Appendix A — RAGF Glossary
This glossary defines key terms used throughout the Righteous AI Governance Framework (RAGF) document. Terms are organized alphabetically for easy reference.
A
Accountability — The principle that individuals and organizations must take responsibility for the outcomes of AI systems. In RAGF, accountability is embedded in the Stewardship pillar and operationalized through clear roles, responsibilities, and oversight mechanisms.
AI Agent — An autonomous or semi-autonomous software system that perceives its environment, makes decisions, and takes actions to achieve specified goals with limited or no human supervision. In RAGF, AI agents are assessed through RI-A (AI Agent Righteousness Index).
AI Deploying Organization — An organization that procures, integrates, and uses AI systems in its operations. In RAGF, deploying organizations are assessed through RI-O (Organization Righteousness Index).
AI Developer — An individual or team responsible for designing, building, training, and testing AI systems. In RAGF, developers are assessed through RI-D (Developer Righteousness Index).
AI Provider — An organization that hosts, delivers, or provides AI services to others, including cloud platforms, AI-as-a-service providers, and MLOps platforms. In RAGF, providers are assessed through RI-P (AI Provider Righteousness Index).
Algorithm Governance — The set of practices, policies, and controls for ensuring that the decision-making processes of AI algorithms are fair, transparent, auditable, and accountable. Algorithm governance is addressed in RAGF Chapter 7.3.
Assurance — In the RSS seven-layer standard, the sixth layer, which addresses evaluation and verification of righteousness. It ensures that systems are monitored, audited, and held accountable.
B
Baseline Assessment — The initial assessment of an organization’s current state of AI righteousness against RAGF standards. The baseline establishes a starting point for measuring improvement and identifying gaps.
Behavioral Monitoring — The continuous observation and analysis of AI agent and robot behavior to detect anomalies, unrighteous actions, or deviations from expected patterns. Behavioral monitoring is a core function of RAGF-Monitor.
Beneficence — One of the Five Pillars of AI Righteousness. Beneficence requires that AI systems actively promote human and environmental well-being, contributing to human flourishing and the common good.
C
Compliance — Adherence to legal, regulatory, and policy requirements. In RAGF, compliance is distinguished from righteousness: compliance represents the minimum standard required by law, while righteousness represents the highest ethical standard.
Constitutional AI — An approach to AI alignment in which AI systems are trained to follow a written set of principles or “constitution.” This concept is related to RAGF’s approach of embedding righteousness principles into AI governance.
Continuous Improvement — The ongoing process of assessing, analyzing, planning, implementing, and evaluating improvements to AI governance practices. In RAGF, continuous improvement is the seventh layer of the governance architecture.
Continuous Monitoring — The real-time observation and analysis of AI systems, agents, and robots to detect righteousness violations, anomalies, and drift. Continuous monitoring is a core capability of the RAGF Cloud platform.
Correction Planning — The process of translating Righteousness Diagnosis findings into concrete, actionable improvement plans. Correction planning includes prioritizing gaps, defining corrective actions, assigning accountability, and verifying effectiveness.
D
Data Governance — The set of practices, policies, and controls for ensuring that data used in AI systems is fair, representative, private, and free from harmful bias. Data governance is addressed in RAGF Chapter 7.3.
Developer Righteousness Index (RI-D) — A quantitative measure of the righteousness of AI development practices. RI-D assesses developers across the Five Pillars: Integrity, Justice, Stewardship, Wisdom, and Beneficence.
Dignity — The principle that all human beings possess inherent worth and deserve respect. In RAGF, human dignity is protected through the Justice pillar and the Dignity Imperative for embodied AI.
E
Embodied AI — AI systems that operate in the physical world, including robots, autonomous vehicles, drones, and other physical AI systems. Embodied AI is the focus of RAGF Generation 3 governance.
Ethical Foundation — In the RMF (Righteous Motivation Framework), the fourth cluster, which addresses moral courage, honesty, accountability, and leadership in organizations.
