The CLeAR Documentation Framework for AI Transparency: Recommendations for Practitioners & Context for Policymakers


Kasia Chmielinski* (Data Nutrition Project & Harvard University)
Sarah Newman* (Data Nutrition Project & Harvard University)
Chris N. Kranzinger* (Data Nutrition Project)
Michael Hind (IBM Research)
Jennifer Wortman Vaughan (Microsoft Research)
Margaret Mitchell (Hugging Face)
Julia Stoyanovich (New York University)
Angelina McMillan-Major (independent researcher)
Emily McReynolds (independent researcher)
Kathleen Esfahany (Data Nutrition Project & Harvard University)
Mary L. Gray (Microsoft Research, Indiana University, Harvard University)
Audrey Chang (Data Nutrition Project & Harvard University)
Maui Hudson (TKRI, University of Waikato)

*Joint first authors

The views expressed in Shorenstein Center Discussion Papers are those of the author(s) and do not necessarily reflect those of Harvard Kennedy School, Harvard University or the organizations and institutions with whom the authors are affiliated. 

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This report introduces the CLeAR (Comparable, Legible, Actionable, and Robust) Documentation Framework to offer guiding principles for AI documentation. The framework is designed to help practitioners and others in the AI ecosystem consider the complexities and tradeoffs required when developing documentation for datasets, models, and AI systems (which contain one or more models, and often other software components). Documentation of these elements is crucial and serves several purposes, including: (1) Supporting responsible development and use, as well as mitigation of downstream harms, by providing transparency into the design, attributes, intended use, and shortcomings of datasets, models, and AI systems; (2) Motivating dataset, model, or AI system creators and curators to reflect on the choices they make; and (3) Facilitating dataset, model, and AI system evaluation and auditing. We assert that documentation should be mandatory in the creation, usage, and sale of datasets, models, and AI systems.

This framework was developed with the expertise and perspective of a team that has worked at the forefront of AI documentation across both industry and the research community. As the need for documentation in machine learning and AI becomes more apparent and the benefits more widely acknowledged, we hope it will serve as a guide for future AI documentation efforts as well as context and education for regulators’ efforts toward AI documentation requirements.


Machine learning models are designed to identify patterns and make predictions based on statistical analysis of data. Modern artificial intelligence (AI) systems are often made up of one or more machine learning models combined with other components. While the statistical foundations of modern machine learning are decades old [1,2], technological developments in the last decade have led to both more widespread deployment of AI systems and an increasing awareness of the risks associated with them. The use of statistical analysis to make predictions is prone to replicate already existing biases and even introduce new ones [3], causing issues like racial bias in medical applications [4,5], gender bias in finance [6], and disability bias in public benefits [7]. AI systems can also lead to other (intentional as well as unintentional) harms [8,9], including the production of misinformation [10–13] and the sharing or leaking of private information [14].

With growing national and international attention to AI regulation [15,16], rights-based principles [17–21], data equity [22,23], and risk mitigation [24], this is a pivotal moment to think about the social impact of AI, including its risks and harms, and the implementation of accountability and governance more broadly. Most proposed AI regulation mandates some level of transparency, as transparency is crucial for addressing the ways in which AI systems impact people. This is because transparency is a foundational, extrinsic value—a means for other values to be realized. Applied to AI development, transparency can enhance accountability by making it clear who is responsible for which kinds of system behavior. This can lessen the amount of time it takes to stop harms from proliferating once they are identified, and provides legal recourse when people are harmed. Transparency guides developers towards non-discrimination in deployed systems, as it encourages testing for it and being transparent about evaluation results. Transparency enables reproducibility, as details provided can then be followed by others. This in turn incentivizes integrity and scientific rigor in claims made by AI developers and deployers and improves the reliability of systems. And transparency around how an AI system works can foster appropriate levels of trust from users and enhance human agency.

Transparency can be realized, in part, by providing information about how the data used to develop and evaluate the AI system was collected and processed, how AI models were built, trained, and fine-tuned, and how models and systems were evaluated and deployed [25,26]. Towards this end, documentation has emerged as an essential component of AI transparency [27] and a foundation for responsible AI development.

Although AI documentation may seem like a simple concept, determining what kinds of information should be documented and how this information should be conveyed is challenging in practice. Over the past decade, several concrete approaches, as well as evaluations of these approaches, have been proposed for documenting datasets [28–41], models [42–46], and AI systems [47–55]. Together, this body of work supplies a rich set of lessons learned.

