# Dead Zone of Accountability: Why Social Claims in Machine Learning Research Should Be Articulated and Defended

arXiv: https://arxiv.org/abs/2508.08739 · HTML: https://mlsanity.com/paper/2508.08739/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2025.

## Built on by (most cited first, 1 of 1)

- [The Aftermath of DrawEduMath: Vision Language Models Underperform with Struggling Students and Misdiagnose Errors](https://mlsanity.com/paper/2603.00925/) (2026)

## Abstract (arXiv)

Many Machine Learning research studies use language that describes potential social benefits or technical affordances of new methods and technologies. Such language, which we call "social claims", can help garner substantial resources and influence for those involved in ML research and technology production. However, there exists a gap between social claims and reality (the claim-reality gap): ML methods often fail to deliver the claimed functionality or social impacts. This paper investigates the claim-reality gap and makes a normative argument for developing accountability mechanisms for it. In making the argument, we make three contributions. First, we show why the symptom - absence of social claim accountability - is problematic. Second, we coin dead zone of accountability - a lens that scholars and practitioners can use to identify opportunities for new forms of accountability. We apply this lens to the claim-reality gap and provide a diagnosis by identifying cognitive and structural resistances to accountability in the claim-reality gap. Finally, we offer a prescription - two potential collaborative research agendas that can help create the condition for social claim accountability.