Artificial Intelligence Risk Management Framework (AI RMF 1.0)
Appendix A: > Descriptions of AI Actor Tasks from Figures 2 and 3

Descriptions of AI Actor Tasks from Figures 2 and 3

AI Design tasks are performed during the Application Context and Data and Input phases
of the AI lifecycle in Figure 2. AI Design actors create the concept and objectives of AI
systems and are responsible for the planning, design, and data collection and processing
tasks of the AI system so that the AI system is lawful and fit-for-purpose. Tasks include ar-
ticulating and documenting the system's concept and objectives, underlying assumptions,
context, and requirements; gathering and cleaning data; and documenting the metadata
and characteristics of the dataset. AI actors in this category include data scientists, do-
main experts, socio-cultural analysts, experts in the field of diversity, equity, inclusion,
and accessibility, members of impacted communities, human factors experts (e.g., UX/UI
design), governance experts, data engineers, data providers, system funders, product man-
agers, third-party entities, evaluators, and legal and privacy governance.
AI Development tasks are performed during the AI Model phase of the lifecycle in Figure
2. AI Development actors provide the initial infrastructure of AI systems and are responsi-
ble for model building and interpretation tasks, which involve the creation, selection, cali-
bration, training, and/or testing of models or algorithms. AI actors in this category include
machine learning experts, data scientists, developers, third-party entities, legal and privacy
governance experts, and experts in the socio-cultural and contextual factors associated with
the deployment setting.
AI Deployment tasks are performed during the Task and Output phase of the lifecycle in
Figure 2. AI Deployment actors are responsible for contextual decisions relating to how
the AI system is used to assure deployment of the system into production. Related tasks
include piloting the system, checking compatibility with legacy systems, ensuring regu-
latory compliance, managing organizational change, and evaluating user experience. AI
actors in this category include system integrators, software developers, end users, oper-
ators and practitioners, evaluators, and domain experts with expertise in human factors,
socio-cultural analysis, and governance.
Operation and Monitoring tasks are performed in the Application Context/Operate and
Monitor phase of the lifecycle in Figure 2. These tasks are carried out by AI actors who are
responsible for operating the AI system and working with others to regularly assess system
output and impacts. AI actors in this category include system operators, domain experts, AI
designers, users who interpret or incorporate the output of AI systems, product developers,
evaluators and auditors, compliance experts, organizational management, and members of
the research community.
Test, Evaluation, Verification, and Validation (TEVV) tasks are performed throughout
the AI lifecycle. They are carried out by AI actors who examine the AI system or its
components, or detect and remediate problems. Ideally, AI actors carrying out verification
and validation tasks are distinct from those who perform test and evaluation actions. Tasks
can be incorporated into a phase as early as design, where tests are planned in accordance
with the design requirement.
• TEVV tasks for design, planning, and data may center on internal and external vali-
dation of assumptions for system design, data collection, and measurements relative
to the intended context of deployment or application.
• TEVV tasks for development (i.e., model building) include model validation and
assessment.
• TEVV tasks for deployment include system validation and integration in production,
with testing, and recalibration for systems and process integration, user experience,
and compliance with existing legal, regulatory, and ethical specifications.
• TEVV tasks for operations involve ongoing monitoring for periodic updates, testing,
and subject matter expert (SME) recalibration of models, the tracking of incidents
or errors reported and their management, the detection of emergent properties and
related impacts, and processes for redress and response.
Human Factors tasks and activities are found throughout the dimensions of the AI life-
cycle. They include human-centered design practices and methodologies, promoting the
active involvement of end users and other interested parties and relevant AI actors, incor-
porating context-specific norms and values in system design, evaluating and adapting end
user experiences, and broad integration of humans and human dynamics in all phases of the
AI lifecycle. Human factors professionals provide multidisciplinary skills and perspectives
to understand context of use, inform interdisciplinary and demographic diversity, engage
in consultative processes, design and evaluate user experience, perform human-centered
evaluation and testing, and inform impact assessments.
Domain Expert tasks involve input from multidisciplinary practitioners or scholars who
provide knowledge or expertise in - and about - an industry sector, economic sector, con-
text, or application area where an AI system is being used. AI actors who are domain
experts can provide essential guidance for AI system design and development, and inter-
pret outputs in support of work performed by TEVV and AI impact assessment teams.
AI Impact Assessment tasks include assessing and evaluating requirements for AI system
accountability, combating harmful bias, examining impacts of AI systems, product safety,
liability, and security, among others. AI actors such as impact assessors and evaluators
provide technical, human factor, socio-cultural, and legal expertise.
Procurement tasks are conducted by AI actors with financial, legal, or policy management
authority for acquisition of AI models, products, or services from a third-party developer,
vendor, or contractor.
Governance and Oversight tasks are assumed by AI actors with management, fiduciary,
and legal authority and responsibility for the organization in which an AI system is de-
signed, developed, and/or deployed. Key AI actors responsible for AI governance include
organizational management, senior leadership, and the Board of Directors. These actors
are parties that are concerned with the impact and sustainability of the organization as a
whole.