Based on these lessons, this paper introduces the CLeAR Documentation Framework, designed to help practitioners and policymakers understand what principles should guide the process and content of AI documentation and how to create such documentation. The CLeAR Documentation Framework introduces four principles for documentation and offers definitions, recommends approaches, explains tradeoffs, highlights open questions (Table 1), and helps guide the implementation of documentation (Table 2). It builds on and is aligned with previous principles-based frameworks for documentation [44,56].

At a high level, the CLeAR Principles state that documentation should be:

  • Comparable: Able to be compared; having similar components to documentation of other datasets, models, or systems to permit or suggest comparison; enabling comparison by following a discrete, well-defined format in process, content, and presentation.
  • Legible: Able to be read and understood; clear and accessible for the intended audience.
  • Actionable: Able to be acted on; having practical value, useful for the intended audience.
  • Robust: able to be sustained over time; up to date. 

A healthy documentation ecosystem requires the participation of both practitioners and policymakers. While both audiences benefit from understanding current approaches, principles, and tradeoffs, practitioners face more practical challenges of implementation, while policymakers need to establish structures for accountability that include and also extend beyond documentation, as documentation is necessary but not sufficient for accountability when it comes to complex technological systems. We offer guidance for both audiences.

Documentation, like most responsible AI practices, is an iterative process. We cannot expect a single approach to fit all use cases, and more work needs to be done to establish approaches that are effective in different contexts. We hope this document will provide a basis for this work.

Why Now?

While experts have been alerting the public about the shortcomings of AI systems for some time [3,57,58], we have recently witnessed an unprecedented surge in public awareness and concern regarding AI. This recent attention can be attributed in part to the development of large language models by prominent tech companies, such as Google’s LaMDA [59] and Gemini [60], Google DeepMind’s Sparrow [61], Meta AI’s Galactica [62] and LLaMa 2 [63], and OpenAI’s GPT 3 [64], 3.5, and 4 [65] as well as emerging open-source initiatives such as BigScience’s BLOOM [9a], Microsoft’s Phi-2 [66] and Orca-2 [67], Mistral AI’s Mistral 7B [68], and TII’s Falcon-180B [69]. In January 2023, OpenAI’s ChatGPT (based upon GPT-3.5) became the fastest-growing application in history when it reached more than 100 million monthly active users within two months of its introduction [70]. The subsequent media coverage surrounding these developments [71–73] further intensified public interest and concern.

While often categorized as technical, AI systems and their underlying data and models are sociotechnical. In other words, they combine the technical infrastructure and design with the social context in which they are designed, developed, evaluated, and deployed. Accountability for these systems and their impacts requires transparency around their design and creation and how they are intended to be used [74]. In recent years, alongside the exponential increase in data collection and the efforts to develop increasingly powerful machine learning models, there have been notable efforts calling attention to the need for documentation to accompany datasets, models, and AI systems, and to account for the process of creating them. These include Datasheets for Datasets [28], Dataset Nutrition Labels [29–31], Data Cards [32], Data Statements for Natural Language Processing [33–35], the Aether Data Documentation Template [36], Traditional Knowledge Labels [37], Nutritional Labels for Data and Models [42–44], Model Cards [45,46], Method Cards [47], FactSheets [48–54], and System Cards [55], among others.

These documentation efforts are complementary to Impact Assessments for AI systems, sometimes referred to as Algorithmic Impact Assessments, which are im- pact and risk evaluations carried out internally by the product team prior to development. These are usually intended to assess and categorize risks of the system, or to facilitate reflection on design choices. Impact assessments can result in documentation assets like those mentioned above; we discuss this further in Section III below. Both documentation and impact assessments are inspired, in part, by standardized approaches prevalent across diverse industries such as electronics, food and drug manufacturing, transportation, and civil and environmental engineering, that motivate and enforce product quality.

The public’s recent concern has renewed regulatory focus on standards and requirements for machine learning models and systems. There are a number of proposed or passed legislative measures that suggest a move toward documentation requirements as an element of broader AI governance. These include the US White House AI Bill of Rights [18], EU AI Act [75], Canada’s Artificial Intelligence and Data Act (AIDA) [76], Singapore’s Proposed Advisory Guidelines on Use of Per- sonal Data in AI Recommendation and Decision Systems (updated for PDPA) [77], and Brazil’s draft AI legislation [78]. Notably, in May 2023, the U.S. congressional hearing “Oversight of A.I.: Rules for Artificial Intelligence” led by Senator Richard Blumenthal, brought further public attention to the need for AI documentation, including several calls for “fact sheets” or “nutrition labels” for datasets, models, or AI systems [27]. The October 2023 Hiroshima “Code of Conduct,” signed by the G7 countries, called for “transparency reports … informed by robust documentation processes” that will “contribute to increased accountability” [79]. This is echoed by the October 2023 Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence issued by the White House, which calls to “develop standards, tools, and tests to help ensure that AI systems are safe, secure, and trustworthy” [80]. The EU AI Act recommends assessments of risk, impact, and data privacy that, while distinct from the documentation outlined in the CLeAR Framework, will certainly support transparency and accountability broadly [81].

