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Abstract With a large number of film capacitors being deployed in critical locations in electrical and electronic systems, artificial intelligence (AI) technology is also expected to address the problems encountered in this process. According to our findings, AI applications can cover the entire lifecycle of film capacitors. However, the AI safety hazards in these applications have not received the attention they deserve. To meet this, the authors argue, with specific examples, risks that flawed, erratic, and unethical AI can introduce in the design, operation, and evaluation of film capacitors. Human‐AI common impact and more multi‐dimensional evaluation for AI are proposed to better cope with unknown, ambiguity, and known risks brought from AI in film capacitors now and in the future.

Abstract Film capacitors are playing an increasingly important role in power‐related fields, driven by the continuous development of dielectric materials and practical needs. Long‐term accumulation has also led to an increasing wealth of data related to film capacitors. Sonification opens up a new way for people to make good use of data from film capacitors. A framework for sonifying film capacitors data based on TwoTone is presented. Based on the analysis and discussion, it is clear that the sonification results can easily represent the monotonic variation pattern of film capacitors data. What's more, the sonification results increase the possibility that people pay attention to the changing trend of film capacitors data when there is no significant difference in the visual perception of the data. In addition to providing a new way of music generation of electrical equipment, the method proposed is expected to contribute to theory reference in typical scenarios, such as factory calibration of film capacitors, monitoring of film capacitor operation status, and presentation of statistical data of film capacitors' dielectric materials, which will help us to better understand the distribution characteristics of polymer films.