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Governance frameworks, formal policies, and strategic alignment mechanisms.
Also in Oversight & Accountability
Reasoning
Defines scope and governance framework for ethics assessment activities within organization.
Define governance
Assemble an Ethics Board, with roles, responsibilities, system objectives, and accountability structures defined within the first few months of project initiation.
2.1.2 Roles & AccountabilityIdentify potential incidents
Analyze current literature to identify potential incidents, and related proposed mitigation measures for the specific use case.
2.2.1 Risk AssessmentDefine review process:
Establish regular review mechanism for ethics clearance by process of multi-stakeholder collaboration including ethics advisors.
2.1.3 Policies & ProceduresDefine scope
Select AI system features that must undergo ethical screening by the Ethics Board
2.1.3 Policies & Procedures“Data Fairness defined“
2.2 Risk & Assurance“Data Fairness defined“ > Identify at-risk-groups
Identify at-risk groups which may be systematically disadvantaged by the AI system
2.2.1 Risk Assessment“Data Fairness defined“ > Define fairness
Select fairness objectives and associated fairness metrics to measure consequences of biased or unfair data on model outputs with respect to harms and benefits which (at-risk) individuals may receive by use of the AI system.
2.2.1 Risk Assessment“Data Fairness defined“ > Implement bias detection
Implement bias detection processes throughout the AI lifecycle at defined bias checkpoints based on selected fairness metrics. Definition of processes for mitigation of bias present in data or outputs that impact fairness of the AI system.
2.2 Risk & Assurance“Data Fairness defined“ > Implement fairness audit
Define regular screening and data audits to ensure compliance with fairness data guidelines and ethics principles.
2.2.3 Auditing & ComplianceModel Materiality classified“
2.2.1 Risk AssessmentTowards Trusted AI: A Blueprint for Ethics Assessment in Practice
Wirth, Christoph Tobias; Maftei, Mihai; Martín-Peña, Rosa Esther; Merget, Iris (2025)
The development of AI technologies leaves place for unforeseen ethical challenges. Issues such as bias, lack of transparency and data privacy must be addressed during the design, development, and the deployment stages throughout the lifecycle of AI systems to mitigate their impact on users. Consequently, ensuring that such systems are responsibly built has become a priority for researchers and developers from both public and private sector. As a proposed solution, this paper presents a blueprint for AI ethics assessment. The blueprint provides for AI use cases an adaptable approach which is agnostic to ethics guidelines, regulatory environments, business models, and industry sectors. The blueprint offers an outcomes library of key performance indicators (KPIs) which are guided by a mapping of ethics framework measures to processes and phases defined by the blueprint. The main objectives of the blueprint are to provide an operationalizable process for the responsible development of ethical AI systems, and to enhance public trust needed for broad adoption of trusted AI solutions. In an initial pilot the blueprinted for AI ethics assessment is applied to a use case of generative AI in education. ¬© Christoph Tobias Wirth, Mihai Maftei, Rosa Esther Martín-Peña, and Iris Merget.
Other (outside lifecycle)
Outside the standard AI system lifecycle
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Govern
Policies, processes, and accountability structures for AI risk management
Primary
6.5 Governance failure