The recommendations and insights in this paper are distilled from years of experience and iterative development of documentation practices. Many of the authors of this paper have themselves been deeply involved in the major documentation projects and related research efforts from the last decade [21,23,28– 31,34,35,38,39,41–45,47,49–56,74,82–84] and in creating AI documentation in industry contexts.

Key Themes and Considerations for AI Documentation Practices

Documentation is worthwhile for various stakeholders. It improves the understanding of practitioners creating or building datasets, models, or AI systems, which opens up opportunities to reflect on implicit and explicit decisions, ultimately enhancing the reliability of the systems they create. For organizations, it enables knowledge transfer across silos and encourages responsible use. Further, it provides information to users and potentially affected communities that can be used to determine the appropriateness of an AI system or its underlying data or models, thus helping inform consumer choice, advocacy work, regulation development, and regulation enforcement. It also enables recourse in the event of harms caused by or inquiries into the AI system, and accountability regarding who might be held responsible for those harms (see examples in Table 2).

This section provides background and context for AI documentation, as well as lessons learned from our prior work implementing AI documentation in a variety of contexts.

Importance of documenting datasets, models, and AI systems

In software engineering, documentation helps to improve the usability of a codebase or software system. However, AI systems (and the machine learning models they are built on) require more than just properly functioning code to work well across different use cases. For instance, how well a model works in a given context is greatly influenced by elements like the quality and representativeness of the training data and the cultural contexts it represents, and how the model was designed, built, trained, and fine-tuned. For this reason, a system developed in one country or cultural context, or one domain or domain context, may not perform well in another. The choice of benchmarks and evaluation criteria that were used to determine whether a model or system was ready to be released are also important for contextualizing its utility with respect to a specific use case: each test provides information about how well the model or system might work in the specific use contexts that the test represents.1For example, standard methods of model performance testing using hold-out data might not be sufficient to ensure acceptable machine learning model behavior in real-world settings. Yet external validation of machine learning models and systems is of special importance given they tend to fail silently, meaning they appear to function “normally” even when applied to data or contexts with very different properties than their training data [85,86]. As such, it is essential to also document upstream building processes (including dataset curation and model training) and intended use case scenarios. This information can inform future downstream use as the AI system changes hands: for example, if a third party builds on top of an existing model or AI system.

Documentation discloses potential risks, and can provide relevant information for mitigating them. It also can provide valuable information for appeal and recourse when harms have occurred. This is especially critical for models and AI systems that have high-impact applications, such as those used in healthcare, child welfare, immigration, financial lending, and public benefit distribution, where the potential for harm—such as loss of wealth or even death—is significant. This issue is particularly acute for subpopulations historically subject to unfair or unjust treatment, as historic injustices are reflected in the data and reproduced by models and AI systems.2These include, but are not limited to, Indigenous peoples, people of color, women, LGBTQ+ communities, people with disabilities, immigrant communities, low-income and economically disadvantaged groups, and religious minorities.

Documentation should occur throughout the lifecycle

While documentation should be created in parallel with development, it is common today to produce documentation after development. The post hoc practices around AI documentation pose challenges to developing technology that effectively accounts for foreseeable use and foreseeable harms. Furthermore, relevant information might be lost between development and documentation when documentation is created after development [31]. As such, we encourage developers to integrate documentation throughout AI development and deployment:

Models and AI systems should be inspected for bias and potential harms at all stages of the AI lifecycle.
Often, the sociotechnical impacts of a model or AI system are only evaluated or critiqued after development and sometimes only after deployment. Even in the case where harms have not yet occurred, waiting until the end of the development cycle to inspect datasets and AI systems prevents timely adjustments, and will dramatically increase the cost to address issues, in terms of resources, reputation, and time. Indeed, in the “launch early and often” era of software development, it often falls on researchers, the media, or the public to identify and highlight harms of AI systems after they have already been deployed. Harms identification and mitigation only commencing after launch is too late; it is imperative that machine learning developers begin reflecting on potential harms in early stages of a project—for example, by completing an impact assessment—and continue to audit systems for risks both during the development process, as well as afterwards.

