BMF Collaborative Project 14: How knowledge influences belief of AI’s distress

AISDL Team
Email: aisdl_team@mindsponge.info

February 7, 2023

1. Project description

1.1. Background

Many people wonder if artificial intelligence (AI) can feel negative emotions. In terms of information processing, the concept of distress can be more flexible compared to its common notion applied to humans. Thus, it is interesting to explore how a person’s level of knowledge affects the belief about AI’s capability of feeling distressed.

1.2. Main objectives

This study will examine how people’s education levels and their familiarity with digital/robotic technology (developing software, writing code, and building robots) affect the belief that AI can feel distressed.

1.3. Materials

The research project will employ a dataset of 266 US residents collected in 2018 [1].

The research project will follow the Bayesian Mindsponge Framework (BMF) [2,3]. The bayesvl R package will be employed for statistical analyses [4].

1.4. Main findings

The analysis shows that a higher education level increases the likelihood of believing that AI can feel distressed. The less a person is familiar with digital/robotic technology, the more they will believe that AI can feel distress. When these factors interact, a negative effect was found, indicating that people with high education levels but not familiar with digital/robotic technology are more likely not to believe that AI can feel distress.



Figure: Interval distributions of the analytical model’s posterior coefficients

2. Collaboration procedure

Portal users should follow these steps to register to participate in this research project:

• Create an account on the website (preferably using an institutional email).
• Comment your name, affiliation, and your desired role in the project below this post.
• Patiently wait for the formal agreement on the project from the AISDL mentor.
  

If you have further inquiries, please contact us at aisdl_team@mindsponge.info.

If you have been invited to join the project by an AISDL member, you are still encouraged to follow the above formal steps.

All the resources for conducting and writing the research manuscript will be distributed upon project participation.

AISDL mentor for this project: Tam-Tri Le.

AISDL members who have joined this project: Viet-Phuong La, Minh-Hoang Nguyen, Minh-Khanh La, and Quan-Hoang Vuong.

The research project strictly adheres to scientific integrity standards, including authorship rights and obligations [5], without incurring an economic burden at participants’ expenses [6].

References

[1] Shank DB, Gott A. (2019). People’s self-reported encounters of Perceiving Mind in Artificial Intelligence. Data in Brief, 25, 104220.

[2] Nguyen MH, La VP, Le TT, Vuong QH. (2022). Introduction to Bayesian Mindsponge Framework analytics: An innovative method for social and psychological research. MethodsX, 9, 101808.

[3] Vuong QH, Nguyen MH, La VP. (2022). The mindsponge and BMF analytics for innovative thinking in social sciences and humanities. De Gruyter.

[4] La VP, Vuong QH. (2019). bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with ‘Stan’. The Comprehensive R Archive Network.

[5] Vuong QH. (2020). Reform retractions to make them more transparent. Nature, 582(7811), 149.

[6] Vuong QH. (2018). The (ir)rational consideration of the cost of science in transition economies. Nature Human Behaviour, 2(1), 5.