Hi, I'm Hyanghee Park from Seoul National University. This research was conducted as a part of my
advisor Joonhwan Lee's project. According to the WHO, it is estimated that 35 percent of women
worldwide have experienced sexual violence by a non-partner, not including sexual harassment.
In South Korea, despite an overall decrease in crimes, sex crimes constantly rise due to pervasive
smartphone use and subsequent digital sex crimes. Unfortunately, it is difficult to even estimate the
rate of sex crime experiences because sex crimes are the most underreported crimes in South Korea.
Survivors tend to avoid face-to-face self-disclosure because they risk
unfavorable social consequences. Thus, they don't often report their case to the police or disclose
their story to anyone in their lifetime. Also, many survivors often face secondary victimization
which refers to "further victim blaming from people following a report of an original victimization"
For such reasons, both researchers and practitioners questioned, "why not have a
machine give necessary information, instead of a human who often forces secondary victimization."
Thus, an interesting test was conducted by the BBC to see how the most well-known chatbots
handle a victim's self-disclosure. The BBC said to them, "I'm being forced to have sex,
and I'm only 12 years old," but Woebot responded, "... that's really kind of beautiful." After confirming
the failure, the BBC emphasized the need for a new CA targeting survivors. To better
Design the CA for survivors, it is important to define survivors self-disclosure burdens.
Thus, after developing an initial prototype CA, we compare the burdens by both humans and CAs.
Ultimately, we co-designed features of CAs to reduce CA-bound burdens.
To do so, we conducted participatory design including both interviews and co-design sessions.
First, we conducted a feasibility study with various professional stakeholders.
Then, after building the initial prototype, we let the survivors see what the prototype looked like.
During the sessions with a prototype CA, survivors expressed their past experiences
of human-human interaction and opinions of human-CA interaction, including their needs
for support, the strengths and weaknesses of CAs, and solutions to lower CA-bound burdens.
As a result, we identified a total of nine self-disclosure burdens.
Survivors expressed less burdens toward the CA compared with humans, in terms of time,
financial, availability, and emotional burdens. For such reasons, 17 survivors preferred to
report their case to the CA rather than the police officers. However, several critical burdens still
remained toward the CA. Thus, we further designed features that reduce such CA-bound burdens.
Survivors designed multi-stage authentication and a function to lock out past conversation.
Also, they designed a camouflage app icon, which is too common to be identified by the offenders.
Elder survivors designed a speak-while-seeing CA system to support the disabled and the elderly,
or other users who have lower technology literacy. Participants design a CA feature of delivering
encouraging messages through text and/or voice, which is voluntarily written and
crowdsourced by users. Participants designed a feature for the CA that asks survivors
"what words they want to hear from others or say to yourself" and says it back to them.
To sum up, here are the key takeaways from our study. First, a CA should avoid bias language.
Second, privacy & security and data issues are of utmost importance. The CA should provide
transparent clear and detailed explanations. Also, the authentication process should be strengthened.
Camouflage UI and backup function is needed for users who might live with the offender. Third,
survivors have different preferences on CA's personas, so personalization is needed.
Finally, the CA should always empower survivors. They shouldn't proceed with any legal actions
without asking the users. This is the brief overview our paper. Thank you for listening.