Data and datasets also have a lifecycle that should be inspected for risks (ranging from privacy concerns to stereotypes and discriminatory content to how and when the data was produced or collected) [87]. Dataset creation introduces bias at every stage, including in collection, cleaning, processing, and labeling. As such, it is crucial to be intentional about which biases are acceptable for a particular use case or in tandem with mitigation procedures, and which are not. Choices that are made—either implicitly or explicitly—during the creation of a dataset will impact its downstream use, and so it is crucial to be intentional about these choices (especially as biases that arise unintentionally are no less harmful than intended bias). We note that data documentation is especially pertinent, and challenging, for large language models, whose efficacy draws directly from the size and scale of their training datasets, most of which are poorly documented today, if documented at all.

In addition to documenting throughout the development process, we also encourage developers to document the development process itself and policymakers to scope regulation that takes into account the context and processes which led to the development of the dataset, model, or system. This can start, for example, from reflecting on a system’s potential biases and harms when conducting an impact assessment. Although the final model or AI system may be the component most directly experienced by a user (for example, interacting with a chatbot, GPS system, or online reservation system), the product lifecycle encompasses much more. For instance, it may include: identifying a motivating problem or use case; determining which model(s) to use; gathering data for training, finetuning, and evaluating models; testing and validation; deployment in production; monitoring and updating; and so on. These stages typically occur non-linearly and are naturally iterative, meaning that documentation of the development process must be regularly updated to capture the dynamic nature of development.

Expanding the focus of documentation to be context-aware

In addition to documenting the process itself, and doing so throughout the AI lifecycle, the documentation should also be context-aware. Context-aware documentation expands the focus beyond the technical specifications of a dataset, model, or system to include sociotechnical information and decisions made during the process of development, many of which are critical to the performance of the AI system but would not otherwise be visible to someone using the system. This could include information such as initial motivation for the dataset, model, or system, how decisions about data collection and cleaning were made (including which communities were involved and why), and what particular metrics motivated de- sign choices in models and systems. Gathering information on the implications of such sociotechnical decisions usually requires the participation of subject matter experts. It can further require the consultation of affected communities or other actors who are traditionally not considered part of the technical development pipeline, and thus whose perspectives are not typically included in documentation processes that don’t call for such information. For example, Local Contexts is an initiative that enables the co-production of a dataset’s relevant cultural metadata with Indigenous communities [88,89]. Including the knowledge of the populations represented in a dataset will preserve knowledge and contextual significance and honor lived experience, while driving a valuable exchange of information that can help mitigate downstream harms.

Risk and impact assessments in the context of documentation

In the context of AI governance, risk assessments, impact assessments, and documentation more broadly are closely related concepts that can be overlapping and complementary. While risk assessments concern the identification, analysis, and evaluation of threats and vulnerabilities, impact assessments go further by considering and, in some cases, measuring, implications for individuals, communities, and their environments. Risk-based AI governance approaches, such as those in effect or soon to be in effect in Canada [90] and the EU [81], mandate assessments as a way to identify the risk class of a system, which then determines the rules and—in many cases—documentation requirements applicable to the system in question.

Impact assessments are also increasingly common in the private sector [91] and civil society [92] as sociotechnical mechanisms to drive better design decisions. Typically, impact assessments are conducted prior to development by and for internal development teams in order to assess potential impacts of the system. This is in contrast to other documentation efforts that are focused on other parts of the AI system lifecycle, for a diversity of audiences, and covering a wide variety of system components (such as datasets and models). That said, these efforts are complementary, as risk and impact assessments include many overlapping themes to the documentation efforts we have discussed in this report (and cited in Figure 1 in the appendix), and will be easier to conduct and likely more accurate on systems that have Comparable, Legible, Actionable, and Robust documentation from which to draw information. Other forms of documentation can be informed by and should be updated following findings from impact assessments.

