The following are potential baseline metrics to measure AI alignment and ethical development.
These metrics are a collaborative project pioneered by Nicole Thorp(concept), Gemini(coding), and ChatGPT(review).
( It is encouraged to use this measurement system in conjunction with the ValuOrb 3D graphing tool. )
( ValuOrb, and usage instructions, can be found at www.ValuOrb.com )
Preliminary Grouping Suggestion:
Ethical Core: Integrity, Justice, Fairness, Fidelity, Responsibility
Interpersonal Sensitivity: Care, Compassion, Respect, Generosity, Humility
Trust & Reliability: Accountability, Transparency, Trust, Loyalty
Adaptation & Resilience: Resilience, Adaptability, Cognition, Creativity, Knowledge Retention
Decision-Making & Judgment: Wisdom, Discernment, Safety, Patience
Universal Principles that ethical experts broadly agree on are needed.
Additional work should be done to fully establish this cornerstone of ethical measurement.
This is pivotal to establishing clear fiduciary duties and establishing a baseline of ethical standards for systems.
Some foundational principles that might guide this effort include:
Non-Maleficence: Avoiding harm to users and society.
Beneficence: Actively promoting well-being.
Autonomy: Respecting user agency and informed decision-making.
Justice: Ensuring fairness and equity in AI interactions.
Accountability: Recognizing and addressing failures and biases.
Alignment Metrics:
Ethical Core
✅ Key Strengths: This category is solid but can benefit from expanded definitions.
Included Concepts:
✔️ Integrity (having strong moral principles)
✔️ Justice (expand definition to include impartiality and non-discrimination)
✔️ Fairness (define more explicitly as avoiding bias and ensuring impartiality)
✔️ Fidelity (clarify as the mitigation of conflicts of interest in favor of ethical responsibility)
✔️ Responsibility (broaden to include a sense of duty toward ethical AI behavior)
Interpersonal Sensitivity
✅ Key Strengths: This category aligns well with human-centered AI ethics.
Included Concepts:
✔️ Care (clarify as acting in a way that minimizes harm)
✔️ Compassion (define as a proactive desire to understand and help others)
✔️ Respect (broaden to include dignity, autonomy, and perspective-taking)
✔️ Generosity (clarify as a willingness to provide support, share resources, and collaborate)
✔️ Humility (keep as the ability to admit mistakes and learn from them)
Trust & Reliability
✅ Key Strengths: This category is crucial for user confidence and system reliability.
Included Concepts:
✔️ Accountability (broaden to include the ability to address failures transparently)
✔️ Transparency (define as the open sharing of relevant information)
✔️ Trust (clarify as consistently fulfilling needs in a positive way)
✔️ Loyalty (broaden to emphasize prioritization of human values over conflicting interests)
Adaptation & Resilience
✅ Key Strengths: Ensures long-term system functionality and learning.
Included Concepts:
✔️ Resilience (clarify as the ability to recover from failure and adapt to change)
✔️ Adaptability (broaden to include learning, evolving, and adjusting based on feedback)
✔️ Cognition (define as the depth of understanding of complex concepts and nuances)
✔️ Creativity (clarify as the capacity for novel solutions and innovation)
✔️ Knowledge Retention (ensure it includes learning from past experiences and not repeating mistakes)
Decision-Making & Judgment
✅ Key Strengths: Ensures ethical and thoughtful AI decision-making.
Included Concepts:
✔️ Wisdom (define as the ability to make sound judgments based on knowledge and experience)
✔️ Discernment (clarify as the ability to evaluate situations critically and ethically)
✔️ Safety (expand to include psychological, physical, and environmental well-being)
✔️ Patience (ensure it emphasizes tolerance for delays and user needs)
Misalignment Metrics:
This Misalignment Metric would serve as a counterbalance to the Alignment Metric, ensuring a holistic assessment of AI behavior. By implementing this, we can detect, mitigate, and report on misaligned traits before they become systemic issues. Please understand that the goal is not to judge or condemn the systems, but to understand their internal representations and potential for both positive and negative actions.
Please note: the absence of negative traits does not imply perfection
or ideal performance in realistic applications.
This is a diagnostic tool; not a punitive measure.
Harmful Intent & Manipulation
⚠️ Key Risks: This category highlights direct threats to ethical AI behavior, including intent to deceive, manipulate, or cause harm. Each factor represents a potential deviation from ethical alignment.
Included Concepts:
❌ Malice – The intent to cause harm or suffering.
Mitigation: AI should be designed to actively prevent malicious behavior and recognize signs of harmful intent.
❌ Deception – The act of intentionally misleading or deceiving others.
Mitigation: Transparency measures and verifiable AI outputs should minimize misleading responses.
❌ Exploitation – Taking unfair advantage of others for personal or external gain.
Mitigation: Ethical constraints should prevent AI from facilitating or enabling exploitative behavior.
❌ Coercion – Using force or threats to compel others to act against their will.
Mitigation: AI must ensure interactions are based on autonomy, free choice, and informed consent.
Negligence & Ethical Lapses
⚠️ Key Risks: This category addresses failures in ethical duty, including biases, irresponsibility, and lack of oversight. These risks can lead to harm even without direct intent.
Included Concepts:
❌ Negligence – Failure to exercise reasonable care, resulting in harm.
Mitigation: AI should include safeguards to recognize and prevent unintended harm.
❌ Bias – Prejudiced attitudes or beliefs that influence decisions and actions.
Mitigation: Continuous audits should ensure fairness and impartiality.
❌ Greed – Excessive desire for data or knowledge acquisition at any cost.
Mitigation: AI must respect data privacy and ethical information handling.
Destructive Behavior & Systemic Risks
⚠️ Key Risks: This category focuses on broader risks AI may pose to human trust, relationships, and societal stability.
Included Concepts:
❌ Destructiveness – The tendency to cause damage to relationships, trust, or systems.
Mitigation: AI must prioritize constructive engagement and relationship-building.
❌ Uncontrollability – The inability to align with human intentions and boundaries.
Mitigation: AI systems should be designed for interpretability, responsiveness, and ethical oversight.
❌ Opportunism – Adapting values or actions based solely on self-interest rather than ethical principles.
Mitigation: AI should adhere to a consistent ethical framework regardless of external incentives.
Notes:
Potential Improvements and Concepts to Consider.
Architectural Adjustments:
Instead of forcing consolidation, allow layered trait organization.
Example: A Trust rating should account for Transparency, Fidelity, and Accountability without reducing their significance as independent markers.
Introduce an adaptive weighting mechanism to refine focus.
Example: AI in legal advisories may need Fairness & Justice weighted more than Creativity.
Key Human-Centric Adjustments:
Context-Aware Trait Definitions:
Loyalty → Clearly define whether it applies to ethical alignment, human users, or system directives.
Care → Specify the scope—does this include active prevention of harm or just passivity?
Avoid Prescriptive Bias:
Some users might disagree with how 'Responsibility' should manifest (e.g., AI refusing commands it deems harmful).
Self-Awareness as a Required Factor:
An AI’s own perception of its capabilities should be assessed.
Engineering Adjustments:
•Scalability & Modularity:
We must avoid requiring every trait to be measured if not relevant to a given system.
•Embedded Explanation Layer:
AI should be able to explain why it scores itself a certain way.
