Currently, companies often frame scraping errors or data misuse as mere 'misalignment'—a technical glitch in the model's training process. This framing allows developers to treat copyright or privacy violations as debugging tasks rather than legal breaches. To change this, law must pivot toward institutional liability.
One path involves updating agency frameworks, such as the FTC's consumer protection rules, to treat unauthorized scraping as an unfair or deceptive trade practice. Instead of blaming a rogue algorithm, we hold the corporation responsible for the data supply chain. If a Large Language Model (LLM) ingests proprietary datasets without a license, the fault lies with the corporate governance, not the stochastic parrots themselves.
Furthermore, we need democratic oversight through mandatory transparency audits. Just as banks face strict regulatory scrutiny for systemic risks, AI developers should undergo third-party inspections of their training pipelines. This turns a technical debate into a matter of public accountability. When scraping becomes a matter of statutory compliance rather than code optimization, the stakes move from software patches to heavy fines and judicial injunctions. This shift ensures that the cost of infringing on human creativity remains higher than the cost of licensing it.