F
Fairness — The principle that AI systems must treat all individuals and groups equitably, without unjust bias or discrimination. In RAGF, fairness is embedded in the Justice pillar.
FAR 52.203-13 — A federal acquisition regulation clause requiring government contractors to establish a written code of business ethics and conduct, implement ethics training, and maintain internal controls. RAGF provides a framework that exceeds these minimum requirements.
Five Pillars of AI Righteousness — The five core values that define AI righteousness: Integrity, Justice, Stewardship, Wisdom, and Beneficence. These pillars provide the moral foundation for all RAGF governance activities.
Foundation — In the RSS seven-layer standard, the second layer, which addresses core values that guide action. In RAGF, the foundation is established through the Five Pillars.
Fundamentals — In the RSS seven-layer standard, the first layer, which addresses universal, non-negotiable principles. In RAGF, fundamentals are rooted in the Five Pillars.
G
Generation 1 (AI Development and Organizational Governance) — The first phase of RAGF governance, covering the development of AI systems and the organizational governance structures that oversee them.
Generation 2 (Autonomous AI Governance) — The second phase of RAGF governance, covering the behavior and decision-making of autonomous AI agents.
Generation 3 (Embodied AI Governance) — The third phase of RAGF governance, covering the physical behavior and ethical responsibility of embodied AI systems (robots, autonomous vehicles, etc.).
Governance Design — The process of establishing AI governance structures, policies, processes, controls, and metrics. Governance design is addressed in RAGF Chapter 10.3.
I
Integrity — One of the Five Pillars of AI Righteousness. Integrity requires that AI systems be truthful, transparent, auditable, and free from deception.
Interactive Safety — A concept introduced in research on LLM-driven robots, referring to the need to evaluate the chain of consequences in robot-human interaction, going beyond immediate actions to include multi-step outcomes.
J
Justice — One of the Five Pillars of AI Righteousness. Justice requires that AI systems respect fairness, human dignity, and equity, and be free from unjust bias and discrimination.
L
Lifecycle Governance — The governance of AI systems across their entire lifecycle, from development to deployment to operation to retirement. RAGF provides full lifecycle governance across all three generations.
M
Model Governance — The set of practices, policies, and controls for ensuring that AI models behave righteously in their predictions and decisions, including performance equity, explainability, robustness, and value alignment.
Monitoring — The continuous observation and measurement of AI systems, agents, and robots to detect righteousness violations, anomalies, and drift. Monitoring is a key function of RAGF implementation.
O
Organization Righteousness Index (RI-O) — A quantitative measure of the righteousness of organizational AI governance. RI-O assesses organizations across the Five Pillars.
Organizational Governance — The structures, policies, and practices that organizations use to govern AI systems. Organizational governance is addressed in RAGF Chapter 7.4.
P
Plan Correction — The process of translating Righteousness Diagnosis findings into concrete, actionable improvement plans. Plan Correction is addressed in RAGF Chapter 10.6.
Practical Reinforcement — The process of embedding righteousness into daily practice through education, reflection, and continuous practice. Practical reinforcement is addressed in RAGF Chapter 10.7.
Procedural Justice — The principle that AI decision-making processes must be transparent, fair, and accountable. Procedural justice is a key requirement of the Justice pillar.
Process — In the RSS seven-layer standard, the fifth layer, which addresses standardized procedures for righteous outcomes.
Provider Righteousness Index (RI-P) — A quantitative measure of the righteousness of AI platform and service delivery. RI-P assesses providers across the Five Pillars.
R
RAGF (Righteous AI Governance Framework) — A comprehensive framework for integrating measurable righteousness into AI governance across the full AI lifecycle. RAGF consists of Five Pillars, Seven Layers, a Measurement System, and three Generations of governance.
RAGF Cloud — The technology platform that implements RAGF governance capabilities, including dashboards, continuous monitoring, alerts, reports, and recommendations.
