Transparency in
Generative AI Systems

GenAI technologies are rapidly being embedded in products, workplaces, and everyday decision-making. As these systems become more powerful and pervasive in our workplaces and lives – while also evolving into increasingly autonomous AI agents – the need for transparency has never been greater. Transparency is fundamental to responsible and sustainable adoption: it helps people understand AI systems and their outputs in ways that empower them to meaningfully use, trust, and adopt AI tools.

This playbook is for organizational leaders seeking to embed transparency into the design, deployment and governance of genAI systems, offering practical strategies to operationalize transparency across the AI lifecycle.

In practice, transparency is how organizations turn generative AI from a noisy black box into a system people can interpret, use, and govern responsibly.

Five things every leader should understand

10 actionable plays across three categories

AI transparency means enabling people to understand AI systems well enough to meaningfully use, trust, and adopt them. For business leaders, transparency is increasingly essential to responsible deployment: supporting better decision-making, accountability, and stakeholder confidence.

Rather than a single or fixed concept, transparency is better understood as an end goal: ensuring that different people have the information and understanding they need to engage with AI systems in meaningful and responsible ways. Because stakeholders vary widely, their transparency needs also differ. What counts as "transparent" for an engineer looks very different from what is useful for a customer or regulator.

Transparency is a critical AI governance issue and a core principle in responsible AI. In a review of AI ethics frameworks, transparency was the most widely cited principle across guidelines from companies, governments, and civil society – alongside fairness, safety, security, privacy and accountability.^[3] Yet it remains limited in practice, including in generative AI (genAI). The stakes are rising as AI systems move from chat interfaces to agents that take autonomous actions and integrate into organizational workflows.

This playbook focuses on what business leaders and managers can do to bridge the gap between transparency as a principle and its implementation in practice. It's not a technical guide to interpretability research or a debate on open vs. closed models. It's a practical resource with leadership and organizational strategies to implement actionable transparency and build the kind of informed trust that enables responsible adoption.

This is not an individual business issue, it's a collective opportunity. The AI transparency landscape is still fragmented and rapidly evolving, which means the choices organizations make now will help shape emerging norms and can inform standards within and across industries. The costs of opacity are visible: loss of trust, regulatory scrutiny, and real harm to people. The opportunity is also clear. When AI systems are transparent, trust can grow among users, organizations, and the broader ecosystem. The choices that business leaders make today about how to build, deploy, and govern AI systems will help define what trustworthy AI looks like for their organizations and society more broadly.

What is AI transparency and why does it matter?

AI transparency refers to the extent to which an AI system – and the decisions or outputs it produces – can be understood by the people who build, deploy, and use it. It involves opening the curtain on how an AI system works in practice: what data it draws from, how it generates results, what limitations it has, the decision-making processes that went into its development, and how it is governed.

For organizations, transparency is not simply a technical concern, it is foundational to responsible adoption, supporting trust, accountability, effective oversight and informed use.^[4] Without it, stakeholders can't evaluate AI outputs, catch errors, or know when a system is being used appropriately, leaving organizations exposed to risk they can't see.

GenAI raises the stakes

Transparency has long been an important topic in machine learning systems, which learn from massive amounts of data to find patterns and make predictions. Foundation models powering genAI systems continue to scale rapidly, trained on ever-larger datasets and powered by increasing amounts of compute. Yet their behavior remains difficult to fully predict or explain.

Unlike earlier systems that primarily classified or ranked information, genAI systems generate new outputs, including text, code, images, audio and more. They can produce responses that sound confident but are inaccurate ("hallucinations"), and it is often unclear why a particular output was produced or how a system arrived at it. This creates significant challenges for oversight, accountability, and responsible use.

As these systems are increasingly deployed as agents capable of taking autonomous actions within organizational workflows, this opacity becomes even more consequential: errors don't just mislead, they act.

Transparency is multi-dimensional

Transparency is not a single feature or checkbox.^[5][6] It encompasses several dimensions that work together to support meaningful understanding and oversight.

