The AI Accountability Ecosystem in the Era of Language Models

Accountability must extend beyond individual companies to entire AI supply chains: The paper argues that responsibility for AI systems can no longer focus solely on the company releasing a product, but must include all actors involved in the infrastructure and supply chain—from data providers to model developers to deployment platforms.

Continuous monitoring of real-world outcomes is now essential: Rather than one-time safety checks before release, the framework emphasizes ongoing monitoring to identify problems as language models are used "in-the-wild," allowing for faster detection and correction of issues that emerge unpredictably in actual use.

End-users bear new accountability responsibilities: Because large language models behave unpredictably in ways their creators cannot fully control, the paper identifies end-users themselves as part of the accountability ecosystem—meaning people using these tools share responsibility for how they're deployed and the consequences of their use.

Accountability must shift from discrete products to distributed systems: The traditional model where a single company controls and oversees an AI application no longer fits reality; instead, accountability needs to be "distributed, continuous, and institutionalized" across multiple actors and over time rather than treated as a one-time compliance event.