Module C: Ethics Issues Identification
ModulesModule C: Ethics Issues Identification
Module C introduces the AI Ethics Issues Identification process as the practical evaluation framework of the AIOLIA training programme. It translates the seven ALTAI requirements into an actionable, checklist-based methodology that enables learners to systematically detect, evaluate, and document ethical risks across real-world AI applications.
Building upon the core operational workflow established in Module B, this module anchors abstract ethics principles to concrete evaluation steps. Through guided theory, illustrative vignettes, and applied use case scenarios, it trains learners to move beyond passive awareness and rigorously analyze how complex issues—such as automation bias, proxy discrimination, data privacy erosion, black-box opacity, and loss of human agency—manifest across diverse operational contexts.
Learning Goals
- Master the 7 Assessment List for Trustworthy Artificial Intelligence (ALTAI) requirements and their core ethical dimensions.
- Apply Section 0 of the AIOLIA Issues Identification Checklist to define system scope, inputs/outputs, and affected stakeholder groups.
- Systematically evaluate AI systems across Sections 1–7 of the checklist using structured Likert-scale assessments and narrative scenario criteria.
- Distinguish between technical, organizational, and societal root causes of ethical risks within real-world deployments.
- Formulate clear, auditable issue statements to document identified risks, creating a reliable foundation for downstream mitigation strategies.
What is this about?
Why is this important?
Introduction to ALTAI Requirements
The seven Assessment List for Trustworthy Artificial Intelligence (ALTAI) requirements, established by the European Commission’s High-Level Expert Group on AI, define the core technical, organizational, and ethical standards for human-centric AI systems. The framework establishes that trustworthy AI must be lawful, ethical, and robust across its operational lifecycle. The foundational requirements begin with Human Agency and Oversight, which mandates that systems support human autonomy rather than replacing critical decision-making, guarding against challenges like automation bias. Technical Robustness and Safety requires systems to maintain accuracy, resilience against adversarial attacks, and reliable fallback mechanisms. Privacy and Data Governance enforces data protection standards, ensuring full control over personal data, lawful processing, and proper consent mechanisms.
Building upon governance and system integrity, Transparency requires clear explainability of model outputs, traceability of algorithmic decisions, and honest communication about system capabilities and limitations. Diversity, Non-Discrimination, and Fairness targets algorithmic bias and proxy discrimination, requiring representative training datasets and equitable outcomes across protected demographic groups.
At a broader level, Societal and Environmental Well-Being evaluates the systemic impacts of AI deployment, including psychological impacts, democratic discourse, and the ecological footprint of model training and inference. Finally, Accountability establishes mechanisms for auditability, risk assessment, and clear human oversight to ensure legal and moral responsibility when harms or errors occur.
Ethics Issues Identification
The practical identification of AI ethics issues requires applying the seven Assessment List for Trustworthy Artificial Intelligence (ALTAI) requirements to concrete operational settings using the AIOLIA Issues Identification Checklist. The evaluation process begins by Establishing Operational Context (Section 0), where practitioners define system parameters—including input/output specifications, user profiles, decision autonomy, and affected stakeholder groups—before assessing risk. Grounding risk identification in specific operational contexts prevents vague, generic evaluations and ensures that downstream risk assessments reflect actual deployment realities.
Across Sections 1–7 of the checklist, practitioners systematically evaluate real-world scenarios to detect failure modes and ethical vulnerabilities. Evaluated risks include subtle loss of student agency caused by over-reliance on adaptive AI tutoring prompts, severe safety hazards stemming from data poisoning in industrial monitoring tools, and privacy violations arising from unverified training data in organizational assistants. The evaluation further examines black-box opacity in automated risk scoring platforms, algorithmic bias and proxy discrimination in content moderation systems, skills degradation among domain experts, and unclear legal liability during clinical decision-support failures.
To complete the identification process, qualitative observations and Likert-scale evaluations are synthesized into Structured, Auditable Issue Statements. Each statement explicitly documents the affected ALTAI requirement, the precise operational context, the root cause of the vulnerability, and the potential severity of harm. This standardized documentation creates a clear audit trail, establishing the necessary empirical foundation for selecting targeted technical and organizational mitigation measures.
