Module B - AI Ethics Operationalisation Workflow
ModulesModule B - AI Ethics Operationalisation Workflow
Module B introduces the AI Ethics Operationalisation Workflow as the central operational framework of the AIOLIA training programme. It translates high-level AI ethics principles into a structured, step-by-step workflow that supports the practical assessment, identification, and mitigation of ethics issues in AI systems across their lifecycle. The module builds on three core building blocks—ethics requirements, practical measures, and ethics tensions—which collectively support the operationalisation of the seven ALTAI requirements in real-world contexts. Illustrated through a practical use case scenario, it guides learners through a 5-step iterative process and establishes the operational backbone of the AIOLIA methodology.
Learning Goals
- Understand the 5-step AI Ethics Operationalisation Workflow and its role in bridging theory and practice
- Use Section 0 of the AIOLIA Issues Identification Checklist to establish the operational context of an AI system
- Identify potential AI ethics risks and map them under the seven ALTAI requirements using Sections 1–7 of the AIOLIA Issues Identification Checklist
- Navigate, select, and adapt context-appropriate measures from the AIOLIA Portfolio of Measures (119 technical and organisational measures)
- Recognize and analyze ethics tensions and trade-offs that arise between competing ALTAI requirements
- Perform an Ethics Check to validate findings, ensuring completeness, traceability, and auditable documentation
What is this about?
This module introduces the AI Ethics Operationalisation Workflow as the central operational framework of the AIOLIA training programme. It translates high-level AI ethics principles into a structured, step-by-step workflow that supports the practical assessment, identification, and mitigation of ethics issues in AI systems across their lifecycle.
Building on three core building blocks, ethics requirements, practical measures, and ethics tensions, the module demonstrates how the seven ALTAI requirements can be operationalised in real-world contexts. Through a practical use case scenario, learners explore a 5-step iterative process that forms the operational backbone of the AIOLIA methodology.Why is this important?
While high-level AI ethics principles and guidelines are widely recognized, translating them into concrete engineering choices, organizational policies, and daily practices remains a significant challenge. High-level principles often feel abstract, and real-world implementation inevitably brings competing priorities and ethics tensions.
This module bridges the gap between theory and practice by providing a structured, step-by-step workflow. By learning how to systematically assess risks, apply targeted technical and organizational measures, and navigate ethics trade-offs, learners gain the operational tools needed to design, evaluate, and deploy trustworthy AI systems in real-world environments.The building blocks
The AI Ethics Operationalisation Workflow provides a structured approach to translating AI ethics requirements into practical actions in the design, development and deployment of AI systems.
The workflow is based on three interconnected building blocks: Ethics Requirements, Practical Measures and Ethics Tensions.
It draws on the seven key requirements for Trustworthy AI, as defined by the European Commission's High-Level Expert Group on Artificial Intelligence and operationalised through the Assessment List for Trustworthy Artificial Intelligence (ALTAI). These requirements provide a framework for identifying ethics considerations in specific AI application contexts.
To address the identified ethics issues, the workflow distinguishes between technical measures, focusing on the design and operation of AI systems, and organisational measures, focusing on the policies, procedures and governance practices needed to support responsible AI.
Finally, the workflow recognises that ethics requirements may interact and sometimes pull in different directions. Identifying and addressing these ethics tensions is therefore an essential part of operationalising AI ethics in practice.
The following presentation introduces the three building blocks and illustrates how they can be applied through practical examples and scenarios.
AI Ethics Operationalisation Workflow
The AI Ethics Operationalisation Workflow bridges the gap between high-level ethics principles and concrete, day-to-day practices across the AI lifecycle. It is built on three core building blocks: Ethics Requirements (the 7 ALTAI requirements), Practical Measures (a catalogue of 119 technical and organizational safeguards - Portfolio of Measures), and Ethics Tensions ("right vs. right" conflicts between competing ethics priorities).
The framework executes through a 5-Step Operational Workflow:
- Establish Context: Define the system's role, data sources, users, and affected stakeholders using Section 0 of the Issues Identification Checklist.
- Identify Ethics Issues and Map them under ALTAI Requirements: Systematically evaluate risks across problem formulation, data engineering, model development, deployment, and monitoring using Sections 1–7 of the Issues Identification Checklist.
- Propose & Prioritise Practical Measures: Browse, assess, adapt, and combine technical measures (for developers) and organizational measures (for management) from the Portfolio of Measures.
- Identify Ethics Tensions: Analyze and navigate unavoidable trade-offs between competing requirements, such as Privacy vs. Fairness or Technical Robustness vs. Transparency.
- Perform an Ethics Check: Validate findings and assemble auditable, traceable documentation of all decisions and residual risks.
Applying the AI Ethics Operationalisation Workflow
This section demonstrates how the AI Ethics Operationalisation Workflow can be applied in practice through a real-world-inspired scenario involving PhiShield, an AI-supported cybersecurity system used in an organisational HR context.
The case study follows Marcus, an employee whose performance in a simulated phishing exercise results in a high susceptibility score with serious consequences for his career. It illustrates how AI-generated assessments can overlook important contextual factors and raise ethical concerns when used to support decisions affecting individuals.
Through this scenario, you will explore how to:
- Establish the context by understanding the AI system, its intended purpose, users and affected stakeholders.
- Identify ethics issues and map them to the relevant ALTAI requirements.
- Propose and prioritise practical measures to address the identified issues, considering both technical and organisational safeguards.
- Identify ethics tensions that may arise when balancing competing requirements, such as fairness, transparency and societal well-being.
The scenario provides an opportunity to move from identifying ethical requirements to applying them systematically in a specific AI application context. It also highlights the importance of considering the wider circumstances in which AI-generated assessments are produced and used.
As you follow the steps, consider how the workflow can help organisations make more responsible and context-sensitive decisions about AI systems.
Hotspot AI Ethics Operationalisation Workflow
AI Ethics Operationalisation Workflow
Sort the steps
Arrange the steps of the AI Ethics Operationalisation Workflow in the right order.
What is this about?
This module introduces the AI Ethics Operationalisation Workflow as the central operational framework of the AIOLIA training programme. It translates high-level AI ethics principles into a structured, step-by-step workflow that supports the practical assessment, identification, and mitigation of ethics issues in AI systems across their lifecycle.
Building on three core building blocks, ethics requirements, practical measures, and ethics tensions, the module demonstrates how the seven ALTAI requirements can be operationalised in real-world contexts. Through a practical use case scenario, learners explore a 5-step iterative process that forms the operational backbone of the AIOLIA methodology.