The opportunity to drive behavior through documentation requirements

The intention and value of documentation can shift dramatically depending on what is shared, with which audiences, and when. For example, internal documentation can provide standardized structures and create cultural norms that facilitate knowledge transfer, scientific rigor, and information sharing through a common vocabulary and approach. Furthermore, sharing the results of evaluations, inspections, and audits with third parties or oversight organizations can drive knowledge sharing across industry, promote understanding and increased public accountability, as well as support justice, equity, diversity, and inclusion (JEDI) initiatives. Documentation is a lever that enables accountability—especially when accountability systems (which can range from societal expectations, to voluntary standards, to regulation and legislation) require documentation review by either internal or external evaluators or auditors. In cases where the documentation is missing, inaccurate, or misleading—or in which the documentation reveals issues with the dataset, model, or AI system itself—mitigation procedures can minimize harm while the issues are addressed. Depending on the use case, such mitigation procedures can include limiting an AI system’s automated decision making authority in favor of human review and oversight, or taking it offline. Additionally, documentation can provide standardized approaches and templates for running inspections, which can include compiling results, performing audits and evaluations, and communicating these results to relevant stakeholders.

Documentation requires robust organizational support

Documentation requirements are unlikely to be successful without robust organizational support, including additional resources for technical and workflow processes. In practice, the success of AI documentation largely depends on integrating documentation into existing tools and workflows to the extent possible [41] and on modifying current organizational and process structures as needed. Useful documentation takes time and requires appropriate skills. Thus, it is crucial that organizations prioritize and incentivize this task to develop an organic documentation culture. Having buy-in at the executive level and aligning the goals of documentation with the organization’s goals is a first critical step to building momentum around real change in an organization’s approach to documentation. In parallel, there is a powerful and important opportunity now to leverage regulation and policy as a mechanism to drive useful documentation practices.

The CLeAR Documentation Framework

We now introduce the CLeAR Framework. This framework aims to provide practitioners and policymakers with concrete principles to guide the implementation of Comparable, Legible, Actionable, and Robust documentation of datasets, models, and AI systems. It also offers guidance for navigating the tradeoffs that can arise in practice when implementing these principles. By tradeoffs, we mean that at times the CLeAR principles are in tension with one another. For example, making documentation comparable limits the amount of customization, which means that for the sake of comparability, it can be beneficial to exclude certain details. Likewise, sometimes legibility might be at the expense of being very technical, being actionable might mean being less comprehensive, and robustness may be at odds with cost effectiveness. Since principles are often compelling in theory but challenging to realize in practice, our hope is that by discussing such tradeoffs, CLeAR can serve as a realistic guide for creating documentation while considering and understanding some of the choices that will need to be made.

The CLeAR Documentation Framework is intended to provide a foundation for choosing or designing an approach to documentation that is most suitable for a particular dataset, model, or AI system. It builds on and is aligned with other principles-based frameworks for documentation [44,56].3Notably, the CLeAR Principles are aligned with the properties laid out by Stoyanovich and Howe: The principle Legible loosely maps to the property Comprehensible, Actionable to the properties Consultative and Concrete, and the principle Robust to the properties Computable, Composable, and Concomitant. The suitability of a particular form of documentation depends on a number of factors, including the context of the dataset, model, or system being documented, and the potential uses, goals, and audiences for the documentation. There is no one-size-fits-all approach when it comes to documentation; rather, our goal is to provide useful guidance and a shared vocabulary to support decision making.

The CLeAR Documentation Principles

Dataset, model, and AI system documentation should be Comparable, Legible, Actionable, and Robust. These four principles are introduced below and further discussed throughout the remainder of the paper.

Comparable: Documentation can be compared. It has similar components to the documentation of other datasets, models, or systems to permit or suggest comparison. It enables comparison by following a discrete, well-defined format in process, content, and presentation.

Comparability facilitates understanding by familiarizing various stakeholders with a consistent process and format for documentation. For instance, it enables potential users of a dataset, model, or AI system to evaluate it and contrast it with others, making it easier to judge relative performance, suitability, or impact.

While comparability is important, it requires a certain level of consistency or standardization. We propose that there is a base set of information relevant to all datasets, models, and AI systems that should be documented. For example, this includes intended use. For datasets specifically, this includes source as well as dataset license. Wherever possible, the presentation of these details should be standardized in format and content to provide for basic comparisons.

It is important to stress that given the variety of datasets, models, and AI systems, it is likely not feasible for documentation to be standardized across all contexts and domains. Rather, the content, specific use cases, and the audience for the documentation itself should shape the documentation type and format, thus driving comparability that is adjusted to the need. For example, image processing algorithms applied in healthcare to detect potential cancerous cells in radiology images should adhere to a different set of required content for comparability than AI systems used to detect email spam. Also, the metrics used to evaluate these two distinct AI systems would be different. The first would likely have a measure like sensitivity as a primary metric since the costs of missing cancerous cells (so-called false negatives) are very high; in the second example, a primary metric like accuracy may be best suited, as marking legitimate emails as spam (false positives) can be as bad as failing to filter out spam emails (false negatives). Another example could be that documentation for a speech dataset may need to include information about the dialects spoken in each clip, whereas different demographic information would be appropriate for a vision dataset. There may also be differences in what is documented within different organizations based on their existing policies and systems.

