Profiling public perception of emerging technologies: Gene editing, brain chips and exoskeletons. A data-analytics...

Area of Science:

• Investigates advancements at the intersection of Artificial Intelligence (AI), nanotechnology, and biotechnology.
• Classifies AI developments as forms of human enhancement.
• Focuses on data-driven analytics for understanding AI and human enhancement.

Background:

• Based on a February 2021 survey of over 5,000 U.S. respondents.
• Survey included ~100 questions on human enhancement, science roles, concerns, fairness, and demographics.
• Addresses the societal implications of emerging AI technologies.

Purpose of the Study:

• To advance the understanding of AI and human enhancement through data analytics.
• To provide practical tools for future research and societal applications.
• To analyze general public attitudes towards AI-human enhancement.

Main Methods:

• Employs a data analytics framework including DBSCAN and K-means clustering.
• Utilizes ANOVA for cluster analysis, PCA, t-SNE, and UMAP for visualization.
• Includes prediction tasks with multi-class classification for advanced customer profiling.

Main Results:

• Clustering methods identified distinct AI customer profiles: moderate, tech-ethics advocates, and tech-forward visionaries.
• Achieved an ROC-AUC score of 0.852 and an average F1 Score of 0.987 in prediction tasks.
• Demonstrated significant insights into public perception of AI and human enhancement.

Conclusions:

• Technology creators and legislators must collaborate on ethical AI development.
• Ensures technological advancements are ethically grounded and widely accepted.
• Aligns AI progress with societal values and public trust.