These include: visibility, explainability, interpretability, openness, and accessibility (see Box 1). Achieving transparency requires addressing these dimensions holistically. Visibility ensures that stakeholders are informed about the presence and role of AI in the first place. Explainability and interpretability focus on how decisions and processes are understood. Openness focuses on disclosing information about the design, development, and operation of AI systems. Accessibility ensures that information is understandable and usable by all relevant stakeholders.

Relying on any one dimension or mechanism alone, such as a model card or a disclosure label, is insufficient. Importantly, transparency is not only about technical understanding: it also includes how AI systems shape organizational decisions, worker experiences, and broader societal impacts.

Transparency for whom?

Transparency means different things to different stakeholders. What counts as "transparent" depends on who is interacting with the system and what they are doing with it.

User transparency helps end users understand and trust both AI outputs (e.g., through citations and confidence scores) and the broader AI system (e.g., its data use and retention, and any external influences), enabling them to make informed decisions. Meanwhile, developer transparency supports system builders to understand, debug, and enhance AI models by providing insights into their internal workings. Organizations adopting third-party models also need this kind of transparency from model providers in order to make informed decisions and support accountability. Regulatory transparency and societal transparency support external accountability, compliance, and public legitimacy.^[7]

In a 2026 survey, the UCB Responsible AI Initiative asked 400 end users, developers, and policymakers globally what transparency means to them, which features matter most, and where the biggest gaps exist. Trusting accuracy and protecting privacy emerged as the top two priorities overall, with 73% of respondents selecting accuracy as their primary transparency need, followed by privacy and data protection at 49%. Notably, these two priorities were consistent across respondents in high, middle, and low-income countries.

Across thirteen transparency features ranging from user-facing to governance-related, end users prioritize citations and sources, privacy information, and confidence indicators, reflecting their need to trust that outputs are accurate and that systems are trustworthy. Developers and policymakers place greater weight on training data information, independent audits, and societal impact, reflecting their more governance-oriented roles. Yet across groups, features considered most important are consistently the least available, particularly for end users.

The implication for business leaders is clear: stakeholders want more transparency than they are currently getting. Also, needs differ across groups. Transparency is best understood as an end goal: enabling different groups to engage with AI systems meaningfully and responsibly, rather than a one-size-fits-all definition or technique.

The current state of genAI transparency

Despite widespread agreement that transparency is important, genAI transparency remains limited.

On openness, Stanford University's Foundation Model Transparency Index has scored foundation model developers since 2023 on transparency on 100 transparency indicators. Its most recent 2025 edition found that companies scored worse than the prior year, with a 17-point average decrease from 2024.^[8] Training data continues to be particularly opaque with little disclosure on copyright and licensing, and many companies still don't share basic information such as model size and architecture. That said, not all companies declined: Writer and IBM significantly increased their scores.

On interpretability, core technical approaches that might support transparency, such as interpretability and mechanistic understanding of large models, remain an active area of research. While novel methods are evolving, including efforts to map internal concept representations and trace computational graphs inside models, reliable methods to explain why large language models produce particular outputs is lacking.^[9][10]

Yet, gaps in explainability and accessibility of information may be the most underresourced. Model cards are among the most common tools organizations use to demonstrate transparency, yet only 32% of end-user respondents in the UCB Responsible AI Initiative survey reported ever accessing them. This gap is telling: transparency efforts focused on technical documentation may satisfy developer or regulatory needs, but aren't reaching or serving end users. Without a foundational understanding of how AI systems work and what their limitations are, users risk becoming overtrusting or overreliant on these systems. This creates real liability and harm risks for individuals and the organizations deploying these systems.

The gap between the importance of transparency in principle and its limited realization in practice – compounded by fragmented and still-emerging standards for visibility and disclosure, documentation, and governance – motivates this playbook.

Transparency in AI can take various forms, but at its core, it's about ensuring that stakeholders have the information they need to engage with AI systems responsibly and effectively. In practice, it includes both what an AI system communicates directly to its users and what information is available about how the system is developed and governed.

