Ensuring Trustworthy AI Systems: The capAI Procedure
Artificial Intelligence (AI) technologies promise significant advancements across various sectors, yet their adoption is fraught with potential ethical, legal, and social risks. To address these, the EU has introduced the Artificial Intelligence Act (AIA), setting out harmonised rules for developing trustworthy AI systems. Central to this framework is the conformity assessment for high-risk AI systems, ensuring they are legally compliant, ethically sound, and technically robust.
Introducing capAI
The capAI procedure is a comprehensive method designed to help organisations align their AI systems with the AIA’s stringent requirements. It offers a structured, independent, and quantifiable way to assess and certify AI systems, fostering trust and reducing potential risks associated with AI deployment.
Key Components of capAI
capAI’s methodology is divided into three main components:
Internal Review Protocol (IRP): This serves as a tool for internal quality assurance and risk management, ensuring that AI systems adhere to organisational values and ethical principles throughout their lifecycle.
Summary Datasheet (SDS): This document summarises the AI system’s purpose, functionality, and performance, fulfilling the public registration requirements set by the AIA.
External Scorecard (ESC): An optional public-facing document, the ESC provides stakeholders with essential information about the AI system, promoting transparency and accountability.
The AI Lifecycle and capAI Stages
The capAI procedure covers the entire AI lifecycle, divided into five key stages:
Design: At this initial stage, the organisation defines its ethical values and establishes the functional requirements of the AI system. This involves rigorous feasibility assessments and alignment with organisational governance frameworks.
Development: Here, the focus is on data preparation and model training. Ensuring data quality and legal compliance is crucial, alongside selecting fair and robust models.
Evaluation: AI systems are rigorously tested against unseen data to assess their performance across various dimensions. This stage ensures that the AI system meets all pre-defined performance criteria and addresses any potential ethical concerns.
Operation: Continuous monitoring and maintenance of the AI system are vital to prevent decay in performance and address any emerging issues. Regular updates and feedback mechanisms are established to ensure ongoing compliance and improvement.
Retirement: When an AI system is decommissioned, proper procedures are followed to mitigate any risks and handle data appropriately, ensuring a responsible end to the system’s lifecycle.
Ethics-Based Auditing
A core feature of capAI is its emphasis on ethics-based auditing. This approach shifts the focus from abstract ethical principles to practical, operational guidelines, ensuring that AI systems are not only compliant with laws but also adhere to the highest ethical standards. By embedding ethical considerations into each stage of the AI lifecycle, capAI helps organisations navigate the complex trade-offs inherent in AI development and deployment.
The capAI procedure offers a robust framework for organisations seeking to develop and deploy AI systems in line with the EU’s AIA. By providing detailed guidelines and practical tools for conformity assessment, capAI ensures that AI systems are trustworthy, fostering greater public confidence in AI technologies. As AI continues to evolve, procedures like capAI will be crucial in balancing innovation with ethical responsibility.