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Images of AI: How AI practitioners view the impact of Artificial Intelligence on society, now and in the future

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  • Spiegler, Simone
  • Hoda, Rashina
  • Pant, Aastha

Abstract

Despite unprecedented technological advancement, intense commercial investment, international agreements, and growing societal concerns with Artificial Intelligence (AI), there is little insight into how those driving the field – the everyday AI practitioners – perceive AI and its impact on society, now and in the future. We address this critical gap by conducting a broad-based survey with 100 AI practitioners, followed by in-depth interviews with 20 AI practitioners, including developers, managers, and consultants. Using socio-technical grounded theory (STGT) for data analysis, we inductively identified six images of AI which capture six ways in which AI practitioners view AI, now and in the future, and their implications for impact on society and human control: Parrot captures AI that mimics human behaviour, including biases; Companion surrounds humans in daily life and supports decision making with empathy-like traits; Wolf in Sheep’s Clothing highlights AI misused by humans, causing societal harms; Saviour envisions AI solving complex problems beyond human capacity; Wizard portrays AI as powerful, yet, unpredictable and inexplicable; and Pinocchio imagines AI as gaining free will, learning from mistakes, and possibly harming humans. These images of AI provide a novel framework for understanding how AI practitioners perceive and shape AI solutions. Our findings and recommendations will assist AI practitioners, companies, and users with a shared vocabulary and understanding to explicitly and critically examine the intended and unintended impacts of AI solutions on human society, contributing to more responsible and human controlled AI design and use.

Suggested Citation

  • Spiegler, Simone & Hoda, Rashina & Pant, Aastha, 2026. "Images of AI: How AI practitioners view the impact of Artificial Intelligence on society, now and in the future," Technology in Society, Elsevier, vol. 84(C).
    • Handle: RePEc:eee:teinso:v:84:y:2026:i:c:s0160791x25002994
    DOI: 10.1016/j.techsoc.2025.103109
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    1. Coeckelbergh, Mark & Sætra, Henrik Skaug, 2023. "Climate change and the political pathways of AI: The technocracy-democracy dilemma in light of artificial intelligence and human agency," Technology in Society, Elsevier, vol. 75(C).
    2. Soheyl Khalilpourazari & Saman Khalilpourazary & Aybike Özyüksel Çiftçioğlu & Gerhard-Wilhelm Weber, 2021. "Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1621-1647, August.
    3. Lim, Weng Marc & Jasim, K. Mohamed & Malathi, A., 2025. "Service robots in healthcare: Toward a healthcare service robot acceptance model (sRAM)," Technology in Society, Elsevier, vol. 82(C).
    4. Nils Köbis & Jean-François Bonnefon & Iyad Rahwan, 2021. "Bad machines corrupt good morals," Nature Human Behaviour, Nature, vol. 5(6), pages 679-685, June.
    5. Björn Ross & Laura Pilz & Benjamin Cabrera & Florian Brachten & German Neubaum & Stefan Stieglitz, 2019. "Are social bots a real threat? An agent-based model of the spiral of silence to analyse the impact of manipulative actors in social networks," European Journal of Information Systems, Taylor & Francis Journals, vol. 28(4), pages 394-412, July.
    6. Logg, Jennifer M. & Minson, Julia A. & Moore, Don A., 2019. "Algorithm appreciation: People prefer algorithmic to human judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 90-103.
    7. Wei, Xinyi & Chu, Xiaoyuan & Geng, Jingyu & Wang, Yuhui & Wang, Pengcheng & Wang, HongXia & Wang, Caiyu & Lei, Li, 2024. "Societal impacts of chatbot and mitigation strategies for negative impacts: A large-scale qualitative survey of ChatGPT users," Technology in Society, Elsevier, vol. 77(C).
    8. Jeon, June & Kim, Lanu & Park, Jaehyuk, 2025. "The ethics of generative AI in social science research: A qualitative approach for institutionally grounded AI research ethics," Technology in Society, Elsevier, vol. 81(C).
    9. Ammeling, Jonas & Aubreville, Marc & Fritz, Alexis & Kießig, Angelika & Krügel, Sebastian & Uhl, Matthias, 2025. "An interdisciplinary perspective on AI-supported decision making in medicine," Technology in Society, Elsevier, vol. 81(C).
    10. Michelle Vaccaro & Abdullah Almaatouq & Thomas Malone, 2024. "When combinations of humans and AI are useful: A systematic review and meta-analysis," Nature Human Behaviour, Nature, vol. 8(12), pages 2293-2303, December.
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