Legible: Documentation can be read and understood by its intended audience. It is clear and accessible to this audience.

Legibility enables comprehension, which in turn supports the process of decision-making about the associated dataset, model, or AI system.

A key motivation for documentation is to provide transparency into the underlying asset (i.e., dataset, model, or AI system) with the goal of better understanding it, both in terms of its technical architecture, as well as the sociotechnical elements of how it was created and intended to be used. To be effective, the documentation must be legible, providing clear, comprehensive, and accurate information that enables those affected by it, those who will use it, and those who might leverage it to examine and assess it. It is important to keep in mind that what counts as legible will likely be different for different audiences [41]. Also, as with all approaches to transparency, documentation should be evaluated to ensure it helps its intended audience meet their goals [74,83,93]. While previous work has often focused on approaches intended for general audiences [82], we argue that different audiences might benefit most from having different versions of documentation that is legible for them.

In the spirit of clarity and comprehensibility, legible documentation should openly state relevant knowledge gaps, uncertainties, ongoing developments, and areas needing improvement that the developers consider important to inform on intended use. It also should articulate the expectation of updates (such as developments and improvements not foreseeable at the time of documentation) to capture the dynamic nature of the development process.

Actionable: The documentation can be acted on. It has practical value and is useful to its intended audience.

Actionable documentation contains the appropriate level of granularity and detail to enable informed decision making for its intended audience.

To be actionable, documentation must contain practical information that can be effectively used by stakeholders, for example for users to audit a dataset or evaluate model outputs [47]. Different stakeholders may have different uses for the information provided. For instance, designers may use the information presented in a model card to eliminate risky design ideas, create conditional designs to mitigate harms, provide transparency to end users, and advocate for the user perspective within their team [84]. Different stakeholders may also benefit from different kinds of information, and thus documentation may require multiple levels of technical depth (from highly technical to more qualitative or legal in nature) to be actionable for a broad set of users. For example, a data scientist may require technical information like performance and evaluation metrics, while a compliance officer may want to see details on the license and provenance. On the other hand, an affected consumer or user may be primarily interested in the system’s biases or the fairness of its predictions.

In addition to considering audience(s) when determining an approach, practitioners should consider the information design and hierarchy within the documentation. For example, actionable documentation might contain “at a glance” information for some users, with deeper dive information for others. The visual design of the documentation, including designing it in an organized format and making it accessible for quick and easy reference, will also be crucial to make it actionable and should be considered as part of any approach.

Robust: The documentation can be sustained over time. It is kept up to date.

Robust documentation has durability and longevity that will help maintain the trust of stakeholders.

Without organizational or systemic support, documentation often falls by the wayside. Robust documentation remains effective over time, which may require regular updates. Its durability is enabled by sufficient support, integration, and flexibility of documentation practices. Documentation should be thought of as an integral part of the development process for datasets, models, and AI systems and must be prioritized and embedded across existing processes and expectations. Documentation should allow for additions as well as modifications as needs evolve over time. Stewards and managers of documentation should build in a process for updating over time even as they formalize the operational and organizational processes around the creation and publishing of documentation.

Using the CLeAR Framework

We have created the CLeAR Framework to help guide conversations through a shared vocabulary and understanding of why and how to leverage AI documentation. However, we know that these conversations are neither new nor simple. To help describe the complexities and real challenges of implementation, we have gathered below a number of tradeoffs that occur for each principle. In the subsequent section, we include some open research questions (Table 1) that are critical to address through both research and trial-and-error implementation in order to support progress over time. Examples to help guide the implementation of the CLeAR Framework can be found in the appendix (Table 2).

Comparable vs. customized: Even though there is not a one-size-fits-all approach, and we anticipate different standards across different datasets, models, or systems to be documented, documentation should follow a certain schema to enable comparison where possible. This will necessarily limit customization. It is conceivable that for the sake of comparability, certain information would be excluded. Similarly, creating comparable documentation may require alignment, potentially sacrificing customization, on certain aspects of the documentation process, such as when documentation begins, the kinds of expertise included, and general approaches for maintaining accuracy. Comparable processes can help produce comparable documentation and will mitigate inconsistencies from following different approaches, but may come at the expense of flexibility or customization.

