NIST.AI.100 1 · Table (p.32)

0	1
NIST AI 100-1	AI RMF 1.0
	Table 2: Categories and subcategories for the MAP function. (Continued)
Categories	Subcategories
	MAP 2.3: Scientific integrity and TEVV considerations are iden-
	tified and documented, including those related to experimental
	design, data collection and selection (e.g., availability, repre-
	sentativeness, suitability), system trustworthiness, and construct
	validation.
MAP 3: AI	MAP 3.1: Potential benefits of intended AI system functionality
capabilities, targeted	and performance are examined and documented.
usage, goals, and
	MAP 3.2: Potential costs, including non-monetary costs, which
expected benefits
	result from expected or realized AI errors or system functionality
and costs compared
	and trustworthiness – as connected to organizational risk toler-
with appropriate
	ance – are examined and documented.
benchmarks are
	MAP 3.3: Targeted application scope is specified and docu-
understood.
	mented based on the system’s capability, established context, and
	AI system categorization.
	MAP 3.4: Processes for operator and practitioner proficiency
	with AI system performance and trustworthiness – and relevant
	technical standards and certifications – are defined, assessed, and
	documented.
	MAP 3.5: Processes for human oversight are defined, assessed,
	and documented in accordance with organizational policies from
	the GOVERN function.
MAP 4: Risks and	MAP 4.1: Approaches for mapping AI technology and legal risks
benefits are mapped	of its components – including the use of third-party data or soft-
for all components	ware – are in place, followed, and documented, as are risks of in-
of the AI system	fringement of a third party’s intellectual property or other rights.
including third-party
	MAP 4.2: Internal risk controls for components of the AI sys-
software and data.
	tem, including third-party AI technologies, are identified and
	documented.
MAP 5: Impacts to	MAP 5.1: Likelihood and magnitude of each identified impact
individuals, groups,	(both potentially beneficial and harmful) based on expected use,
communities,	past uses of AI systems in similar contexts, public incident re-
organizations, and	ports, feedback from those external to the team that developed
society are	or deployed the AI system, or other data are identified and
characterized.	documented.

Source

NIST.AI.100-1.pdf

Domain

pdfs_from_pdf_links

Type

pdf

Method

camelot_stream

Dimensions

46 × 2

Page

32