Stop Asking If AI Should Be Regulated

Stop asking whether to regulate AI, start asking what it’s for.

The push comes from Matthias Oschinski and Mina Narayanan of the Center for Security and Emerging Technology (CSET), who argue in a Newsweek op-ed that governance should anchor innovation to a public purpose rather than chase the latest buzzwords. The authors contend the real debate isn’t innovation versus regulation, but innovation guided by purpose versus innovation left to default incentives.

Policy documents show a shift in thinking that prioritizes outcomes over process. Oschinski and Narayanan sketch a framework where governance is not a blunt constraint but a compass: design rules that steer AI toward visible benefits and away from foreseeable harm. Instead of blanket bans or open-ended mandates, they advocate for a “purpose-driven” lens—assessing who benefits, who bears risk, and how to measure success in real-world use.

That stance lands on several practical implications for policymakers, industry, and the public. First, it pushes for sector-specific, use-case based oversight rather than one-size-fits-all rules. A health care diagnosis tool, a financial credit algorithm, and a hiring assistant each raise distinct questions about safety, accuracy, and bias. Policy documents show the call for tailored risk assessments, independent evaluations, and transparent decision-making that can be audited by outsiders when needed. Second, it foregrounds governance mechanisms that align incentives with societal goals. If default incentives reward speed to market over robust testing or fair outcomes, the regulation should realign those incentives through licensing, disclosure requirements, or sunset clauses that force periodic re-evaluation. Third, it emphasizes accountability as an ongoing, adaptable process. Rather than a single compliance deadline, the approach requires ongoing monitoring, red-teaming, and responsive updates to reflect new capabilities.

From a practitioner’s lens, the argument maps onto dozens of real-world tradeoffs. A purpose-driven regime can reduce unnecessary frictions by focusing on demonstrable harms and clear benefits, but it also demands credible measurement of those outcomes. That means investing in independent testing, robust data provenance, and transparent reporting that holds developers and operators to verifiable standards. It also means coping with regulatory lag: as AI advances in weeks, not years, governance must be nimble enough to adjust risk thresholds without becoming advisory bureaucracy. And it requires international coordination to prevent regulatory arbitrage, since AI deployments routinely cross borders and affect supply chains, finance, and information ecosystems worldwide.

The upshot for regular people is tangible. When governance centers on purpose, you get clearer explanations for automated decisions, safer products and services, and less exposure to deceptive or harmful AI use cases. The risk, of course, is policy drift: if the frame shifts too often or is captured by narrow interests, the goal of public good could blur. The op-ed’s core message is a reframing exercise for lawmakers and regulators: design rules that articulate why AI is being deployed, what outcomes matter, and how to measure success over time. It’s a call to move beyond the question of whether to regulate AI toward a more precise question of what governance is for.

As Oschinski and Narayanan put it, governance should steer innovation toward purpose, not allow incentives to drift toward unchecked capability. The lever is not simply more rules, but smarter ones that connect technical possibility with human values.

Sources

Stop Asking Whether to Regulate AI—Start Asking What It’s For