AI Today Podcast: Trustworthy AI Series: Governed AI
Oct 4, 2023
auto_awesome
The podcast discusses the importance of governance in AI and the need for controls, processes, and organizational structure. It emphasizes trustworthy AI practices, including system auditability and risk management. The chapter also explores the importance of building trust in AI systems through an appeals process and transparency. Additionally, it discusses the significance of establishing controls, compliance, and data governance to ensure trustworthiness. The podcast highlights the importance of monitoring and managing AI systems to maintain their trustworthiness.
Governance is an essential aspect of building trustworthy AI systems, involving oversight, control, and visibility into the design, development, and usage of AI systems.
Auditability and contestability are critical components of governance, enabling organizations to audit decisions, gain operational control, and address questions about AI system operations.
Deep dives
The Importance of Governing AI Systems
Governed AI is one of the crucial layers of building trustworthy AI systems. It involves implementing controls, processes, and organizational structures that provide oversight, control, and visibility into the design, development, management, and usage of AI systems and the associated data. Trustworthy AI requires accountability, consent, disclosure, and other aspects falling under governance. System auditability and contestability are essential elements to consider, allowing organizations to audit and contest AI systems' decisions. Establishing periodic reviews, ensuring compliance with framework practices, and keeping an eye on data quality and model drift are key control measures. Ultimately, governance plays a critical role in building and maintaining trust in AI systems.
The Need for Auditability and Traceability in AI Systems
Auditability and traceability are crucial components of governance in AI systems. AI systems should maintain comprehensive records of data provenance, lineage, and storage, ensuring the ability to audit decisions and gain operational control and visibility. Implementing audit trails and capturing relevant data streams enable the documentation of the entire AI system process, from data collection to decision-making. Creating a robust audit trail ensures transparency, accountability, and the ability to address questions about AI system operations. By automating documentation processes, organizations can manage the volume of required documentation effectively.
Enabling Appeals and Contestability in AI Systems
Allowing individuals to contest and appeal AI system decisions is vital for building trust and ensuring responsible AI practices. Implementing mechanisms for contestability provides people with the ability to challenge AI system outputs, even if they cannot change the outcome. It goes beyond disclosure and consent, providing avenues for addressing unintended outcomes and appealing biased or unfair decisions. By enabling contestability, organizations display transparency, accountability, and a commitment to trust-building. Offering a clear process for contesting decisions, documenting appeals, and making decisions on those appeals enhances the overall trustworthiness of AI systems.
AI systems have the potential to provide great value. But, also the potential to cause great harm. Knowing how to build or use AI systems is simply not going to be enough. You need to know how to build, use, and interact with these systems ethically and responsibly. Additionally you need to understand that Trustworthy AI is a spectrum.