AI Fortune-Teller
AI Fortuneteller | Credits: Soonho Kwon, Dong Whi Yoo, Younah Kang
AI Fortuneteller | Credits: Soonho Kwon, Dong Whi Yoo, Younah Kang
AI Fortuneteller | Credits: Soonho Kwon, Dong Whi Yoo, Younah Kang
AI Fortuneteller | Credits: Soonho Kwon, Dong Whi Yoo, Younah Kang
AI Fortuneteller | Credits: Soonho Kwon, Dong Whi Yoo, Younah Kang
AI Fortuneteller | Credits: Soonho Kwon, Dong Whi Yoo, Younah Kang
Predictive algorithms, powered by advanced machine learning and artificial intelligence (AI), can provide actionable recommendations about future events and how to prepare for them, such as predicting cancer risk based on genetic information. However, due to their complexity, it is nearly impossible to understand how these algorithms formulate specific recommendations. In other words, predictive algorithms offer guidance for an uncertain future, which we cannot comprehend. This is interesting because it parallels a more traditional approach to uncertainty: fortune-telling. The parallel between predictive algorithms and fortune-telling is intriguing. Although they are similar in that the rationale behind their recommendations is obscure, they differ significantly in their epistemological foundations. This striking similarity and contrast prompted us to explore how we might better interact with predictive algorithms, drawing lessons from traditional coping mechanisms.
In our project, participants interacted with a conversational career counseling AI agent, unaware that the responses were manually crafted by a mudang (a Korean traditional shaman). Our work, titled AI Fortune-Teller, captures this deception and documents participants' reactions, highlighting shifts in their initial perceptions of the agent's advice following the reveal. By juxtaposing scientific and pre-scientific approaches, we explored complex dynamics of human autonomy, motivation, trust, and attitudes toward such agents. Notably, even after learning that the advice came from a mudang and not an AI, participants did not change their initial decisions or attitudes toward the advice. This raises questions about the perceived importance of AI’s explainability or accuracy—elements that are prioritized by many AI experts. Regardless of AI's advancements, we continue to navigate life in human ways: wonderfully messy and uncertain.
Predictive algorithms, powered by advanced machine learning and artificial intelligence (AI), can provide actionable recommendations about future events and how to prepare for them, such as predicting cancer risk based on genetic information. However, due to their complexity, it is nearly impossible to understand how these algorithms formulate specific recommendations. In other words, predictive algorithms offer guidance for an uncertain future, which we cannot comprehend. This is interesting because it parallels a more traditional approach to uncertainty: fortune-telling. The parallel between predictive algorithms and fortune-telling is intriguing. Although they are similar in that the rationale behind their recommendations is obscure, they differ significantly in their epistemological foundations. This striking similarity and contrast prompted us to explore how we might better interact with predictive algorithms, drawing lessons from traditional coping mechanisms.
In our project, participants interacted with a conversational career counseling AI agent, unaware that the responses were manually crafted by a mudang (a Korean traditional shaman). Our work, titled AI Fortune-Teller, captures this deception and documents participants' reactions, highlighting shifts in their initial perceptions of the agent's advice following the reveal. By juxtaposing scientific and pre-scientific approaches, we explored complex dynamics of human autonomy, motivation, trust, and attitudes toward such agents. Notably, even after learning that the advice came from a mudang and not an AI, participants did not change their initial decisions or attitudes toward the advice. This raises questions about the perceived importance of AI’s explainability or accuracy—elements that are prioritized by many AI experts. Regardless of AI's advancements, we continue to navigate life in human ways: wonderfully messy and uncertain.
Director & Facilitator: Soonho Kwon & Dong Whi Yoo
Advisor: Younah Kang
Editor: Gyutae Kim
Mudang: Ewha Doryeong
Location: Default Studio & GT Entertainment
Starring: Sungho Chang, Haein Cho, Jin Ho Hwang, Dohyeon Kim, Jungyoon Kim, Hyo Bin Lee, & Ji Won Lee
Soonho Kwon (KR) is a PhD student in Human-Centered Computing at the Georgia Institute of Technology. He focuses on critical and humanistic approaches toward technologies to assess their social implications. He received a BS in Information and Interaction Design and a BA in Comparative Literature and Culture from Yonsei University. Dong Whi Yoo (KR) is an assistant professor at Kent State University exploring socio-technical aspects of artificial intelligence. He earned a PhD in Human-Centered Computing from the Georgia Institute of Technology. Younah Kang (KR) is an associate professor at Yonsei University. Her research interests are in human-computer interaction and user experience design, with a specific focus on emerging technologies. Kang received her PhD in Human-Centered Computing from the Georgia Institute of Technology.
Soonho Kwon (KR) is a PhD student in Human-Centered Computing at the Georgia Institute of Technology. He focuses on critical and humanistic approaches toward technologies to assess their social implications. He received a BS in Information and Interaction Design and a BA in Comparative Literature and Culture from Yonsei University. Dong Whi Yoo (KR) is an assistant professor at Kent State University exploring socio-technical aspects of artificial intelligence. He earned a PhD in Human-Centered Computing from the Georgia Institute of Technology. Younah Kang (KR) is an associate professor at Yonsei University. Her research interests are in human-computer interaction and user experience design, with a specific focus on emerging technologies. Kang received her PhD in Human-Centered Computing from the Georgia Institute of Technology.
In AI Fortune-Teller interactees engaged with an AI career counselor, unaware that in fact a mudang (Korean shaman) crafted its responses. The video documents reactions, highlighting shifts in perception post-revelation. The project stems from realizing parallels between reliance on AI-based decision systems and pre-scientific agents like religion. These systems serve as coping mechanisms for an uncertain future. By juxtaposing scientific and pre-scientific realms, it explores human autonomy, trust, and motivation dynamics. Interestingly, participants didn't alter their decisions or attitudes post-revelation, questioning the importance of AI's explainability or accuracy. The project critiques blind trust in AI and stimulates discussions on healthy human–AI relationships, and the meaning of human autonomy in the AI era.
In AI Fortune-Teller interactees engaged with an AI career counselor, unaware that in fact a mudang (Korean shaman) crafted its responses. The video documents reactions, highlighting shifts in perception post-revelation. The project stems from realizing parallels between reliance on AI-based decision systems and pre-scientific agents like religion. These systems serve as coping mechanisms for an uncertain future. By juxtaposing scientific and pre-scientific realms, it explores human autonomy, trust, and motivation dynamics. Interestingly, participants didn't alter their decisions or attitudes post-revelation, questioning the importance of AI's explainability or accuracy. The project critiques blind trust in AI and stimulates discussions on healthy human–AI relationships, and the meaning of human autonomy in the AI era.