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

arXiv: https://arxiv.org/abs/2608.08408 · HTML: https://mlsanity.com/paper/2608.08408/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 90 references that are in the map.
- First posted on arXiv 9 Aug 2026.

## Builds on (most cited first, 12 of 12)

- [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021)
- [Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction](https://mlsanity.com/paper/2210.05791/) (2022)
- [Towards a Critical Race Methodology in Algorithmic Fairness](https://mlsanity.com/paper/1912.03593/) (2019)
- [Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence](https://mlsanity.com/paper/2007.04068/) (2020)
- [The Forgotten Margins of AI Ethics](https://mlsanity.com/paper/2205.04221/) (2022)
- [Model Positionality and Computational Reflexivity: Promoting Reflexivity in Data Science](https://mlsanity.com/paper/2203.07031/) (2022)
- [Black Feminist Musings on Algorithmic Oppression](https://mlsanity.com/paper/2101.09869/) (2021)
- [The Contestation of Tech Ethics: A Sociotechnical Approach to Technology Ethics in Practice](https://mlsanity.com/paper/2106.01784/) (2021)
- [Responsible AI Research Needs Impact Statements Too](https://mlsanity.com/paper/2311.11776/) (2023)
- [Epistemic Power in AI Ethics Labor: Legitimizing Located Complaints](https://mlsanity.com/paper/2402.08171/) (2024)
- [Articulation Work and Tinkering for Fairness in Machine Learning](https://mlsanity.com/paper/2407.16496/) (2024)
- [Whole-Person Education for AI Engineers](https://mlsanity.com/paper/2506.09185/) (2025)

## Abstract (arXiv)

Logics of abstraction in computational AI research often push important forms of knowledge and reflection aside: dominant standards of legitimacy separate from lived experience of harm; the goals of work misalign with the practices that operationalize them; and career demands crowd out critical reflection. Even as prior academic and community-oriented efforts have sought to recontextualize and challenge common practices, exposure to sociotechnical harms and epistemic injustice persists. As three early-career critical AI researchers, we experienced this as alienation: feeling like outsiders in our research communities. This alienation has involved having some aspects of our backgrounds overlooked and others tokenized. We argue our alienation occurred through mechanisms that mirror abstraction by creating distance from relevant material realities. Beyond abstraction's role in computational AI research as a foundational practice structuring complex computational tasks, we have encountered it as a social norm in computational research spaces, illustrated through an autoethnographic inquiry into our alienation. We narrate three vignettes describing how we encountered and resisted alienation in our research communities. By analyzing themes across these accounts, we construct an interpretive framework of alienation categorizing its preconditions, mechanisms, and harms. Finally, we identify affect abstraction, one of the mechanisms of alienation we describe, as a high-leverage mechanism that is resistible by staying attuned to our affective responses, and collective action as a way to reduce risk and isolation when engaging in resistance. To assist others with similar reflection, we present our framework as a hermeneutic resource. Critical self-reflection and meaning-making are necessary steps toward challenging exclusionary disciplinary norms and cultivating more inclusive forms of AI research.