To regulate algorithms, regulators must ensure that transparency leads to accountability.
Algorithmic transparency requirements—which require organizations to disclose how their automated systems work—have expanded across regulatory regimes under a shared premise: Making automated systems legible reduces harm. Two very different disclosure regimes, however, operate under the shared label of transparency.
What I term “audit transparency” makes systems transparent to regulators. It provides regulators with access to system design, training data, and outcomes, narrowing the information gap that enables institutions to evade regulation. “Operational transparency,” however, runs toward the consumers, users, and other parties those systems evaluate, exposing algorithmic decision criteria to them directly. When operational logic becomes visible, those parties can adapt their behavior to satisfy known rules rather than the outcome the system is meant to achieve.
“Gaming the system” has always occurred in the gap between a metric and the outcome it is meant to measure. In opaque systems, exploiting that gap requires inferring how different inputs affect the final decision. But when the decision rule is visible, actors know how inputs affect the outcome, and the algorithm itself becomes the governing rule. Actors can observe which inputs move a score, trigger a threshold, or change a ranking and adjust their behavior accordingly. Once the criteria by which decisions are evaluated become visible, optimization is no longer about improving the underlying outcome but instead satisfying the metric.
Credit scoring illustrates this pattern. As individuals began to understand the logic of credit scoring systems, an industry emerged to engineer inputs that maximize scores even without improving an individual’s underlying creditworthiness. Internet search ranking shows the same dynamic. Once people understood the elements of how search results are ranked, a parallel optimization ecosystem emerged that adapted web content to satisfy algorithmic signals rather than improve the content’s informational quality.
Operational transparency mandates can unintentionally reproduce these conditions. When a regulation requires the broad disclosure of an algorithm’s details, it makes that algorithm legible to the parties it governs, reshaping those parties’ decisionmaking. Actors being evaluated by the algorithm can adapt their inputs faster than an institution can adjust its algorithm in response to that gaming. Instead of regulators shaping institutional behavior through regulatory standards, the algorithm’s disclosed criteria—not the regulator’s intent—become the rule that governs outcomes.
The question is: When is this risk significant enough to make operational transparency the wrong approach? Operational transparency should be treated as risky when three conditions are met: the actors facing the algorithm have strong incentives to optimize against the institution’s decision system, they have the technical capacity to infer how inputs affect decisions, and a gap exists between what the algorithm measures and the outcome it is meant to achieve. The credit scoring and search ranking cases above satisfy all three criteria. Where all three are present, regulators should opt for audit transparency—allowing themselves insight into algorithms—rather than operational transparency, which requires disclosure of operational criteria to the parties an algorithm governs.
But limiting operational transparency has real costs. People subject to automated determinations deserve to know that an algorithm was involved, understand what general categories of information the algorithm used, and have access to meaningful recourse. Without detailed information on an algorithm’s inner workings, individual rights to challenge decisions are harder to exercise. The design question for regulators is: How much operational detail enables meaningful recourse without providing a roadmap for institutional gaming?
One might argue that public transparency is necessary because regulatory agencies themselves may fail to enforce. Some might go further, arguing that public disclosure of operational criteria is necessary precisely because it enables individual legal challenges, giving affected parties the information needed to contest decisions when regulators have failed to act. But collapsing the distinction between audit and operational transparency does not solve the enforcement problem. It displaces it—from the regulator onto actors with no accountability for outcomes. The answer is to strengthen auditing authority, reporting requirements, and individual challenge rights, not to treat public disclosure of operational criteria as a substitute for institutional oversight.
Audit transparency is only as effective as the agency’s capacity to interpret what it receives. Strengthening that capacity requires independent auditors with authority to inspect proprietary models, mandatory adverse-outcome reporting, and legal avenues for affected individuals to challenge algorithmic decisions. Adverse-outcome reporting means an obligation on algorithm deployers to disclose to regulators, not to the public, statistically significant disparities in how a system performs across groups—before those disparities become visible through individual complaints. These steps will strengthen oversight without disclosing operational criteria to the public.
Controlled access models for sensitive data—where qualified researchers work inside secure environments without public release of the underlying material—demonstrate that tiered access is operationally feasible. The U.S. Census Bureau’s Federal Statistical Research Data Centers provide a working example: Researchers access sensitive, use-restricted data in secure environments without public release of the underlying data. A similar structure could govern algorithmic oversight. Detailed system information would be available to regulators, auditors, and qualified researchers under controlled access, expanding oversight without converting operational criteria into a public optimization roadmap. Researchers can be insulated from the political institutional incentives that distort regulatory enforcement, while also lacking the incentives to game the system.
The purpose of algorithmic regulation is not transparency for its own sake. It is accountability: the capacity to detect harm, assign responsibility, and compel change. Transparency directed at regulators expands that capacity. But transparency directed at the market may transfer strategic knowledge to actors whose incentives run counter to regulatory goals. The European Union’s Artificial Intelligence Act makes systems transparent to regulators, but emerging U.S. state laws have prioritized operational transparency and the right to seek explanations.
Gaming risk varies depending on who receives disclosure—regulators or algorithm-facing individuals—but regulators are making that design choice by default rather than by analysis. The operative question is not whether algorithmic systems should be transparent—they should—but to whom, under what access conditions, and at what stage of enforcement. Getting that choice wrong does not merely reduce regulatory effectiveness. It converts disclosure mandates into a strategic asset for actors optimizing for algorithmic criteria, rather than the regulator’s intended outcomes.
