The AI Accountability Ecosystem in the Era of Language Models

Chris Percy; Artur d'Avila Garcez

cs.CYApr 29, 2026

The AI Accountability Ecosystem in the Era of Language Models

Authors: Chris Percy, Artur d'Avila Garcez

Organizations: University of Warwick, Coventry, CV4 7AL, UK · City St George’s, University of London, London, EC1V 0HB, UK

Abstract

This article reviews and updates the framework for accountability in AI based on account- ability ecosystems. We update the framework in light of the latest developments since the release of Large Language Models for general public use. We propose three interlinked updates to the original AI accountability ecosystem: (i) reorienting the accountability ecosystem to AI infrastructure and supply chains, (ii) providing greater emphasis on outcomes monitoring and identification of issues that support decentralized system improvement, and (iii) incorporating end-user accountability given the new risks of unpredictability of language models in-the-wild. Collectively, these updates mark a shift towards accountability as distributed, continuous, and institutionalized, away from a system in which frontier AI applications can be modeled as discrete products controlled by single identifiable actors with industry-specific oversight.

Figures & tables

Figure 1: The Accountability Ecosystem fit for LLMs (simplified schematic for a mixed market economy in a democracy). The framework identifies the core supply chain of AI systems as encompassing three sets of actors: frontier model developers, application developers, and users. Those involved in model assessment, market participants, and government actors form three consecutive layers that interact with the core supply chain, having access to multiple accountability mechanisms and systems to hold the core supply chain to account, but with increasingly reduced reaction speed and directness of control. Outcomes need to be monitored and risks managed for all six sets of actors in the AI ecosystem.

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Asymmetric Communication: Large Language Models and Language Games

Enzo Fenoglio

Dept. Computer Science University College London 66-72 Gower Street, London WC1E 6EA, UK

Artificial Intelligence Alignment Moral Reasoning Language Pairs Cognitive Science

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Contemporary AI discourse attributes to language models properties they cannot bear: general intelligence as substrate-independent cognition, hallucination as cognitive failure, agency as autonomous goal-pursuit, sentience as emergent inner life, alignment as goal synchronization. This paper argues that these are instances of a single category mistake--properties constituted within human communicative practice are projected onto the machine side--and explains its structure. Human-LLM interaction constitutes a language game in which one side bears all normative activity. We call this configuration asymmetric communication since model outputs circulate communicatively, entering further exchanges, without the system undertaking commitments, bearing entitlements, or performing the assessment on which discursive standing depends. Three conditions define the asymmetry: (i) correctness is enforced exclusively by the receiver; (ii) accountability is borne by human participants alone; and (iii) the practical standing of any output depends entirely on human uptake. These conditions are structural, hold independently of capability, and remain unchanged as more powerful models raise the stakes of misattribution. The framework draws on Wittgenstein (meaning enacted in shared practices), Luhmann (communication completed on the receiver's side), Esposito (algorithmic contingency sufficient for uptake), and Brandom (normative scorekeeping as the source of discursive standing). Applied to all five, it reclassifies each as a receiver-side phenomenon, grounds guardrails as structural necessities rather than manifestations of machine moral agency, and yields an implication for AI governance. Alignment is institutional constraint engineering, not goal synchronization between agents, while responsibility remains with human institutions.

Abstract

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