Tools for Trusted AI

What is Trusted AI – Let’s ask ChatGPT!

Trusted AI encompasses systems that are not only ethically aligned but also reliable, robust, secure, transparent, and accountable. While ethical considerations form a crucial aspect, Trusted AI extends to include the technical quality and performance of the system. The goal is to create AI technologies that various stakeholders—including users, developers, businesses, and regulators—can trust to behave as intended and to meet specific performance, security, and governance standards.

The concept integrates aspects from multiple domains, such as:

• Ethics: Fairness, transparency, and accountability.
• Reliability: Consistent and dependable system performance.
• Robustness: Ability to handle unexpected inputs or conditions without failing.
• Security: Measures to protect data and models from unauthorized access and tampering.
• Transparency: Understandability of system decisions and actions.
• Accountability: Mechanisms to trace actions back to the responsible entities for evaluation and auditing.

In this broader context, “trust” is a multidimensional construct. It’s not just about being ethical but about fulfilling a wide range of expectations and standards that make a system worthy of trust.

What is Trusted AI – Let’s ask Claude 2!

Trusted AI refers to artificial intelligence systems that are designed and validated to be reliable, safe and technically robust. Key principles of trusted AI include:

• Reliability - AI systems consistently produce accurate, reproducible results that can be externally validated. Models are unbiased and decisions explainable.

• Safety and security - AI systems are developed using secure software practices and are resilient against attacks, hacking or misuse. They protect privacy and confidentiality.

• External oversight - There are independent audits and ongoing monitoring of AI systems to ensure transparency and identify issues. Public disclosure builds trust.

• Human control - Humans remain fully in control of AI systems, which augment human intelligence rather than replace it. Autonomous systems have human supervision.

• Proportionality - AI capabilities do not exceed what is appropriate for the task. AI is not used when less advanced technology suffices.

• Technical robustness - AI systems are thoroughly tested for stability, scalability and corner cases. Failsafes prevent glitches or unintended harm.

In contrast to responsible AI, trusted AI emphasizes technical system properties over organizational processes. It focuses on engineering reliable, safe and externally validated AI, rather than holistic internal governance.

Foundational Components for Trusted AI

• Automate the integration and deployment of code, ensuring quality and operational efficiency.
• Standardized Development Environments: Establish consistent, easily replicable environments to accelerate development and experimentation.
• Data & Experiment Versioning: Implement robust systems to track changes in data and experiments, allowing for traceability and repeatability.
• Model Lifecycle Management: Streamline the training, deployment, monitoring, and updating of machine learning models.
• Flexibility Across Layers: Design the architecture to allow for different levels of customization, from high-level APIs to low-level controls, facilitating adaptability.