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On March 13, 2026, the debate over how to regulate advanced machine learning systems continues to intensify. Policymakers face a difficult trade-off: they must shield individuals from harm while keeping the digital commons open enough to sustain invention and research. A practical approach reframes regulation away from policing every dataset used in model training and toward governing the services and outputs these models produce. By concentrating on what systems do in the world rather than every input that touched them, regulators can tackle real harms without chilling the routine use of publicly available information that fuels progress.
This shift also requires clearer expectations about how autonomous systems behave and how organizations can use data responsibly. Three complementary measures stand out: prioritize outcome-based rules that target harmful behavior, set transparency norms for autonomous agents, and create a legal safe harbor for sensible use of publicly available data. Together, these steps aim to protect people while preserving an open environment for developers, researchers, and entrepreneurs who depend on free-flowing information to innovate.
Regulate outputs rather than indiscriminately policing training inputs
Focusing regulatory attention on the outputs of AI systems means designing rules that address the concrete effects these systems have on people and markets. The alternative—attempting to control every piece of data used during model development—risks suppressing legitimate research and blocking beneficial services. An output-focused approach targets behaviors such as wrongful discrimination, defamatory statements, or automated high-risk decisions, and it places responsibility on providers to prevent and remedy specific harms. This model also allows for flexibility: as technology evolves, regulators can update safety thresholds and compliance mechanisms based on observable outcomes instead of chasing an unbounded list of forbidden inputs.
Establish transparency norms for autonomous AI agents
Autonomous agents—systems that plan and take actions with limited human oversight—introduce novel accountability challenges. Policymakers should require clear disclosure about when a user is interacting with an autonomous agent and what capabilities it possesses. Transparency norms should include provenance information, basic operational constraints, and summaries of risk mitigation measures. Such standards enable users, auditors, and affected parties to understand and contest harmful decisions. Rather than heavy-handed secrecy requirements, these norms should be proportionate and focused on meaningful information that improves oversight without exposing trade secrets or facilitating misuse.
Practical mechanisms to increase agent transparency
Concrete tools can make transparency workable: standardized labeling for agent behavior, machine-readable provenance metadata, and audit logs that record decision pathways. These instruments help regulators and independent auditors reconstruct how an agent reached a particular conclusion or action. Importantly, transparency should not be an end in itself; it must be paired with obligations to remedy harms, such as remediation funds or notice-and-correct processes. A balanced package of disclosure plus enforceable rights creates accountability while limiting burdens on innovation.
Create a safe harbor for responsible use of publicly available data
To preserve the open information ecosystem that underpins discovery, a limited safe harbor can protect entities that use genuinely public sources in good faith. This legal carve-out would shield developers who rely on content that is widely accessible online, so long as they follow clear responsible use rules—such as respecting copyright takedown mechanisms, avoiding targeted scraping of sensitive personal data, and implementing reasonable privacy safeguards. A thoughtfully designed safe harbor reduces legal uncertainty for researchers and startups while still leaving room for enforcement against bad actors who exploit public data to harm individuals.
Design considerations and limits for the safe harbor
Crafting the safe harbor requires careful boundaries: it should exclude willful misuse, deliberate evasion of privacy protections, and commercial exploitation that undermines rights holders’ legitimate interests. Eligibility criteria, oversight mechanisms, and sunset reviews can ensure the protection remains proportionate. Combining the safe harbor with the output-focused regulatory frame and agent transparency norms creates a coherent system: developers can innovate with public data, users gain clarity about automated behavior, and regulators can act when outputs cause harm.
Together, these three policy moves—prioritizing outputs, promoting transparency for autonomous agents, and offering a conditional safe harbor for use of publicly available data—provide a pragmatic pathway to safeguard individuals without stifling the experimentation that drives progress. By aligning rules with observable harm and enforceable responsibilities, governments can protect people while sustaining an open information environment that fuels innovation.