Hello everyone. I am Amama Mahmoud.
I am a second year computer science PhD
student at Intuitive Computing Lab at Johns Hopkins University.
I am interested in designing effective
and diverse AI agents to create productive and intuitive interactions with users.
Today, I am presenting my work on the effectiveness of sincere apologies
and repairing relationships between users and conversational AI agents.
In voice based interactions, errors are inevitable,
such as presenting wrong search results or sending a message to a wrong person.
I'll present three types of AI errors next.
First, when no speech is detected at all.
Second is where speech is detected but not understood by the agent.
Third, when speech is recognized incorrectly and the agent has already
acted on it, these types of errors are trickier to recover from.
Even with successful recovery from such
errors, users may still feel slight discomfort
or frustration that can result in them abandoning the technology.
Hence, it is important to repair the relationship between users and agents
during the recovery phase of error mitigation.
In this work, we look at how can we mitigate such negative impacts of AI
errors to repair user-agent relationship to maintain satisfactory interactions.
We take inspiration from human-human interactions in customer service and look
at what do humans do when they make mistakes?
They apologize.
Since apologies offered by people are perceived to be more sincere than robots
and agents, these are effective in repairing trust in human relationships.
However, same is not yet established for apologies offered by machines,
since machines are perceived as not having regret.
So we ask if apologies from conversational agents can be perceived sincere enough
to maintain a trusted relationship between users and agents.
There are various aspects of apology
that can affect how sincere and genuine it may sound.
In this study, we explore two dimensions of apology.
First, the degree of seriousness with which the agent delivers the apology.
Second, who is assigned blame for the mistake while doing so?
Serious is very to reflect casual
and serious attitude, while the agent either takes full
responsibility for the error or shifts the blame to others.
We set up a within subjects experiment in an online shopping task scenario.
We also included control condition where
the agent offers no apology and simply proceeds with the task
once the mistake is resolved.
We randomize the order in which the five conditions are presented to user.
Let's look at four experimental conditions now.
First, casual attitude is reflected
by saying informal phrases such as sorry for the mishap and the blame is shifted
to engineering team. Whereas for serious attitude,
more formal phrasing is used, such as I am sorry for the inconvenience.
While maintaining the casual attitude and taking the blame condition,
the agent explicitly mentions that I
confused the items instead of blaming the engineering team.
And finally,
for serious attitude and accepting the blame condition agent says that I am
sorry for the inconvenience. I confused the items.
I have difficulty distinguishing between homonyms.
We contextualize our experiment in an online shopping task presented
to participants as a simulated interactive storyboard.
First, the agent is introduced
to the participants along with the task that mentions a shopping list
for an event such as a party.
The participant clicks on the items in the shopping list one by one.
The agent responds with audio and text
participant confirms addition of the item to the cart.
In case of error, the participants get reported by clicking No,
this is incorrect.
We artificially introduced one intent
recognition error in each session for the study where the agent confuses
homonyms, words that sound the same but mean different things, such as bow.
The participant meant ribbon bow, but the agent suggested an archery bow.
These errors can be resolved by asking users for clarifications.
In our experiment, the agent recovers from the error in second attempt
and then apologizes for the mistake
based on the experimental condition.
We constructed subjective measures by asking participants questions about
their experience and perceptions of the agents
after every session.
Informed from prior work, we asked participants how satisfied they
were with the agents approach to handle the errors.
Using Godspeed questionnaire,
we gauged their perceptions of agents intelligence and likability.
We also asked about their willingness
to use the smart speaker for ordering things in future.
We recruited 37 participants
by convenience sampling aged between 18 to 48.
Now for results, here are our five conditions.
First, we look at service recovery satisfaction.
We found that the participants rated the agent that apologized seriously
and accepted the blame higher on service recovery satisfaction than the agent
that apologized casually and shifted the blame to others.
Similarly, this agent was perceived to be
more intelligent than other agents whose apologies lacked both seriousness
and acceptance of blame or lacked even one of these two components.
In addition to being more likable than
rest of the agents who apologized, this agent was considered more likable
than the agent that did not apologize at all.
There are two main takeaways from this study.
First, apology delivered by the agent
with a serious attitude, while owning its mistakes sincerely may
result in better service recovery and perceptions of the agent.
This finding suggests that both seriousness and blame assignments are
important factors to consider while designing good, effective apologies.
Second, apology lacking an internal
attribution of blame, even though delivered with a serious
attitude, can be worse than no apology at all, as it resulted in lower
willingness to use in future than the control condition.
This finding suggests that it is pertinent to consider negative impacts that poorly
designed or bad apologies may have on users'
perceptions of agents.
In this study, we used female voice for our agents
and did not consider other aspects of AI embodiment and behavior that may affect
how apologies by these agents are perceived.
For instance, using gendered voices for AI
may introduce stereotypical gender biases towards agents.
My future research will focus
on understanding if and how these social biases may manifest in human-agent
interactions due to agents' appearance and behavior.
And how can we mitigate these biases?
My goal is to design diverse digital
agents to create more inclusive AI-enabled spaces.
Thank you for taking the time to listen to my talk.
I'll be happy to chat more about my work.