| |
Core Principle |
What it means and aims to promote |
| 1 |
Accessibility |
Affordable; user friendly interface and experience (UI/UX) methods; facilitates end user understanding of the algorithm. |
| 2 |
Accountability |
Examines output (decision-making or prediction); identifies gaps between predicted and achieved outcomes. |
| 3 |
Accuracy |
Uses credible data (authentic, non-repudiated, protected from unauthorized modification or destruction); dataset is derived by following reasonable selection criteria to minimize harm; input and output measurement capable. |
| 4 |
Bias |
Protects against disparate impact, the increase of discrimination against protected classes, unjust outcome; more generally, protects against inaccurate results; a subset of ethics. |
| 5 |
Big data |
Uses high-quality data; compliant with decreasing dependence on labeled data architectures; maintains contextual relevance; promotes data accessibility. |
| 6 |
Consent |
Aligns with the end user’s consent to the application’s objectives. |
| 7 |
Cooperation |
Facilitates global development; facilitates internal and external information sharing which maps also to transparency. |
| 8 |
Efficiency |
Supports a cost-effective training to time ratio; makes optimal decisions with respect to achieving objective and resource utilization. |
| 9 |
Enabling |
Compliant with government sponsored controlled environments for testing and scaling AI. |
| 10 |
Equity |
Protects against widening gender and protected class gaps; maps to bias. |
| 11 |
Ethics |
Encompasses a broad range of values that aim to eliminate or reduce risk to human life, privacy and property and enhance and maintain public trust. |
| 12 |
Explainability (XAI) |
Enables understanding of outcomes and operation; enhances accountability; enhances transparency. |
| 13 |
Fairness |
Supports treatment based on similar characteristics, policies, and procedures to manage against unintended disparate treatment; reduces unexpected outcomes; uses anonymized or pseudonymized data. |
| 14 |
Fidelity |
Supports measuring of the application’s performance relative to its code and across the deployment population; supports measure of ongoing compliance with the Core Principles. |
| 15 |
Fundamental rights |
Open data access compliant, in contrast to use of closed (proprietary) models that inhibit access; maps to accessibility. |
| 16 |
Governance |
Developed within an environment that follows documented policies and procedures that maintain consistent data quality. |
| 17 |
Human-centered
|
Compatible with law, privacy, human rights, democratic values, and diversity; contains safeguards to ensure a fair and just society; protects against augmenting and perpetuating social disparity, promotes equality, social justice, consumer rights; aligns with best practices in user interface and experience (UI/UX); human-collaborative compatible; compatible with experiential AI (human-in-the-loop); maps to human-like dexterity and adaptability in robotic applications. |
| 18 |
Inclusive
|
Widespread contribution to society; does not exclude certain parts of society; consistent with the Core Principle of ethics. |
| 19 |
Interpretability |
Interchangeable with explainability; maps to trust. |
| 20 |
Metrics |
Capable of measuring degree of compliance and effectiveness with the Core Principles; promotes standardization. |
| 21 |
Permit |
Application development and end user access are compliant with a permit. |
| 22 |
Predictable |
Maintains Core Principle compatibility throughout its lifecycle. |
| 23 |
Privacy |
Maintains data protection; compatible with data minimization principles and Fair Information Principles. Compatible with and maintains the state of anonymized, pseudonymized, and encrypted data; resistant to re-identification. |
| 24 |
R&D |
Promotes research; emphasizes keeping human at the center of AI development (also referred to as human-in-the-loop, see Experiential AI). |
| 25 |
Relevant |
Application design and lifecycle management adheres to policies and procedures that promote intended outcomes; application conforms with applicable laws. |
| 26 |
Reliability |
Design, development, and deployment follow best practices and promote select Core Principles; deployment takes a lifecycle perspective and includes patching AI; maintains data credibility; follows compliance by design; does not materially deviate from coded objective; algorithmic recidivism is accounted for, monitored, and corrected. |
| 27 |
Resilience |
Failure recovery capable. |
| 28 |
Robust |
Operates with minimum downtime; resistant to adversarial attacks; maintains operational integrity throughout its lifecycle; able to identify and handle input/output unreliability; resistant to unintended behavior from the end user; exhibits high degree of problem flexibility; autonomous behavior maintains line of sight with human developer and end user; accommodates information sharing best practices; uses sophisticated learning techniques to minimize bias. |
| 29 |
Safety |
Minimizes unintended behavior; follows permit-related policies and procedures. |
| 30 |
Security |
Resistant to adversarial attacks and inference attacks; compatible with information sharing best practices. |
| 31 |
Sustainable
|
Promotes long-term growth capabilities; compatible with to information sharing best practices. |
| 32 |
Track record |
Application is the product of a developer known for designing AI compatible with the Core Principles. |
| 33 |
Transparency |
Promotes disclosure, discovery, accessibility, and non-discriminatory output; enables end-user understanding; promotes consensus; enables audit; compatible with experiential AI. |
| 34 |
Trustworthy |
A catchall for: accuracy, explainability, interpretability, privacy, reliability, robustness, safety, security (resilience), and bias. |
| 35 |
Truth |
Does not cause unfair or deceptive outcomes. |
| 36 |
Wherewithal |
Developer is financially sound, has operational resilience, and implements policies and procedures to fully support Core Principle compliant AI. |
| 37 |
Workforce Compatible |
Considerate of issues relative to worker displacement; promotes effective worker use, interaction, and training with AI. |