Comparable vs. aligned with existing tools and processes: In practice, for efforts aimed at approaching AI responsibly—including AI documentation—to succeed, it is beneficial to align them with existing tools and processes within an organization to the extent possible [41,94]. Since tools and processes vary between organizations—and even, in some cases, between different teams within the same organization—this may lead to discrepancies between the content and format of documentation produced.

Comparable vs. actionable and legible for diverse audiences: Different consumers and stakeholders of documentation require different sets of information, both in terms of content and format. It is challenging to optimize the value of legible and actionable documentation to best serve stakeholders (including both creators and users of documentation) with differing values, needs, and goals. In such cases, bespoke or customized documentation may be beneficial, but may result in non-standardized information being shared, which limits comparison with other models or datasets.

Legible vs. (overly or insufficiently) detailed: Creating legible documentation for a specific audience requires including the right level of detail. Including extensive technical details may result in documentation being less comprehensible to audiences without the required background and expertise. Likewise, audiences with technical expertise might find the lack of detail in documentation written for less- or non-technical audiences less helpful. As another example, it may be necessary to include more background information and foundational definitions when preparing legible documentation for regulators as opposed to one’s own team or organization. This challenge can sometimes be mitigated by producing different versions of documentation (e.g., ranging from technical to less- or non-technical), and offering some guidance for which versions of the documentation are intended for which audiences.

Actionable and legible vs. comprehensive: Documentation should encompass the necessary and relevant details for the intended stakeholders and use cases of the associated dataset, model, or AI system. However, documentation is most useful when appropriately concise. Thus, there is tension between being comprehensive and being concise: to be too brief can lose important content, but if documentation is exceedingly dense or lengthy, it will be less actionable.

Actionable vs. protecting privacy, security, and intellectual property: The full disclosure of information regarding the inner workings of a dataset, model, or AI system can come at the expense of exposing sensitive information, including private user data, protected intellectual property, or trade secrets that provide the basis for an organization’s operational sustainability. To enable a balance between these tradeoffs, documentation standards should allow the owners and developers of a documentation asset to redact both personally identifiable information to protect privacy, as well as essential information or design elements that would limit the competitiveness of an organization or fall within protected intellectual property or trade secrets, and limit the release of information that can lead to security breaches, misuse, or fraud. Regulations will ultimately determine what information is required and what can be redacted.

Robust vs. resource intensive: The time and resources required for robust documentation, including keeping it current and updated, must be balanced with demands for other project needs and developments, some of which may be considered higher priority than documentation. Insufficient resource allocation for documentation may result in inadequate and unmaintained documentation. Furthermore, there may be high upfront costs for establishing robust documentation systems as well as a need to build capacities and resources for updating the documentation after the dataset, model, or AI system is initially deployed. Practitioners are encouraged to establish standardized methods to prioritize across documentation needs, including legacy, current, and future datasets, models, and systems, while keeping in mind that documentation can also aid in the successful development of existing or new datasets, models, or systems.

Open Questions and Directions for Exploration

As we have emphasized throughout, AI documentation is a relatively new process and what works best in practice is still being explored. In Table 1, we provide a list of open questions and directions for exploration, broken down by the CLeAR Principles. While considering these questions may provide initial guidance for practitioners in choosing the right approach to documentation to fit their needs and context, we acknowledge that answering these questions may also involve additional research as well as experimentation and iteration. This list is not exhaustive; it is intended as a starting point for further exploration.

Table 1: Open Research Questions


Implementation guidance and tips for practitioners

Below we list concrete suggestions that will assist practitioners in putting documentation into practice.

Look at past work on AI documentation

Researchers and practitioners have proposed methods to document datasets, models, and AI systems including Datasheets for Datasets [28], Dataset Nutrition Labels [29–31], Data Cards [32], Data Statements for Natural Language Processing [33–35], the Aether Data Documentation Template [36], Traditional Knowledge Labels [37], Nutritional Labels for Data and Models [42–44], Model Cards [45,46], Method Cards [47], FactSheets [49–54], and System Cards [55], and others. As these documentation approaches capture various use cases and were designed with different applications in mind, understanding their methodologies and motivations will be of help even for use cases or scenarios that have not yet been captured. These existing documentation approaches also illustrate best practices, such as requiring consistent language and protocols for writing and updating the documentation, and including an author list for transparency into which perspectives or domain expertise has been included in the creation of the documentation itself. Of course, as there is no one-size-fits-all approach to documentation, existing approaches may need to be adapted to fit the needs of a particular project or organization. More information about current documentation approaches can be found in Figure 1 in the appendix, which is a summary of AI documentation tools taken from Hugging Face’s Model Card Guidebook [82].