Transparency in AI outputs and interaction

From a user's perspective, transparency often shows up through the system experience itself. These features shape day-to-day trust and usability. This includes:

• When content is AI generated
• Why the system produced a particular output
• How confident or uncertain the system is
• What the tool should and should not be used for
• What information or sources informed the response

Transparency in development and governance

Transparency extends beyond the interface. Many of the most important transparency signals depend on what organizations disclose about the system or tool behind the scenes, including:

• How the system was built and what data it relies on
• What personal data the system collects, how it is stored or used, and what control users have over it
• What limitations and known failure modes exist, and what societal or ethical impacts have been assessed (or not)
• Whether the system is regularly evaluated for safety, fairness, and reliability – and results of those evaluations
• What accountability mechanisms exist when harms or failures occur
• What oversight, auditing, or governance structures are in place, including whether reviews are conducted independently and whether commercial relationships influence system behavior or outputs
• How changes, incidents, and known issues are disclosed and addressed over time

Transparency in AI outputs and interactions shapes day-to-day usability, but development and governance transparency matters to all stakeholder groups, from developers and regulators to end users. The UCB Responsible AI Initiative survey found that information on personal data use and retention was one of the biggest transparency gaps for end users and knowing whether a system is trustworthy at all was a primary transparency need for end users. That kind of trust cannot be established through individual interactions alone, it requires clear communication about development and governance.

Together, these practices illustrate that transparency is not a single technical feature, but a combination of leadership and product design choices, documentation practices, governance structures, and accountability practices. These considerations become especially consequential for AI agents, where autonomous actions and embedded workflows raise the stakes for every item on this list.

For business leaders, the development and governance components of transparency are the most actionable starting points. Documentation, evaluation, governance processes, and accountability mechanisms determine what information is available in the first place.

With this in mind, we turn to the growing ecosystem of transparency tools and resources to support these efforts.

There are a growing number of tools and resources to help leaders increase transparency, improve oversight, and support more responsible deployment. These tools can serve different purposes: some are designed to help organizations document and communicate how AI systems work, while others are intended to test, measure, and evaluate system behavior in practice.

It is important to note that most organizations adopting genAI are not building foundation models from scratch. Instead, they are integrating commercially available models into products, services, or internal workflows. In these cases, business leaders may have limited ability to influence upstream transparency practices such as training data disclosure or provider-issued model documentation. However, organizations have influence. They can factor transparency into model selection – choosing providers with greater openness about training data, governance, and system behavior – and voice expectations directly to their providers. Customer demand is an important lever for improving upstream transparency practices.

More broadly, organizations can implement meaningful transparency at the system level. Even when an underlying model is relatively opaque, developers and managers retain meaningful control over consequential transparency decisions: how AI is introduced to and communicates with users, what documentation accompanies the system, and what evaluations and benchmarks are conducted and whether results are shared.

We organize transparency tools into two broad categories:

• A) Documentation tools – which help organizations record, communicate, and govern key information; and
• B) Benchmarks and evaluations – which assess system performance, risks, and reliability through structured testing.

Capturing and communicating how AI systems work

Documentation tools support transparency by capturing key information about how an AI system was built, what it's intended to do, and what limitations or risks it may introduce. They are important for oversight and accountability across the AI lifecycle. We organize documentation tools into four levels – dataset, model, system, and practice – based on where the documentation is targeted and who is responsible for creating it.^[16] While dataset and model documentation are primarily the responsibility of model providers, system and practice-level documentation are where deploying organizations have the most direct control and the most immediate opportunity to act.

Dataset level documentation

Dataset level tools focus on transparency in the data used to train or finetune AI systems. They help stakeholders understand: where data came from; when it was collected or created; what populations or content are represented; and what biases, gaps, or risks may be present.

Typically created by: Dataset creators, data scientists, ML engineers

Examples: Datasheets for datasets, Dataset nutrition labels

Model-level documentation

Model-level tools provide standardized information about the underlying AI model, including: intended use cases; performance characteristics; training data sources and evaluation results; and ethical considerations and known failure modes.

