How Metadata Enhances AI Model Accuracy

AI requires massive amounts of data to train its models. The AI-powered outcomes are untrustworthy if trained on low-quality, mismatched, or otherwise “bad” data. Therefore, understanding data before using it as an input for AI applications is crucial, and metadata is the key.

In this post, we’ll define trust with respect to AI and explain how metadata helps improve data quality, how it can be used to understand AI applications, and why a data catalog is a crucial repository for metadata in the AI development process.

Key takeaways

• Metadata is essential for increasing the trustworthiness of AI by providing traceability, context, and accountability for the data used in training AI models.

• Trust in AI is built on understanding data origins and lineage, connecting with those who know the data best, and ensuring appropriate data is used for AI use cases.

• High-quality data is crucial for delivering high-quality AI outcomes, and metadata enables the data governance, stewardship, and analysis that serves as the foundation of data quality efforts.

There’s little chance of overstating the expectations placed on AI, especially with such lofty predictions being floated. The International Monetary Fund expects AI to impact 40% of all jobs globally and 60% of jobs in advanced economies. Goldman Sachs expects AI to raise global GDP by 7%, close to $7 trillion. Not to be outdone, McKinsey expects AI to generate over $25 trillion in economic value.

While AI innovators move forward at a blistering pace, regulators are setting up guardrails. Although the US’s approach to AI regulation is fluid, the European Union’s AI Act came into force in early 2025 to block AI from what it deems to be risky or unacceptable applications, such as behavioral credit scoring, user profiling based on disabilities, and crime predictions. The European Commission further convened an “expert group” to create ethical guidelines for trustworthy AI.

How can these innovators create with AI ethically and efficiently? Data is at the core of trustworthy and responsible AI. Errors, omissions, and biases in the data fed to AI models and algorithms will generate untrustworthy results from irresponsible AI development.

Responsible AI development should address most of these concerns. However, developers may not have complete control over or visibility into the data feeding their AI. On the other hand, AI users lack visibility into the methods by which the AI tools they use have been built (assuming incorrectly, for example, that a model has been adequately trained and tested for their given purpose).

With metadata, AI developers and users can increase trustworthiness by better understanding the data fueling AI: where it originated, how it has been processed or transformed, and who might be an expert to answer questions about it. Metadata delivers the critical context AI developers need. In this way, better traceability leads to increased trust.

Defining trust in AI

The Trustworthy Software Foundation provides a framework for developing and using software in a trustworthy manner. It further defines trust as having the following components:

• Safety: The ability of the software to operate without causing harm to anything or anyone.

• Reliability: The ability of the software to operate correctly.

• Availability: The ability of the software to operate when required.

• Resilience: The ability of the software to recover from errors quickly and completely.

• Security: The ability of the software to remain protected against the hazards posed by malware, hackers, or accidental misuse.

Building on those components, the National Institute of Standards and Technology (NIST) pulled together stakeholders to develop the “essential building blocks of AI trustworthiness,” which include:  

• Validity and reliability

• Safety

• Security and resiliency

• Accountability and transparency

• Explainability and interpretability 

• Privacy

• Fairness with mitigation of harmful bias

Organizations have devoted much effort to creating standards of trustworthiness and responsibility regarding software development. Terms like safety, reliability, security, accountability, privacy, fairness, and explainability lead directly back to AI’s use of data for training, interpretation, and generating outcomes. 

Using metadata to support the development AI applications

Data quality is the degree to which data meets expectations for accuracy, validity, completeness, and consistency. It goes beyond right or wrong; users can evaluate data quality through attributes like completeness, timeliness, duplication, consistency, and others.

In Gartner’s “Quick Answer: What Makes Data AI-Ready?” (paywalled), the firm defines AI-ready data as being “determined by the ability to prove the fitness of data for AI use cases.” The use case and details of the AI application must be known and understood, and the target data must accurately represent the intended use case, including errors, outliers, trends, and patterns. Therefore, determining AI readiness is a case-by-case process that relies on metadata to find, govern, evaluate, justify, and share the best data for the AI use case.

Organizations using AI can improve data quality and AI readiness with best practices like establishing and enforcing data governance guidelines, assigning and enabling collaboration with data stewards, and using a data catalog to capture metadata, enable data governance, and build a data culture.

Metadata provides the basis for data quality tracking and monitoring by surfacing data attributes such as timeliness, source, lineage, and popularity. Beyond quality, data for AI use cases must be representative of the use case, as mentioned in Gartner’s definition above. In other words, the traditional view of data quality—accuracy, timeliness, duplication, etc.—isn’t appropriate here. AI-ready data is data that is representative of the use case, errors and anomalies included. 

For example, consider an AI model trained to detect fraudulent transactions in a financial institution. If the training dataset includes only perfectly clean and error-free data, the model may fail to recognize real-world fraud patterns, which often involve subtle anomalies, incomplete records, or unusual spending behaviors. Metadata plays a crucial role in ensuring the dataset accurately reflects these conditions by capturing historical fraud cases, labeling outliers, and tracking data lineage. With this context, data scientists can refine the model to distinguish between normal variations and actual fraud attempts, improving accuracy and reliability in real-world applications.

Metadata can also capture information like trust flags (which users can select to endorse or deprecate data based on its AI readiness), statistics that provide a quick snapshot of data elements, and alerts to keep data owners, stewards, and users aware of changes. These and other metadata attributes are typically captured and analyzed via a data catalog.

For AI specifically, metadata supports informed AI development and improved AI outcomes by driving:

• Informed AI model development through speedier access to trusted, AI-ready data appropriate for the AI use case.

• Enhanced AI outcomes by ensuring developers can find, understand, and evaluate data in pursuit of the AI project’s business goals and ROI expectations.

• Transparent AI traceability by cataloging AI assets and the datasets used for model training.

Using metadata to enhance AI trustworthiness

While metadata brings incredible value to AI developers through its use in validating and vetting the data used by AI for model training, metadata provides equivalent value in tracking AI itself. Especially as AI pushes into more use cases and technologies, AI proliferation virtually ensures usage by untrained and non-technical users. Having metadata on AI enables solutions such as a data catalog to track, monitor, and offer insights into the AI.

Understanding who built an AI model, the data used to train it, its use case, and how it has evolved will influence how it is used. For example, knowing that an AI model was trained on financial data might sway a marketing user to seek a different model. 

With access to model metadata—including expert references, past conversations, and links to training datasets—data consumers can confidently determine which models to trust for their needs.

Increasing AI model accuracy through metadata

If the world is going to realize the full potential of AI, we need to trust it. As AI continues to seep into every area of our lives, reliable, responsible, and trustworthy AI is crucial. However, trust is earned through information, and that information is metadata. 

Metadata provides the context, traceability, and accountability for the data used to train AI models and for the AI itself. With more information and insights on AI, organizations can improve outcome accuracy, guide ethical development, and encourage safe usage.

Ultimately, metadata increases trust, and trust increases accuracy for AI outcomes. When combined with effective data governance, organizations can develop AI solutions that are powerful and trustworthy, ensuring they get at least a slice of that $25 trillion pie.

Curious to see how a data catalog can help you deliver the metadata AI builders need? Book a demo with us today.