Page 38 - AI Ready – Analysis Towards a Standardized Readiness Framework

AI Ready – Analysis Towards a Standardized Readiness Framework

Table 1: Characteristics of the AI Readiness factors (continued)

AI Readiness Characteristics Notes/Description
factor
Number of datasets cited in AI-specific research (Estimation algorithms on
application research controls such as fertilizers and pesticides).

Number of papers citing The number of papers that use and cite data
the data repositories.

Startup innovations The number of innovations (including papers,
essential patents, and other types of publica-
tions) from startups.

Participation statistics in ITU The number of registrations and attendances
webinars and views related to ITU technology webinars
(e.g. Discovery Series).
Number of standard docu- study group documents, focus group docu-
ments ments citing datasets, models, and AI/
ML-based architectures.
Number of reviewers, anno- The number of reviewers for standards, related
tators, simulators datasets, and annotators involved in analysing
the standards-related datasets. The number
of simulators generating data related to the
standards.
Number of ITU contribu- Local involvement and contextualization.
tions and regional use cases
Number of study groups, Including reviewers in ITU journal and Kalei-
focus group editors doscope; Expert intervention on specific fields
Stakehold- of knowledge related to the use cases under
ers buy-in study.
enabled by
Standards Number of plugfests, and Number of compliance specifications, compli-
interoperability test events. ance test reports would reflect the level of
Interoperability (Alternatives for technology,
interaction with users and other stakeholders,
data format, and coordination).
Number of focus areas from Overlapping focus areas from National regu-
national regulatory bodies lations and laws, related to the standards,
related to AI/ML datasets, and other readiness factors.

Number of documents from Number of regional and national domain-spe-
national standards bodies cific Standards.
which refer to AI/ML

Number of SDGs impacted Level of impact of the use case.
by the use case
Number and level of fund- Level of funding decision-making and invest-
ing, and subsidy model ment – e.g. who deploys the sensors? Are there
subsidies?

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