Mind the gap! On the future of AI research

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.

Drivers behind the public perception of artificial intelligence: insights from major Australian cities

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The promise of artificial intelligence for kidney pathophysiology

“…To persuade healthcare systems to adopt machine learning in the future, implementation research needs to focus on demonstrating value-based care to garner funding. This funding will be especially critical for assessing algorithms in clinical trials that study not only ethnically diverse cohorts [71] but also clinical consequences as a result of implementation that may require recalibration of algorithms [72].…”

Sustainable artificial intelligence: A corporate culture perspective

“…SAI enables companies to better understand the interaction of the three sustainability dimensions through time, thus considering short, long-term, and longer-term interactions 2015 The guiding principle of the 2030 Agenda pursues the goal of enabling a decent life worldwide while permanently preserving the natural foundations of life. Apart from van Wynsberghe (2021), recent works referring to the concept of SAI fail to provide a holistic definition (e.g., Yun et al 2016;Dhar 2020;Bjørlo et al 2021;Dahlin 2021;Fernandez-Aller et al 2021). For example, the working definition given by Bjørlo et al (2021) within the contextual boundaries of online decision-making is based on the Brundtland Report (World Commission on Environment and Development 1987).…”