Typically created by: Model developers, ML engineers, foundation model providers

Examples: Model Cards, Nutrition labels for models

System-level documentation

System-level tools describe an AI system in its full real-world context. They typically document: the system's purpose and intended users; how the model (or multiple models and components) is integrated into the broader product or workflow; and known limitations, safety measures, and governance practices. Unlike model-level documentation, system-level documentation captures the full deployment context: including prompts, interfaces, safeguards, and governance processes. Even when the underlying model is opaque, organizations can document how their system is intended to operate, what risks it introduces, and what accountability mechanisms are in place.

Typically created by: Product leaders, system owners, deployment teams, risk and compliance

Examples: System Cards, AI FactSheets (also covers model-level documentation), Reward Reports, Application Cards

Practice-level documentation

Transparency also depends on organizational processes, standards, and governance practices (not only technical artifacts). These practice-level tools may include: disclosure and reporting practices; internal governance frameworks; protocols and standards for responsible development; and mechanisms for accountability and recourse.

Typically created by: Business leaders, legal and compliance teams, policy and governance teams

Examples: content provenance standards (e.g., C2PA), transparency reporting frameworks, watermarking, organizational transparency policies

Assessing system performance and reliability

Benchmarks and evaluations are two related but distinct tools for assessing AI system performance. Benchmarks are standardized tests designed to measure capabilities such as accuracy, speed, reasoning ability, and reliability that enable comparison across models and over time. Evaluations are context-specific assessments of how a system behaves in a particular product, workflow, or user environment. Both matter, but they answer different questions: benchmarks ask "how does this model perform generally?" while evaluations ask "how does this system perform for us?"

Benchmarks are most often designed and run by foundation model developers, researchers, and infrastructure teams. Today there are three broad categories of benchmarks:

• General performance benchmarks, which measure technical efficiency (e.g., speed, latency, resource use)
• Model capability benchmarks, which test reasoning, instruction-following and task performance (e.g., coding or math)
• Standardized test benchmarks, which compare model performance to human performance on exams or expert tasks

While relatively smaller and less popularized to date, there is another category of benchmarks emerging: Human impact benchmarks, which analyze the impact of models on physical, societal, and psychological dimensions (see for example: MIT's Human AI Impact Benchmark).

While deployers don't usually create benchmarks themselves, they rely on them to compare models when selecting vendors, understand high-level capabilities and limitations, support due diligence and procurement, and demonstrate oversight to regulators, customers, or internal stakeholders.

Benchmarks are useful but incomplete. They can be narrow, easy to game, or unrepresentative of real-world use. For example, they often assume an average English speaking user or ideal conditions. High benchmark scores do not guarantee safety, fairness, or reliability in practice. Hence, benchmarks are one input among many. Benchmarks also reflect value judgments about what matters. What gets measured, and what is left out, signals which outcomes are prioritized. When benchmarks emphasize aggregate performance or average users while excluding bias, fairness, or differential impacts, high scores raise an important question: for whom, and in what context, is this the "best" performance? For business leaders, this means benchmark scores alone are an insufficient basis for deployment decisions, especially in high-stakes or diverse user contexts.

Evaluations can complement benchmarks by testing performance in context, but can also be limited. They vary widely in rigor and scope, and can reflect the same blind spots as the teams that design them. Together, benchmarks and evaluations are important inputs, but aren't substitutes for ongoing human oversight and governance.

System and agent benchmarks offer one way to make evaluation more concrete by measuring how AI performs on structured, real-world tasks rather than isolated capabilities in lab settings. For example, HumanEval measures code generation accuracy and is widely used to evaluate coding assistants like GitHub Copilot and Amazon CodeWhisperer. OSWorld measures task completion in desktop environments and has been used to evaluate agentic systems like Claude's computer use capability. As AI agents take on more autonomous, multi-step workflows, benchmarks like these are an important transparency tool, though the space is rapidly evolving. While there is some convergence towards certain benchmarks (e.g. HumanEval), the rapid proliferation of benchmarks can make it challenging to draw accurate comparisons across systems and agents and results should be interpreted carefully.

