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February 19, 2026Cambridge Forum on AI Culture and Society0 citationsOpen Access

Interdisciplinary research: Friend or foe to ethical AI?

AMAlexander Martin Mussgnug

Key Points

  • This research aims to explore how interdisciplinary practices impact ethical considerations in applied AI.
  • Investigated the culture of interdisciplinarity in applied AI and ethics.
  • Analyzed the importation of norms from social sciences and medicine.
  • Outlined three potential paths forward for improving ethical AI practices.
  • Identified that the separation of applied AI from established disciplines may exacerbate ethical issues.
  • Showed that understanding AI within existing disciplinary frameworks could enhance accountability.
  • Discussed the paradox of interdisciplinarity potentially reinforcing the very issues it seeks to address.

Abstract

Abstract This paper investigates a specific culture of interdisciplinarity that has gained traction at the intersection of applied AI and ethics. To address social and ethical harms of AI applications, scholars have suggested importing norms, methodologies and governance frameworks from established disciplines such as the social sciences or medicine. I show how this importation presupposes and endorses a framing of applied AI as a domain separate from established disciplines. Yet, such separation is what initially allows AI practitioners to operate outside those disciplinary norms that have evolved to prevent harms now associated with AI applications. Conversely, if AI applications were understood as situated firmly within these disciplines, practitioners would already be accountable to their norms and standards. Paradoxically, this culture of interdisciplinarity might thus reinforce a problematic disciplinary isolation of applied AI underlying the very ethical issues it seeks to mitigate – fighting symptoms while playing into their cause. In response, I outline three paths forward.

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Cite This Study

Alexander Martin Mussgnug (2026) studied this question.

synapsesocial.com/papers/6996a798ecb39a600b3ed5eehttps://doi.org/10.1017/cfc.2026.10015
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