As artificial intelligence (AI) becomes more widespread and more sophisticated, it’s essential for organizations to implement robust governance frameworks. AI governance includes all policies, regulations, and ethical considerations when implementing and deploying AI into data practices. Gartner notes how important an AI governance framework is by highlighting a few of AI’s characteristics:

• AI is difficult to govern due to safety and business value demands.
• Scaling AI without governance is ineffective and dangerous.
• AI governance is necessary to meet standards for transparency, accountability, and ethical conduct. 

Data lineage is a crucial element for effective AI governance since it explains AI outputs and creates a mechanism to measure adherence to things like ethical standards. In this article, we’ll dive deeper into the relationship between data lineage and AI governance and why these things are important in AI-driven organizations. 

Data Lineage and its Role in AI Governance

In the context of AI, data lineage refers to the tracking and visualization of data as it moves through systems and processes from its source to its destination. It allows organizations to trace the origin, transformation, and usage of data, ensuring that it’s accurate, reliable, and appropriately managed. This concept is crucial for AI governance, as it enables transparency and accountability, allowing organizations to monitor how data influences AI outputs and ensuring these outputs are justifiable and ethical.

Incorporating data lineage into AI governance provides several benefits. It enables organizations to explain AI decisions, track the flow of data from its source to its use in AI models, and verify that the data feeding AI systems is correct and trustworthy. For example, in high-stakes environments like healthcare or military applications – where AI may suggest or even execute actions autonomously – data lineage can ensure these decisions are based on sound, well-understood data, reducing the risks associated with flawed or biased inputs.

The Importance of Data Lineage and Governance

In modern information systems, data integration is a standard practice. However, as data is integrated, it often becomes difficult to trace its source(s), a challenge for organizations that rely on AI systems to make critical decisions. For instance, knowledge graphs, which aggregate and connect data from diverse sources, build “semantic layers” over data for easier analysis. But if the underlying data is flawed, the conclusions and decisions drawn from it can follow suit. 

This becomes especially critical in AI systems, where decisions are often made automatically, sometimes with limited human intervention or oversight. Whether in military operations, healthcare, or business, the potential of flawed data to facilitate troublesome AI outputs is a significant risk. In these environments, having a clear, auditable data lineage helps organizations verify AI outputs and ensure data is trustworthy.

Digital Transformation and Operationalizing AI

As organizations adopt AI to drive automation and speed decision-making, it’s crucial to operationalize AI in ways that ensure outputs are understandable and verifiable. AI systems may process large amounts of data from diverse sources and instantly provide recommendations. However, these recommendations need to be explainable and based on trustworthy data.

Data lineage plays a vital role in operationalizing AI, ensuring decision-makers can verify the integrity of AI outputs. This traceability mitigates the risk of acting on AI-generated suggestions without fully understanding the data they’re based on. By tracing the journey of the data through various stages, organizations can verify whether the AI system's decisions are sound and justifiable.

The Importance of Verification and Validation

Verification and validation (V&V) processes are crucial for ensuring AI systems produce reliable, accurate outputs. Just as independent verification and validation (IV&V) is used in traditional product testing to verify systems operate as intended, AI systems require V&V to ensure optimal outcomes. Incorporating a third-party entity for IV&V can reduce biases and increase the reliability of AI decisions.

Explainable outputs are vital for ensuring the effectiveness and reliability of AI-powered operations, making it an integral part of the decision-making process. Although delegating actions can improve efficiency, actions must be explainable. Data lineage supports V&V by helping organizations trace the origins and transformations of data used in AI models. This transparency enhances accountability and trust, especially in industries where AI has a significant impact on safety or legal outcomes.

Documentation as a Useful Metaphor 

Traditional IV&V processes are closely tied to documentation, which records actions, milestones, and decisions. This concept provides a useful metaphor for the V&V processes required for AI systems, where robust documentation of actions and decisions can support auditing, forensics, and decision-making. While log files and documentation can be valuable, they’re often insufficient for handling modern AI systems’ complexity.

To meet the need for scalable AI V&V, machine-understandable data and metadata models are required. This is where knowledge graph technology becomes essential.

The Role of Knowledge Graphs in Data Lineage

Knowledge graph technology, such as Altair® Graph Studio™, plays a transformative role in managing data lineage. By creating a semantic layer that integrates data from diverse sources, knowledge graphs enable organizations to track provenance—the detailed history of data transformations and usage.

Key capabilities of knowledge graphs for AI governance include:

• Provenance Tracking: Monitors and records all changes at the field level, from the original data source through mappings, transformations, and computations. It also tracks where data is used across various platforms, including user interfaces, APIs, and service endpoints.
• Semantic Integration: Knowledge graphs integrate data from multiple disparate sources using technologies like W3C Web Ontology Language (OWL) and Resource Description Framework (RDF). These technologies enable the creation of a semantic layer that allows both humans and AI systems to interact with and understand data in a machine-understandable way.
• Supporting V&V: The provenance data captured in knowledge graphs supports V&V processes by providing a machine-readable history of data transformations and usage. This information helps verify that AI-generated outputs are based on accurate and reliable data, ensuring AI systems’ transparency, accountability, and integrity. This enables automated checks by providing clear explanations of data processing pathways.

Knowledge graphs also help build a web of intelligent data, where AI systems can use interconnected datasets to operate more autonomously while still adhering to governance principles.

Humans: From "In the Loop" to "On the Loop"

As automation increases, humans are transitioning from being actively involved in every decision ("in the loop") to overseeing and verifying AI outputs ("on the loop"). This shift underscores the importance of robust V&V processes, supported by clear data lineage, to maintain trust and ensure that AI-driven decisions align with organizational goals and ethical standards.

Data Lineage and AI Governance for Accountability

As AI continues to drive decision-making, it’s increasingly important to ensure AI systems operate with transparency and accountability. Data lineage is foundational in achieving this by allowing organizations to track and understand the flow of data throughout the AI decision-making process. More broadly, effective data governance is essential to managing the complexities of AI systems and ensuring that they operate responsibly, ethically, and transparently. As AI continues to shape industries and decision-making processes, the need for effective data governance has never been greater. 

To learn more about how to effectively use data lineage and governance in your AI solutions, reach out to Altair experts at https://altair.com/contact-us.