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Practices for assessing AI systems, including testing, red teaming, risk assessment, auditing, and compliance verification.
Also in Organisation
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.
Reasoning
Implements bias detection checkpoints to identify and characterize fairness risks throughout AI lifecycle.
“Data Fairness defined“
Scope and Governance of Ethics Assessment defined
2.1.3 Policies & ProceduresScope and Governance of Ethics Assessment defined > 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 & AccountabilityScope and Governance of Ethics Assessment defined > Identify potential incidents
Analyze current literature to identify potential incidents, and related proposed mitigation measures for the specific use case.
2.2.1 Risk AssessmentScope and Governance of Ethics Assessment defined > Define review process:
Establish regular review mechanism for ethics clearance by process of multi-stakeholder collaboration including ethics advisors.
2.1.3 Policies & ProceduresScope and Governance of Ethics Assessment defined > Define 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 & AssuranceTowards 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 (multiple stages)
Applies across multiple lifecycle stages
Developer
Entity that creates, trains, or modifies the AI system
Measure
Quantifying, testing, and monitoring identified AI risks
