The Bias Against the Machine: NIH Syndrome and the Rejection of AI-Generated Content

Tradition Meets AI. AI art

Introduction

Should we dismiss an argument simply because it was written by artificial intelligence? This question lies at the intersection of technology, psychology, and epistemology. The growing presence of machine-produced texts, images, and solutions has ignited debate over their legitimacy. While some evaluate such outputs pragmatically, others reject them outright, insisting that only human thought can provide trustworthy insight. This resistance can be illuminated through the well-documented phenomenon known as Not Invented Here (NIH) syndrome. Originally studied in organizational contexts, NIH describes the tendency to reject ideas because they originate from an external or unfamiliar source. When applied to AI, it highlights a subtle but powerful prejudice: the refusal to recognize machine-generated knowledge as credible. This article explores the nature of NIH, its epistemological implications, and its relevance to the contemporary dismissal of AI outputs.

Defining Not Invented Here Syndrome

The term “Not Invented Here” refers to a cognitive and organizational bias against adopting external knowledge. In their seminal work, Katz and Allen (1982) examined research and development groups, showing that the longer teams worked in isolation, the more reluctant they became to incorporate external ideas, resulting in declining performance. Later studies confirmed that NIH can significantly hinder innovation. Antons, Declerck, and Piller (2017) describe NIH as “a negatively shaped attitude towards knowledge that has to cross a disciplinary, spatial, or organizational boundary” (p. 2).

The syndrome manifests in several ways: rejection of outside proposals, duplication of effort, and a preference for in-house development even when inferior. Hannen et al. (2019) quantified this effect, demonstrating that each incremental increase in NIH correlates with a measurable drop in knowledge absorption. The practical consequences are costly: missed opportunities, reduced efficiency, and weakened competitiveness. Organizations facing NIH often find that pride, ownership, and insularity outweigh rational evaluation.

While much of the research has focused on companies and professional groups, the phenomenon extends beyond institutions. It reflects a deeper human impulse to privilege familiar, internally generated knowledge over that which comes from an external or unfamiliar source.

Epistemological Dimensions of NIH

Epistemology—the study of knowledge—provides a broader lens for understanding NIH. At its core, NIH is not simply an organizational inefficiency but a source-based bias. Instead of evaluating whether a claim is justified, true, or valid, individuals often judge it based on where it originated. This violates the principle that the content of knowledge should matter more than its provenance.

Three key epistemological concepts sharpen this analysis:

1. Epistemic authority: who is entitled to be heard as a knower.
2. Epistemic trust: how much credibility we assign to a speaker or source.
3. Epistemic validity: whether a claim holds up under logical or empirical scrutiny.

NIH distorts this triad by granting authority to internal or familiar sources while withholding trust from outsiders, even when their contributions are valid. In her work on epistemic injustice, Fricker (2007) argues that prejudice often undermines the credibility of certain speakers. When applied to AI, the same pattern emerges: machine outputs are dismissed not because of their quality but because their source lacks recognized authority. The result is a modern form of epistemic injustice directed at non-human agents.

NIH in the Age of Artificial Intelligence

The arrival of AI-generated texts, images, and decisions has given NIH new relevance. Just as firms once resisted adopting external technologies, many now resist acknowledging AI contributions. This resistance is rooted in human pride, suspicion, and the conviction that genuine knowledge must be human-made, people exhibit discomfort when a machine solves questions that have challenged humans for centuries.

Research has begun to probe this dynamic. Arias-Pérez, Vélez-Ocampo, and Cepeda-Delgado (2017) found that NIH negatively impacts innovation performance in service firms, though strong innovation capabilities can offset this effect. More recently, a Colombian study (2022) examined NIH in the context of AI and robotics, linking the bias to knowledge sabotage and reduced performance. Employees skeptical of automation often undermine external knowledge flows. Awareness campaigns and perspective-taking strategies were shown to reduce this effect.

Yet most of this research still frames NIH in terms of inter-organizational knowledge transfer or workplace automation. Far less attention has been paid to the rejection of AI-generated content itself—texts, artworks, or arguments. Here, anecdotal evidence is telling: AI-written articles dismissed in journalism, AI art refused in competitions, AI-authored essays questioned in academia. These examples illustrate NIH in action, where rejection stems not from content quality but from its origin. If NIH once applied to ideas from external firms or research groups, it now applies to machines themselves.

The epistemological stakes are high. By privileging human origin as the criterion of authority, society risks overlooking valid insights and creative contributions. Trust is withheld not because arguments are unsound but because their producer lacks human intentionality. This creates a paradox: machines can generate knowledge that passes logical and empirical tests, yet prejudice prevents its acceptance.

Conclusion

Rejecting AI-generated arguments or artworks solely because they originate from a machine is a contemporary manifestation of the Not Invented Here syndrome. Long recognized as an organizational bias, NIH also functions as an epistemological prejudice: it ties authority and trust to provenance rather than validity. As research by Katz and Allen (1982), Antons et al. (2017), and others shows, NIH reduces innovation and efficiency by discouraging the adoption of external insights. Applied to AI, this bias reflects humanity’s reluctance to share epistemic authority with non-human agents.

The path forward requires a shift in focus. Instead of asking who or what produced knowledge, the more relevant question is whether the knowledge holds up under scrutiny. By confronting NIH in its new digital form, society can ensure that valuable insights—human or machine-generated—are not lost to prejudice. Future studies should explicitly investigate how NIH shapes responses to AI-generated texts and images, integrating psychology, epistemology, and media studies. Only then can we address the deeper challenge: learning to trust knowledge wherever it originates.

References

Antons, D., Declerck, M. B., & Piller, F. T. (2017). Opening the black box of “Not Invented Here”: Attitudes, decision biases, and behavioral consequences. Journal of Organizational Behavior, 38(1), 193–213. DOI: 10.1002/job.2199

Arias-Pérez, J. E., Vélez-Ocampo, J. F., & Cepeda-Delgado, J. (2017). Open innovation in services: Knowledge sources, NIH syndrome and performance. International Journal of Innovation Management, 21(1), 1750036. DOI: 10.1142/S1363919617500360

Fricker, M. (2007). Epistemic injustice: Power and the ethics of knowing. Oxford University Press.

Hannen, J., Heinrichs, H., & Kliewer, N. (2019). The impact of Not Invented Here on knowledge absorption and innovation. Research Policy, 48(9), 103822. DOI: 10.1016/j.respol.2019.103822

Katz, R., & Allen, T. J. (1982). Investigating the Not Invented Here (NIH) syndrome: A look at the performance, tenure, and communication patterns of 50 R&D project groups. R&D Management, 12(1), 7–20. DOI: 10.1111/j.1467-9310.1982.tb00478.x