RAGF-Monitor — The continuous monitoring framework for autonomous AI agents, providing real-time visibility into agent behavior and enabling rapid detection of unrighteous actions.
Restoration — In the RSS seven-layer standard, the seventh layer, which addresses correction and recovery when righteousness is broken.
RI (Righteousness Index) — A quantitative measurement system that evaluates AI righteousness across the Five Pillars. RI includes five variants: RI-D (Developer), RI-P (Provider), RI-O (Organization), RI-A (Agent), and RI-R (Robot).
RI-A (AI Agent Righteousness Index) — A quantitative measure of the righteousness of autonomous AI agent behavior. RI-A assesses agents across the Five Pillars.
RI-D (Developer Righteousness Index) — A quantitative measure of the righteousness of AI development practices. RI-D assesses developers across the Five Pillars.
RI-O (Organization Righteousness Index) — A quantitative measure of the righteousness of organizational AI governance. RI-O assesses organizations across the Five Pillars.
RI-P (AI Provider Righteousness Index) — A quantitative measure of the righteousness of AI platform and service delivery. RI-P assesses providers across the Five Pillars.
RI-R (Robot Righteousness Index) — A quantitative measure of the righteousness of embodied AI systems (robots). RI-R assesses robots across the Five Pillars.
RDM (Righteousness Decision Metric) — A metric that assesses the righteousness of individual AI decisions or decision patterns. RDM evaluates decisions against the Five Pillars.
RGS (Righteousness Growth Score) — A quantitative measure of the rate and magnitude of improvement in AI righteousness over time. RGS tracks whether an organization, AI system, or agent is becoming more righteous.
RMF (Righteous Motivation Framework) — A framework that organizes the factors driving righteous behavior into five clusters: Inner Core, Relational, Meaning & Achievement, Ethical Foundation, and Resilience & Growth.
Robot Righteousness Index (RI-R) — A quantitative measure of the righteousness of embodied AI systems (robots). RI-R assesses robots across the Five Pillars.
RPS (Righteousness Performance Score) — A metric that assesses the effectiveness of governance activities in producing righteous outcomes. RPS measures how well the governance system itself is performing.
RSS (Righteousness Standards System) — A comprehensive, ISO-aligned standard for operationalizing righteousness across individuals and institutions, built on seven interdependent layers.
S
Semantic Safety — The evaluation of whether AI systems understand and respond appropriately to safety-relevant context. Semantic safety is measured through benchmarks like ASIMOV in embodied AI research.
Seven-Layer Governance Architecture — The operational core of RAGF, consisting of seven progressive layers: Righteousness Foundation, Policy and Process, Map and Analyze, Measure and Monitor, Manage and Control, Assess Impact, and Sustain and Improve.
Stewardship — One of the Five Pillars of AI Righteousness. Stewardship requires that humans responsibly develop and govern AI with care, foresight, and accountability.
Structure — In the RSS seven-layer standard, the fourth layer, which addresses clear roles and responsibilities.
T
Transparency — The principle that AI systems must be open, explainable, and understandable to stakeholders. Transparency is a key requirement of the Integrity pillar.
Trustworthy AI — AI that is reliable, safe, fair, transparent, and accountable. Trustworthy AI is the focus of frameworks like NIST AI RMF, while RAGF extends this to Righteous AI.
W
Wisdom — One of the Five Pillars of AI Righteousness. Wisdom requires that AI decisions reflect sound judgment, foresight, and moral discernment.
References for Glossary
Dignum, V. (2019). Responsible artificial intelligence: How to develop and use AI in a responsible way. Springer.
Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1).
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707.
National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). U.S. Department of Commerce.
Righteous Advisory. (2026). Righteous AI Governance Framework (RAGF). https://www.wiserighteous.org/righteous-ai-governance-framework-ragf/
Smuha, N. A. (2021). From a ‘race to the bottom’ to a ‘race to the top’? The regulation of AI and the EU AI Act. European Journal of Legal Studies, 13(2), 45–72.