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Mind the gap! On the future of AI research

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  • Emma Dahlin

    (Linköping University
    University of California)

Abstract

Research on AI tends to analytically separate technical and social issues, viewing AI first as a technical object that only later, after it has been implemented, may have social consequences. This commentary paper discusses how some of the challenges of AI research relate to the gap between technological and social analyses, and it proposes steps ahead for how to practically achieve prosperous collaborations for future AI research. The discussion draws upon three examples to illustrate the analytical gap in different phases of the development of AI systems. Attending to the planning phase, the first example highlights the risk of oversimplifying the task for an AI system by not incorporating a social analysis at the outset of the development. The second example illuminates the issue of system acceptance, where the paper elaborates on why acceptance is multifaceted and need not be approached as merely a technical problem. With the third example, the paper notes that AI systems may change a practice, suggesting that a continuous analysis of such changes is necessary for projects to maintain relevance as well as to consider the broader impact of the developed technology. The paper argues that systematic and substantial social analyses should be integral to AI development. Exploring the connections between an AI’s technical design and its social implications is key to ensuring feasible and sustainable AI systems that benefit society. The paper calls for further multi-disciplinary research initiatives that explore new ways to close the analytical gap between technical and social approaches to AI.

Suggested Citation

  • Emma Dahlin, 2021. "Mind the gap! On the future of AI research," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-4, December.
    • Handle: RePEc:pal:palcom:v:8:y:2021:i:1:d:10.1057_s41599-021-00750-9
    DOI: 10.1057/s41599-021-00750-9
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    References listed on IDEAS

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    Cited by:

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    3. Ghazala Kausar & Sajid Saleem & Fazli Subhan & Mazliham Mohd Suud & Mansoor Alam & M. Irfan Uddin, 2023. "Prediction of Gender-Biased Perceptions of Learners and Teachers Using Machine Learning," Sustainability, MDPI, vol. 15(7), pages 1-18, April.

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