Evidence record 19037 · automatically gathered

No One to Blame: A Framework of Constitutive AI Unaccountability

The increasing deployment of autonomous, agentic AI systems challenges traditional accountability mechanisms. Existing research predominantly frames AI accountability gaps as barriers that can be overcome through better standards, transparency, and institutional reform. We argue that this framing is insufficient: certain configurations of actors, systems, and institutions render AI accountability conceptually unachievable regardless of effort. We introduce the concept of constitutive AI unaccoun

Record details

Published: 12 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Agents & autonomy · Transparency
Retrieved: 13 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Agents & autonomy · Shared topic: Transparency

Convergent Detour Hijacking: Task-Preserving Resource Amplification in Skill-Based LLM Agents

arXiv · 12 August 2026

Shared topic: Agents & autonomy · Shared topic: Transparency

Beyond Memory: A Transactional Continuity Kernel for Long-Lived AI Agents

arXiv · 12 August 2026

Shared topic: Agents & autonomy · Shared topic: Transparency

Unmasking Toxic Mimicry in Medical Offline Reinforcement Learning for ICU Sepsis Management via Counterfactual Clinical Audits

arXiv cs.CY · 13 August 2026

Shared topic: Agents & autonomy · Shared topic: Transparency