Abstracted Away: Resisting Alienation and Ungrounded Abstraction in AI Research Communities

Research in AI communities often prioritizes abstract computational logics, leading to a disconnect from real-world harms and ethical considerations. This can result in researchers feeling alienated, as their diverse backgrounds and critical perspectives are either ignored or superficially acknowledged. The current academic and community efforts to address these issues have not fully mitigated the persistence of sociotechnical harms and epistemic injustice within AI research.

Why it matters

This analysis highlights a critical disconnect between the abstract nature of AI research and its societal impact, potentially leading to the development of technologies that exacerbate harm or injustice. Addressing this alienation and fostering more inclusive research environments is crucial for ensuring that AI development is ethical, equitable, and aligned with broader societal needs, thereby mitigating future risks and enhancing the robustness of innovation.

Key insights

• Dominant AI research methodologies favor computational abstraction, sidelining critical knowledge and reflective practices.
• There is a noted misalignment between AI research goals and the practical application of these goals.
• Career pressures within AI research often suppress critical reflection.
• Existing academic and community initiatives have not fully resolved issues of sociotechnical harm and epistemic injustice.
• Early-career critical AI researchers experience alienation, feeling like outsiders due to overlooked or tokenized backgrounds.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.08408