Democracy in the Dark: Why AI Transparency Matters

Joe Kwon is a technical policy analyst at the Center for AI Policy.

Consider how social media platforms have shaped our political debates in recent years—recommending posts to millions of people based on largely hidden algorithms, with little public insight into how those recommendations are made. The spread of disinformation and manipulation during elections is just one reminder that when powerful AI systems operate in the dark, democratic processes can be undermined. As AI technology races forward, already capable of generating everything from hyper-realistic deepfakes to automated hiring decisions, policymakers are scrambling to address a wide range of risks, from job displacement to misuse by hostile actors.

In these debates, one strategy stands out for its ability to both protect innovation and bolster public trust: transparency. In practical terms, AI transparency means enabling the public and independent experts to understand key details about how AI models are built, the data they’re trained on, and the dangers they might pose, without necessarily disclosing every line of proprietary code. This is akin to “nutrition labels” for AI: enough clarity to evaluate the risks, but not so much that it stifles healthy competition.

While some proposals for broad AI regulation spark concerns about slowing America’s competitive edge, doing nothing risks letting unaccountable systems quietly shape our politics, consumer markets, and national security. Transparency offers a more balanced path: an approach that keeps AI developers moving forward responsibly, while ensuring the public and its representatives aren’t left in the dark. It resonates with a fundamental democratic principle: in order to govern effectively, we need to see what we’re governing.

Why transparency matters for democracy

A bedrock principle of democracy is that citizens should have enough information to understand and shape the forces governing their lives. Yet as AI permeates everything from law enforcement to social media, many of its most powerful algorithms remain black boxes. Consider the controversies over social media recommendation systems, which have been accused of fanning misinformation and intensifying political polarization. The Cambridge Analytica scandal in 2016 revealed how data-driven targeting could exploit user information for political gain, exposing how opaque AI tools can undermine public trust and democratic processes.

This dynamic is not new: throughout history, when critical systems – from financial markets to public health programs – operated behind closed doors, the result was often backlash and overcorrection. The 2008 financial crisis, for example, was partly fueled by opaque mortgage-backed securities and complex risk models that few people understood. Once the crisis hit, lawmakers responded with sweeping reforms that many in the financial sector argued were overly restrictive. The lesson is straightforward: a lack of transparency not only erodes public trust but can spur reactionary crackdowns that neither industry nor civil society truly wants.

In the AI context, transparency helps prevent those same pitfalls. When citizens and their representatives can see who’s building these models, what data they rely on, and how they might influence elections, employment prospects, or access to crucial resources, they have the information they need to influence and oversee these systems. Without such insight, power concentrates in the hands of a technical elite, leaving everyone else to blindly accept or reject outcomes they don’t understand—a fundamentally undemocratic state of affairs.

Crucially, transparency must go beyond jargon-filled technical documentation. Just as financial disclosures include both detailed SEC filings and user-friendly summaries for everyday investors, AI disclosures should meet different audiences where they are. Technical experts need in-depth reports to evaluate safety; lawmakers and the public need clear, plain-language explanations of how an AI system might affect their lives or present broader risks.

A well-designed transparency framework can also bolster national security rather than undermine it. Policymakers worried about an AI “arms race” with other countries often default to secrecy. Yet that approach can leave them with scant visibility into how or where advanced AI systems are actually deployed, creating blind spots for genuine threats. By establishing a carefully managed disclosure framework, through structured reports and capability assessments, government agencies and authorized experts can identify security threats early, without forcing companies to reveal proprietary code. This balanced approach recognizes that democratic oversight and national security can reinforce rather than undermine each other.

A bipartisan path forward

Transparency draws support from across the political spectrum because it provides a balanced solution—one that fosters accountability without hindering innovation. It resonates with conservatives who prefer limited government interference, progressives who champion consumer and civil rights protections, and moderates looking to maintain stable, predictable growth in emerging technologies.

“Transparency is the best way to build in both accountability and trust that artificial intelligence systems are working responsibly, especially as more industries adopt these tools,” said Sen. Gary Peters (D-MI) in 2023. “Americans should know when they are interacting with automated systems that are making critical decisions that could impact their health, finances, civil rights, and more.”

