Step by Step: How SAFE AI contributes to trustworthy AI
"Based on the National Institute of Standards and Technology (NIST) AI Risk Management Framework, the seven characteristics of trustworthy AI are:
1. Valid and Reliable: The AI system should perform as intended, delivering accurate results consistently across different situations.
2. Safe: The system must not produce harmful or dangerous outcomes to human life, health, property, or the environment.
3. Secure and Resilient: The AI must withstand unforeseen events, attacks, or adversarial inputs, maintaining its functionality without compromising data.
4. Accountable and Transparent: There should be a clear understanding of how the system operates, including documentation on data, decisions, and responsibility for its outputs.
5. Explainable and Interpretable: Users should understand the reasoning behind a particular output or decision made by the AI.
6. Privacy-Enhanced: The system must protect sensitive data, uphold privacy standards, and avoid leaking information across sessions.
7. Fair with Harmful Bias Managed: The AI must treat different user groups equitably, actively identifying and mitigating bias in training data or results." (Page 1)
1. Valid and Reliable: The AI system should perform as intended, delivering accurate results consistently across different situations.
2. Safe: The system must not produce harmful or dangerous outcomes to human life, health, property, or the environment.
3. Secure and Resilient: The AI must withstand unforeseen events, attacks, or adversarial inputs, maintaining its functionality without compromising data.
4. Accountable and Transparent: There should be a clear understanding of how the system operates, including documentation on data, decisions, and responsibility for its outputs.
5. Explainable and Interpretable: Users should understand the reasoning behind a particular output or decision made by the AI.
6. Privacy-Enhanced: The system must protect sensitive data, uphold privacy standards, and avoid leaking information across sessions.
7. Fair with Harmful Bias Managed: The AI must treat different user groups equitably, actively identifying and mitigating bias in training data or results." (Page 1)
Stage 1: Trustworthiness in problem definition and concept, 1
Stage 2: Trustworthiness in design, 3
Stage 3: Trustworthiness in development, 4
Stage 4: Trustworthiness in deployment, monitoring and evaluation, 6
Stage 2: Trustworthiness in design, 3
Stage 3: Trustworthiness in development, 4
Stage 4: Trustworthiness in deployment, monitoring and evaluation, 6
