# What does the public think about Artificial Intelligence? — A Criticality Map to Understand bias in the public perception of AI Link to the published article: https://www.frontiersin.org/articles/10.3389/fcomp.2023.1113903/full **Reserarch Design:** ![Research Design][1] Artificial Intelligence (AI) has become ubiquitous in medicine, business, manufacturing and transportation, and is entering our personal lives. Public perceptions of AI are often shaped either by admiration for its benefits and possibilities, or by uncertainties, potential threats and fears about this opaque and perceived as mysterious technology. Understanding the public perception of AI, as well as its requirements and attributions, is essential for responsible research and innovation and enables aligning the development and governance of future AI systems with individual and societal needs. To contribute to this understanding, we asked 122 participants in Germany how they perceived 38 statements about artificial intelligence in different contexts (personal, economic, industrial, social, cultural, health). We assessed their personal evaluation and the perceived likelihood of these aspects becoming reality, and visualised the responses in a criticality map that allows the identification of issues that require particular attention from research and policy-making. The results show that the perceived evaluation and the perceived expectations differ considerably between the domains. The aspect perceived as most critical is the fear of cybersecurity threats, which is seen as highly likely and least liked. We conclude that AI is still a "black box" for many. Neither the opportunities nor the risks can yet be adequately assessed, which can lead to biased and irrational control beliefs in the public perception of AI. The article concludes with guidelines for promoting AI literacy to facilitate informed decision-making. A result of this study is a criticality matrix that 1) can guide developers and implementation managers of AI technology regarding socially critical aspects, 2) may guide policy making regarding specific areas in need of regulations, 3) inform researchers which areas could be addressed to increase social acceptance, and 4) help to identify relevant points for school and university curricula to inform future generations on AI. **Resulting Criticality Map:** ![Artifical Intelligence Criticallity Map][2] [1]: https://mfr.osf.io/export?url=https://osf.io/download/yr2vk/?direct=%26mode=render&format=2400x2400.jpeg [2]: https://mfr.osf.io/export?url=https://osf.io/download/f6x9a/?direct=&mode=render&format=2400x2400.jpeg