This emphasis on openness is echoed by leaders who see it as part of a common-sense approach to governing AI. “License requirements, clear AI identification, accountability, transparency, and strong protections for consumers and kids—such common-sense principles are a solid starting point,” said Sen. Richard Blumenthal (D-CT). “We know what needs to be done—the only question is whether Congress has the willingness to see it through,” added Sen. Josh Hawley (R-MO).

Even outside of Congress, the importance of transparency remains a recurring theme. “In some cases, industry standards may replace or substitute for regulation, but regulation has to be part of the answer,” said former President Barack Obama in 2022. He also urged technology companies to be more open with researchers and regulators about how their products are designed. “At minimum, [they] should share… how some of their products and services are designed so there is some accountability.”

Such broad agreement reflects a growing consensus that AI must not remain a black box shielded from public scrutiny. By clarifying who builds these systems, how they function, and where their data comes from, transparency fosters trust and deters hidden abuses that erode public confidence. Far from smothering innovation, it can encourage responsible growth that enjoys sustained support from citizens, investors, and policymakers alike.

Practical steps toward AI transparency

We don’t need to reinvent the wheel to make AI more transparent. Some leading AI labs like OpenAI, Anthropic, and Google have begun using standardized model cards and ‘frontier scaling policies’, though adoption remains uneven across the industry. Policymakers can encourage broader use of these emerging best practices, which provide clear documentation of an AI system’s intended uses, training data, and potential dangerous capabilities, particularly in sensitive areas such as cybersecurity and chemical, biological, radiological, or nuclear (CBRN) risks. Here’s how this can look in practice:

1. Standardized Model Cards with Risk Assessments: Model cards should serve as concise, structured “nutrition facts” for AI, outlining an AI system’s intended uses, overarching data sources (e.g., scraped web text or curated medical datasets), and any known limitations or potentially harmful capabilities. They can also include key performance metrics, testing methodologies, and any evaluations for dangerous features, such as generating malicious code. By making this information accessible, companies give policymakers, researchers, and everyday users a clearer view of how AI models may impact society.
2. Frontier Scaling Policies and Commitments: For cutting-edge “frontier” AI systems—those approaching human-level performance on certain tasks or carrying high-stakes risks—developers can adopt explicit thresholds that trigger additional oversight. If a model is found to possess advanced capabilities that could be misused for cyberattacks, disinformation, or other malicious purposes, labs should commit to specific actions, such as external audits, phased release strategies, or formal risk reviews. These commitments enhance public trust and provide guardrails to ensure that new safety measures match new leaps in model performance.
3. Tiered Disclosure Aligned with Risk: Not every AI poses the same level of threat. A small startup creating an automated help-desk chatbot should not face the same disclosure obligations as a company deploying a model with significant cybersecurity or biological misuse potential. Policymakers could establish tiers of transparency requirements ranging from basic disclosures (like a simple model card) for lower-risk applications to more rigorous audits, “white-box” evaluations, and additional reporting for powerful or high-risk systems. This approach prevents overburdening smaller players while ensuring that advanced AI models receive the scrutiny they warrant.

By standardizing such disclosures and linking them to a model’s potential for harm, policymakers and developers can ensure AI development proceeds safely, democratically, and responsibly. If adopted widely, these practices would go a long way toward building public trust and preventing the kind of secrecy that invites backlash and overcorrection.

Transparency is an ongoing commitment

It can be tempting to treat AI governance as a single pass-fail moment: either it’s figured out, or it isn’t. In reality, securing the responsible use of powerful technologies is an ongoing commitment—one that must adapt as AI evolves. Transparency is key to making this commitment work in practice. By insisting on visibility into how AI is designed, tested, and deployed, democratic institutions can keep pace with innovation, rather than scramble to react once mistakes or abuses come to light.

Democracy thrives when decisions are made in the open, not behind closed doors. The same principle applies to AI. While the future of these systems can feel uncertain, transparency provides the insights needed to adapt, without resorting to panic or broad prohibitions. It prevents the crises of confidence that often prompt heavy-handed legislation, and it grounds policymaking in facts rather than fear. If the United States truly cares about safeguarding its democratic values while fostering responsible innovation, then requiring transparency from AI developers is a logical first step—one that unites rather than divides. At a time when innovation, national security, and accountability all sit on a knife’s edge, that unity may be the most valuable outcome of all.