Artificial intelligence (AI) applications are increasingly used in everyday life. Whereas some of them are widely accepted (e.g., automatically compiled playlists), others are highly controversial (e.g., use of AI in the classroom). While public discourse is dominated by perceptions of the risks associated with AI, we take a fundamentally different approach of measuring the perceived risks and opportunities of AI applications considering people’s knowledge and confidence in their own knowledge. To this end, we assessed in two studies (N = 394 and N = 437) how knowledge about AI as well confidence in AI knowledge is related to participants risk-opportunity perception of AI scenarios from three domains: media, medicine, and autonomous driving. Results showed that both AI knowledge and confidence in AI knowledge are important predictors regarding people’s risk-opportunity perception beyond people’s attitudes towards AI. More specifically, people with more knowledge about AI exhibited a so-called risk blindness in that they were underestimating the risks. On the other hand, higher confidence in ones’ AI knowledge impacted participants opportunity perception. Knowledge and confidence thus open a new dimension of understanding people’s perception of risks and opportunities in AI.
Professor at George Washington University
I am a Professor with Human-Technology Collaboration and Educational Technology programs at George Washington University in Washington DC. I have written 12 books and more than 100 articles, and I co-host of the Parsing Science podcast where scientists tell the stories behind their research. I am also the developer of the WeShareScience.com online platform for sharing research videos, and SciencePods.com where researchers can create free podcasts about their science. My research interests include human interactions with intelligent machines, needs, needs assessments, and instructional design.
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