1. Accountability: AI systems must be accountable for their actions and decisions, and organizations must be able to trace the root cause of an issue and take corrective action.
2. Data Governance: Organizations must ensure compliance with data privacy laws and regulations. They must be able to audit and monitor AI-driven systems and have processes in place to handle data breaches.
3. Transparency: AI systems must be transparent in terms of their decision-making processes. This transparency allows organizations to understand the factors that affect outcomes and to adjust them accordingly.
4. Auditability: AI systems must be auditable, allowing organizations to monitor and assess the performance of the system, and to detect and address any potential issues.
5. Fairness: AI systems must ensure fairness in the decisions they make and the data they use. This includes preventing bias and eliminating discrimination.
6. Reliability: AI systems must be reliable, with outcomes that are consistent and accurate.
7. Safety: AI systems must be safe, with safeguards in place to protect users and their data.
2. Data Governance: Organizations must ensure compliance with data privacy laws and regulations. They must be able to audit and monitor AI-driven systems and have processes in place to handle data breaches.
3. Transparency: AI systems must be transparent in terms of their decision-making processes. This transparency allows organizations to understand the factors that affect outcomes and to adjust them accordingly.
4. Auditability: AI systems must be auditable, allowing organizations to monitor and assess the performance of the system, and to detect and address any potential issues.
5. Fairness: AI systems must ensure fairness in the decisions they make and the data they use. This includes preventing bias and eliminating discrimination.
6. Reliability: AI systems must be reliable, with outcomes that are consistent and accurate.
7. Safety: AI systems must be safe, with safeguards in place to protect users and their data.