STRENGTHEN THE SUGGESTED FRAMEWORK, INCORPORATE BIBLIOGRAPHY AND MAKE A N OVERVIEW AS A SCHEME AS A TABLE New Artificial Intelligence $($AI$)$ Automated Reliability Assessment Framework:

Objective: To create a comprehensive framework for assessing the trustworthiness of AI systems, combining evidence from existing guidelines and research.

Basic principles:

Human$-$centered approach: Putting people at the center of the development and use of artificial intelligence, ensuring that the technology serves human needs and values.

Transparency and Explainability: Ensuring that AI systems can explain their decisions and actions in a way that humans can understand.

Fairness and Equal Treatment: Avoiding discrimination and bias in AI systems, ensuring fair and equal treatment for all.

Security and Reliability: Ensuring the security and reliability of AI systems, minimizing the risk of errors, malfunctions or malicious use.

Privacy: Comply with data protection regulations and ensure user privacy.

Responsibility and Accountability: Establishing mechanisms for assigning responsibility and accountability in case of problems or damage caused by AI systems.

Continuous Monitoring and Improvement: Monitor the performance and impact of AI systems and continuously improve the evaluation and certification processes.

Control Areas:

Fairness: Assessing bias in training data, algorithms, and results.

Transparency: Control of documentation, explainability and traceability of decisions.

Autonomy & Control: Assessment of the user's ability to understand, control and modify the behavior of the AI $$system$.$

Data Protection: Checking compliance with data protection regulations such as GDPR$.$

Security: Assessment of resistance to attacks, errors and malfunctions.

Reliability: Assessment of accuracy, stability, and generalizability.

Impact on Society: Assessing the potential social, economic and environmental impacts of AI systems.

Evaluation Methodology:

Automated Audits: Use of automated tools and techniques to audit compliance with technical specifications and security requirements.

Human Audits: Evaluation by ethical, legal and social science experts to assess social impact and compliance with ethical principles.

Continuous Monitoring: Monitor the performance and impact of AI systems in real$-$time, with the ability to intervene in case of problems.

Conclusions:

The proposed reliability assessment framework combines the basic principles and control areas proposed by HLEG, Fraunhofer IAIS, and other sources, such as "Generative AI GEN Risks" and "A global scale comparison of risk aggregation in AI assessment frameworks" $.$ It also integrates automated assessment with human expertise for a more comprehensive approach.