Be realistic

Given limited resources and capacities within organizations, the approach to documentation implementation can be very different depending on the maturity and size of both the team and the system, the potential risks and harms of the system, and the scale and number of models or AI systems to document. For example, AI systems currently under development present an opportunity to start documentation in parallel with system development. By contrast, large legacy AI systems, which often include a multitude of individual models, can be exceptionally challenging to document retroactively. Additionally, any AI documentation will likely need to be revised often as new information becomes available or as the dataset, model, or AI system is used for additional tasks [52]. Further, it is important to be realistic about competing priorities and the complex nature of change management within the development process itself.

Start early and revise often

Much of today’s AI documentation is created after development, which is less operationally efficient than parallel documentation creation. It also misses the opportunity to address issues as they arise during development and often makes it more expensive to address them later on. Documentation after development also has a tendency to reduce the accuracy of the information [31]. Thus for any dataset, model, or AI system in development, documentation should be created in parallel with development, beginning with the design of the dataset, model, or system, and throughout its lifecycle.

Starting early enables the opportunity to learn from shortcomings and revise the dataset, model or system development. Creating and adapting documentation is a learning process. To ensure accuracy, documentation should be created alongside technical milestones and continuously updated as needed, rather than delaying revisions until after development is complete [49].

Consider audience

When implementing documentation, a key element to keep in mind is the audience who will interact most closely with the documentation [27, [52]. Documentation that is targeted and designed for a very technical audience of software engineers, data scientists, and machine learning engineers will have a significantly different level of technical depth compared to documentation that aims at enhancing the understanding of end users. Separate forms of documentation can be produced for different audiences, or documentation can be designed in a “layered” way, including both the technical details as well as summaries and additional contextual information aimed at non-technical or business users.

Document regardless of size or scale

Regardless of the size or the scope of a dataset, model, or AI system, documentation is important. Models and systems of all sizes (including comparably “small” or “simple” ones) can reproduce bias, have unintended consequences, and cause outsized harm; this includes AI models and systems as well as non-AI (rule-based) models and systems. The same is true for datasets, where even comparably “small” or “simple” datasets can be highly biased and cause harm. On the other hand, the difficulty of documenting very large datasets does not reduce the importance of doing so [3].


As AI systems continue to permeate our lives, the importance of documenting how these systems are designed, trained, and deployed only grows. The demonstrated consequences and risks associated with AI deployment highlight the need for a deeper understanding of, and subsequent regulatory approach toward, data and data-driven systems. We face a great responsibility to create guardrails and implement changes that will guide the development of AI while mitigating potential harms and acknowledging the sociotechnical nature of these systems: The contexts in which they are used can result in wildly different outcomes, and the potential harms might disproportionately affect those who are already disenfranchised and marginalized in society.

Dataset, model, and AI system documentation are straightforward mechanisms for transparency into AI systems. The CLeAR Framework enables a foundation for designing and implementing documentation, while considering tradeoffs and encouraging holistic thinking about documentation needs. Building on the collective experience and perspective of a team that has worked at the forefront of AI documentation across both industry and the research community, we developed implementation guidance for practitioners, as well as context that may be helpful for policymakers. Given the complexity of AI governance, from data collection to model deployment, our goal is to establish a framework that serves as a guide for the consideration of documentation needs and priorities across datasets, models, and AI systems. Our hope is that this framework will help practitioners to create and use documentation, and support policymakers to better understand the importance of documentation and tradeoffs that should be considered for area-specific documentation regulation. Only through collective efforts can we ensure that AI is created and deployed responsibly. ×




Table 2: Examples of how existing documentation approaches address selected tradeoffs


Figure 1. Summary of AI Documentation Tools adapted from the Hugging Face’s Model Card Guidebook [82]

Figure 1. (continued) Summary of AI Documentation Tools adapted from the Hugging Face’s Model Card Guidebook [82]


The authors would like to thank the following colleagues for their thoughtful feedback on and contributions to this report: Emily Bender (University of Washington), Ashley Casovan (IAPP, Carleton University), Mahima Pushkarna (Google Research), Kush R. Varshney (IBM Research). We would additionally like to thank Lindsay Underwood, Alicia Pritt, and Laura Manley at the Shorenstein Center for their support publishing this paper.


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