This article examines designers’ practices related to the use of artificial intelligence (AI)-based tools across different stages of the design process, interpreting them, among other perspectives, through the lens of Actor-Network Theory (ANT). The analysis is grounded in an understanding of design as a complex, staged, and non-linear process that integrates analytical reasoning, intuition, and embodied practical knowledge. The study is based on 14 in-depth individual interviews conducted in 2025 with practising designers.
The analysis focuses on three dimensions of AI’s impact: process acceleration, creativity, and designer responsibility, relating them to the stages of research, conceptualisation, design, and prototyping. The findings indicate that designers primarily perceive AI as a tool that accelerates work, automates repetitive and technical tasks, and supports data analysis and the generation of alternative design solutions. At the same time, AI’s creativity is viewed as secondary and dependent on existing data, whereas the value of design outcomes is understood to derive from human interpretation, intuition, and contextual knowledge. Responsibility for design decisions and their consequences remains with the designer, whose role is increasingly shifting toward that of a curator and critical decision-maker.
Interpreting the findings through the lens of ANT reveals that AI, as a non-human actor, not only reshapes the relationship between designers and technology but also activates strategies aimed at protecting designers’ autonomy, alongside narratives emphasising the importance of creative authenticity. From this perspective, design practices are accompanied by processes of rationalisation and resistance in response to AI’s expanding role in the design process.
This article addresses the significant scarcity of forensic tools for generating voice samples for identification parades by proposing a novel method for voice reconstruction without a reference recording. The presented solution serves as an auditory analogue to facial composites, utilizing a hybrid architecture based on machine learning and speech synthesis (XTTS). The methodology combines an iterative algorithm for selecting a base voice candidate with a specialized neural network module that allows for the modification of interpretable acoustic parameters via latent space manipulation. Technical validation confirmed the system’s effectiveness in navigating the voice space and accurately translating physical parameters into vector representations. By shifting the identification burden from error-prone verbal descriptions to direct auditory perception, the system minimizes the verbal overshadowing effect. Consequently, the proposed prototype offers promising practical implications for forensic science, providing a technological foundation for law enforcement to conduct accurate voice lineups based solely on witness memory.
Contemporary theories of emotion emphasize the dual role of emotions as both personal experiences and communicative signals during social interactions. However, the impact of macro-level societal structures on emotional expression remains underexplored. This study investigates the expression-experience discrepancy for self-reported emotions across nations, focusing on how societal development relates to this discrepancy, which captures expressivity. Using meta-analysis and multilevel modeling with a multinational sample (Nsample = 12,549, knation = 48; Study 1), we assessed the directions and variabilities in the frequency of expression of both positive and negative emotions, relative to the frequency of experiencing them. Negative emotions were more likely to be under-expressed. Further analyses revealed that societal trust, system quality and fairness, and the human development index significantly predicted the size of these discrepancies. Surprisingly, these effects were not associated with traditional cultural dimensions such as individualism versus collectivism, providing new insights into the social functionality of emotions from a macro perspective. These findings were replicated in an extended multinational sample (Nsample = 19,690, knation = 65; Study 2). Our findings highlight the importance of considering structural and societal factors in emotion research and provide a foundation for future explorations into the relations between cultural contexts and the expression of emotions.
