration, a user reviewing zealous
AI recommendations should see more correctly annotated faces,
but they will also have to reject more false positives.
On the other hand, a restrained AI with the same F1 performance
would prioritize precision by suggesting only high confidence
detections, allowing users to focus on adding missing boxes
without too much noise from areas incorrectly identified as faces.
Higher precision means a higher percentage of correctly identified faces,
out of all instances identified as faces.
While the zealous AI should intuitively help annotators
achieve higher recall, we need to verify this in a real world study.
We also need to evaluate effect on task completion time
as an important performance metric.
Longer task times for humans can also lead to a decline in quality.
We integrated face detection and object tracking
algorithms into existing annotation tools to build an AI-assisted
video annotation workflow for our user study.
The AI-powered system can pre-annotate the videos illustrated here by the green
or orange bounding boxes so the human annotator only need to reveal
the AI’s recommendations while maintaining control over annotation quality.
If the AI accurately suggests faces
as shown here with track ID 1, and ID 4, the user can accept them
without further actions to enhance the overall recall.
For false positive detection, such as track ID 2.
The user can reject the entire track with just a couple of clicks,
boosting precision.
However, for false negative or flawed detections,
annotators must draw new bounding boxes and track them across frames
which improves both precision and recall,
but is much more time consuming.
By breaking down the complex workflow
into fundamental user actions of Accept,
Reject and Solve, and linking them to precision recall.
We found that recall is not only more important
in this task, but also more costly to improve.
This observation reveals an optimization opportunity
by delegating more solve actions to AI.
Furthermore, the human natural ability to locate faces require no special training.
Make face annotation an ideal task
for examining the influence of diverse AI recommendations.
We designed a real world study that ultimately spanned four weeks.
In part one of the two part study, the three treatment groups annotated
the same 24 videos using different methods by working with no AI assistance
or by partnering with their restrained or zealous AI teammates.
We're also curious if prior collaboration experience
with different AI teammates can affect user skills,
should they lose access to AI assistance in the future?
So after participants worked on part one for 2 to 3 weeks,
we removed assistance for everyone in part two of the study.
We considered video difficulty and user expertise as additional factors.
We curated 36 egocentric videos
from the Ego4D dataset and divided them into easy,
medium and hard videos based on the number of faces per frame.
Let's look at some result highlights.
Here we plot the face detection recall for the over
2000 Annotated video clips as a data point each,
to compare the recall distribution of the three methods.
In part one of the study both zealous and restrained
AI helped workers deliver higher recall annotations than the human only group,
particularly in medium and hard videos.
As expected, the group reviewing
zealous recommendations achieved the highest recall.
When comparing the task completion times, both AI assisted groups finished faster
than the human only group, particularly the restrained AI group also achieved
significantly shorter time than the group who worked with the restrained AI.
For medium and hard videos where there are multiple
faces in each frame, zealously AI recommendations
were particularly effective in reducing completion time.
In terms of user expertise, Both novice and veteran annotators
were consistently faster with zealous AI than the human only baseline,
while we did not observe benefits of the restrained AI for novice users.
In part two of
the study, when no one has access to AI assistance any more, annotators
that “trained” with the zealous AI continue to deliver higher recall
compared to workers who previously worked with the restrained AI.
Annotators who previously worked with the zealous
AI now complete tasks as quickly as the baseline group workers
who use the manual method in both parts of the study.
However, those trained with the restrained AI are now significantly slower,
with novice users being the most affected.
Please see our paper for full results and analysis.
To summarize, in this work, we explore the precision-recall tradeoff
in model tuning as a new direction to improve human-AI
team performance in realistic settings.
We proposed the concept of restrained and zealous
AIs for high-precision and high-recall recommendations.
Our study serves as a new real-world
example of complementary team performance.
The zealous AI helps annotators achieve significantly better performance
in the recall–demanding video annotation task.
Our follow-up study observed that naively
pairing humans with AI without optimizing for the task
or human-AI workflow can lead to negative training effects.
We hope our
findings can help the design of future AI systems.
The key to improving human-AI collaboration performance
is identifying and harnessing the complementary strengths of human
and AI teammates through a thorough task analysis.
We would love to see more HCI and AI researchers
conduct latitudinal studies in multiple recall- or precision-
demanding tasks to test and refine our findings.
We hope our study on the precision-recall tradeoff inspires researchers
to further map the property space for machine learning models
to complement human skills. Thank you.