This page is still being polished. If you have thoughts, please share them via the feedback form.
Data on this page is preliminary and may change. Please do not share or cite these figures publicly.
Safety culture, knowledge dissemination, and talent development within the organization.
Also in Engineering & Development
Ensure regular updates of the AI literacy and sustainability training. Adjust training programs based on explaining observed versus expected outcomes, on system improvements, user and stakeholder feedback, and on advancement in state-of-art and energy-efficient technologies. Ensure that employees acquire sufficient knowledge in developing, improving, deploying or using the AI system throughout the entire life cycle.
Reasoning
Training program builds employee AI literacy and knowledge throughout system lifecycle.
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 (outside lifecycle)
Outside the standard AI system lifecycle
Other (multiple actors)
Applies across multiple actor types
Govern
Policies, processes, and accountability structures for AI risk management