Despite the increasing recognition that transparency is important and a proliferation of tools, achieving meaningful transparency in practice remains difficult. Several interconnected challenges stand in the way.

1. The "Black box" problem

AI models, particularly deep learning models including genAI, are inherently complex and operate as "black boxes" making it difficult to understand their decision making processes.^[20][21] This opacity poses significant risks in high-stakes domains like healthcare and finance, where issues of accountability, bias, and trust are paramount.

Explainability tools can offer approximations or high-level insights into why a decision was made. This area of research, often called explainable AI (XAI), provides techniques to clarify AI decisions in ways that humans can understand and mitigate effects of the black box problem.^[22] Researchers have found that highly complex models often provide superior predictive accuracy but are inherently difficult to interpret, creating a tradeoff between performance and interpretability.^[23][24]

2. Corporate opacity and balancing tensions

Companies developing foundation models are often not transparent about their models, withholding details about training data, model architectures, and decision-making processes.^[25] Opacity exists at the system level as well: organizations deploying AI may not disclose how they have configured, fine-tuned, or adapted models. At the product level, organizations may omit uncertainty signals or confidence indicators from user-facing products to protect product perception. This layered opacity exacerbates the black box problem, hindering comprehensive evaluation by users, researchers, and regulators.

This opacity stems from multiple sources. Some are legitimate protective concerns, such as: guarding against misuse by bad actors, protecting user privacy, or managing liability risks associated with training data. Others are more commercial in nature, such as: protecting proprietary model architectures and training methods from competitors, protecting the company from liability in the case of scraping and using training data that is copyright, or protecting product perception.

It's worth noting that some of the recommendations in this playbook are harder to implement because of foundation model opacity. Organizations deploying third-party models may find it difficult to document training data, assess bias, or disclose known limitations when that information is not made available by their provider, underscoring the importance of factoring transparency into vendor selection (discussed in Play 2).

3. Unintended trust consequences and information overload

If explanations or transparency is unclear or if biases and inaccuracies in the AI system are revealed, it can result in loss of trust. Further, high levels of transparency can result in a sense of information overload. There is a balance to strike with different stakeholders.^[26] Striking a balance between faithful (i.e. accurate reflections of the inner workings of the model) and explanations that are understandable to non-technical end users is difficult.^[27]

Researchers highlight that effective transparency is about explanations that are both faithful (i.e. accurate about the AI system) and understandable to the intended audience.^[28] This reinforces the point that transparency looks different for different stakeholders.

4. Pace of AI development and model change

AI models and systems are evolving quickly. New models are released frequently, capabilities change, and organizations face constant pressure to adopt the latest tools. This creates a transparency challenge as documentation, disclosures, and governance processes built for one model or system may become outdated quickly. Meanwhile, the volume of new deployments and transparency needs outpaces the teams responsible for producing and maintaining them. Smaller organizations or those without responsible AI functions may face particular challenges keeping pace. The result is transparency processes that go stale or become superficial checkbox exercises rather than meaningful accountability mechanisms.

5. Lack of standards and evolving regulation

At a higher level, there is a lack of standardization in regards to what transparency should look like in practice. Organizations face a fragmented landscape of documentation frameworks and tools with limited guidance on when each is appropriate, for whom, or what level of detail is sufficient. This fragmentation makes it difficult for stakeholders to interpret transparency efforts in a consistent, comparable, or meaningful way.

Legal and ethical expectations around transparency continue to evolve (see Box 2). Despite increasing regulatory calls for transparency, current legal frameworks often lack specificity about how transparency should be implemented, particularly for organizations deploying systems built on external models. While laws are converging on requirements for documentation, risk assessment, and governance, a clear need for standardized practices and reporting tools remain, especially at the system level.

As a result, regulatory compliance often establishes a minimum floor for transparency. This leaves substantial responsibility, and opportunity, for organizations to go further.