Cyber & IT Supervisory Forum - Additional Resources

Analyze dependencies between contextual factors and system requirements. List potential impacts that may arise from not fully considering the importance of trustworthiness characteristics in any decision making. Follow responsible design techniques in tasks such as software engineering, product management, and participatory engagement. Some examples for eliciting and documenting stakeholder requirements include product requirement documents (PRDs), user stories, user interaction/user experience (UI/UX) research, systems engineering, ethnography and related field methods. Conduct user research to understand individuals, groups and communities that will be impacted by the AI, their values & context, and the role of systemic and historical biases. Integrate learnings into decisions about data selection and representation. What type of information is accessible on the design, operations, and limitations of the AI system to external stakeholders, including end users, consumers, regulators, and individuals impacted by use of the AI system? To what extent is this information sufficient and appropriate to promote transparency? Promote transparency by enabling external stakeholders to access information on the design, operation, and limitations of the AI system. To what extent has relevant information been disclosed regarding the use of AI systems, such as (a) what the system is for, (b) what it is not for, (c) how it was designed, and (d) what its limitations are? (Documentation and external communication can offer a way for entities to provide transparency.) How will the relevant AI actor(s) address changes in accuracy and precision due to either an adversary’s attempts to disrupt the AI system or unrelated changes in the operational/business environment, which may impact the accuracy of the AI system?

Organizations can document the following: Transparency & Documentation

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