Knowledge Graph Framework for Fairness

• Knowledge graph-based frameworks for fairness are structured methods that formalize legal, ethical, and technical fairness requirements using semantic relationships.
• The framework leverages standards like SKOS and OWL to map data flows, decision processes, and mitigation strategies for bias reduction in AI systems.
• It enables continuous, automated fairness verification throughout the system lifecycle by integrating with CI/CD workflows and dynamic testing tools.

A knowledge graph-based framework for fairness seeks to leverage the expressive, relational structure of knowledge graphs (KGs) to encode, operationalize, and verify fairness requirements across the lifecycle of algorithmic and AI systems. The central aim is to make fairness requirements—typically implicit, context-sensitive, and distributed across legal, ethical, and technical sources—explicit, structured, and machine-interpretable via a formalized knowledge graph model. This approach draws on experiences in adjacent domains (such as security requirements engineering) where knowledge graphs have enabled systematic specification and verification, and applies them to the elusive and evolving domain of algorithmic fairness (Ramadan et al., 22 Sep 2025).

1. Knowledge Graphs for Formalizing Fairness Requirements

At the core of the framework is the construction of a knowledge graph from a machine-readable glossary, leveraging formats such as SKOS for controlled vocabularies and semantic relationships. The KG instantiates nodes and edges representing:

• Data: including both structured and unstructured sources, explicit attributes, and proxies.
• Protected Characteristics: e.g., gender, ethnicity, and other legally or ethically protected features.
• Decision-Making Processes: AI and algorithmic models, from neural networks to classical logic programs.
• Data Operations: partitioned into pre-processing, in-processing, and post-processing interventions.
• Threats: explicit discrimination and more subtle threats, such as the use of non-obvious proxy variables.
• Fairness Controls: mechanisms, constraints, or technical mitigations used to address bias.

The resulting graph is designed to support both top-down (legal/ethical guideline–driven) and bottom-up (data or system analysis–driven) reasoning about fairness dependencies and risks. The relationships between classes and properties provide a structured basis for identifying which fairness requirements are applicable to which data flows and model operations.

2. Challenges and Research Objectives in Specification and Verification

The framework responds to three central challenges:

1. Specifying Unambiguous and Verifiable Requirements: Fairness definitions are context-sensitive and multifaceted (demographic parity, equal opportunity, individual fairness, etc.). The knowledge graph supports the mapping of vague or general requirements—such as “fairness regardless of financial background”—onto precise technical representations (for instance, identifying whether income operates as an illegal proxy for age or ethnicity) (Ramadan et al., 22 Sep 2025).
2. Enforcing and Verifying Fairness Across the System Lifecycle: Beyond removing explicit sensitive features, it is essential to identify and manage indirect influences (via proxies). The knowledge graph underpins automated verification to ensure that fairness constraints are embedded in model design, data pipelines, and evolving system architectures.
3. Compliance with Legal and Regulatory Constraints: In many jurisdictions, direct processing of sensitive attributes is forbidden unless for the purpose of fairness auditing. The framework encodes legal constraints into the ontology, thereby supporting engineers in balancing fairness auditing needs with privacy and compliance.

These challenges are formalized into research questions:

• How can requirements engineers be assisted in specifying unambiguous, verifiable fairness requirements?
• How can these requirements be systematically enforced and verified throughout system modeling and implementation?

3. Roadmap: Phased Development of the Fairness KG Framework

The system is structured along a three-phase roadmap:

Phase 1—Fairness Knowledge Representation:

A systematic review yields a SKOS-based glossary of fairness concepts, which is then instantiated as an OWL or similar KG. Nodes represent protected attributes, data types, potential proxies, fairness metrics, threats, and mitigation strategies. Edges encode precise relationships such as “usedAsProxyFor” or “mitigatedBy.”

Phase 2—Requirements Specification:

Customizable requirements templates (e.g., extending MASTeR templates) interface with the KG. For example, a requirement could be:

“The loan approval system shall ensure that for any two applicants differing only in <protected characteristic>, the false positive rate remains within ±𝛿.”

Variables (<...>) are linked to the knowledge graph, ensuring that every identified fairness dimension (e.g., what counts as a protected characteristic, or what proxies are forbidden) is captured. This semi-automated process presents requirements engineers with valid options and relationships derived from the ontology.

Phase 3—Integration and Verification:

Requirements specified via the KG are integrated into system models (e.g., class diagrams, state machines, data flow diagrams) and source code. Automated rule-based checking, both at design time (e.g., flagging the direct or proxy use of sensitive features in state machines or data transformations) and in post-deployment (using static analysis and dynamic testing), ensures continuous compliance. Tools such as GraphWalker or ModelJUnit may generate abstract test cases for verifying conformance to fairness requirements.

4. Illustrative Use Cases and System Integration

Two illustrative examples drawn from the framework highlight its application:

• Loan Processing System:

A vague requirement (“applicants must be treated fairly regardless of financial background”) is deconstructed via the knowledge graph—flagging income as a potential illegal proxy for age or protected status, and requiring explicit analysis and mitigation. The KG assists in re-specifying the requirement with concrete variables and control mechanisms.

• State Machine Verification:

In a UML state machine for lending decisions, the KG-driven checker identifies state transitions that may introduce fairness risks (e.g., those relying on income thresholds without proper documentation or mitigation of possible proxy bias). The knowledge graph allots traceable rationale behind each risk, supporting further requirement refinement or technical mitigation.

Such use cases demonstrate the KG’s ability to surface implicit risks, support requirement refinement, and embed compliance checks within mainstream design and testing practices.

5. Automated Fairness Verification and Continuous Compliance

By embedding fairness requirements in a knowledge graph and integrating it into CI/CD workflows:

• Automated transformations generate design-level documentation, code annotations, and test cases from the KG.
• Static analysis tools, informed by the fairness ontology, flag code and model fragments that contravene requirements.
• Continuous model-based testing ensures that data flows and decision logic remain within fair practice constraints, according to both technical and legal specifications.

This systematic approach addresses the traditionally reactive nature of fairness auditing (post hoc testing and mitigation), shifting fairness verification into an ongoing, transparent, and repeatable process throughout the system lifecycle.

6. Insights and Paradigm Implications

The adoption of a knowledge graph–driven framework for fairness signifies a paradigm shift, transforming high-level, subjective fairness principles into machine-verifiable rules and relationships applicable directly to software engineering tasks. Key benefits include:

• Bridging abstract legal and ethical concepts with system design, producing traceable, enforceable, and auditable fairness requirements.
• Reducing human cognitive and interpretive variability by supplying requirement engineers and developers with standard, context-sensitive templates and tooling.
• Supporting proactive, continuous assessment and mitigation of fairness risks, rather than relying on informal or ad hoc post hoc audits.
• Enabling integration with other governance, privacy, and compliance frameworks by aligning fairness knowledge graphs with broader domain ontologies.

A plausible implication is that, as AI systems become more central to high-stakes decision making, the systematic representation and verification of fairness requirements via knowledge graphs will become integral to both technical and regulatory best practices (Ramadan et al., 22 Sep 2025).

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In conclusion, a knowledge graph-based framework for fairness operationalizes the explicit, transparent, and verifiable modeling of fairness requirements, drawing on principled ontologies and automated verification methods to ensure that AI systems align not just with technical specifications but with societal, ethical, and legal expectations regarding discrimination and equity.