Page 11 - AI Ready – Analysis Towards a Standardized Readiness Framework

AI Ready – Analysis Towards a Standardized Readiness Framework

2 Introduction

In this cross-domain study, we analyzed use cases related to the use of AI in different verticals
such as traffic safety, health, agriculture, disaster management, accessibility, public services, etc
with an aim to find patterns in applications of AI in different scenarios. The goal was to derive a
standardized data analysis method and metric that could be applied to measure the readiness
to use AI for solving relevant problems in these use cases. Our analysis of the use cases included
the following characteristics of use cases to be considered while evaluating AI readiness: The
data used in each use case, domain-specific research needed in the use case, deployment with
infrastructure requirements, human factors supported by standards, experimentation capability
via a sandbox, and ecosystem creation using opensource. These characteristics are analyzed in
“Table 2 – General use case analysis and AI impacts” in Appendix A.
The main AI readiness factors identified in this report are:

1) Availability of open data

The Kingdom of Saudi Arabia set up an Open Data Platform [3] providing datasets to the
public to enhance access to information, collaboration, and innovation. The major areas of
dataset availability in this open data platform are Health, Agriculture and Fishing, Education
and Training, Social Services, and Transport and Communications. The transportation system in
the major cities enables advanced use cases such as tracking vehicles with excessive speed to
guarantee pedestrian safety, providing the best driving routes to reduce the number of traffic
jams, and reducing the mortality rate caused by collision. These use cases utilize diverse data
such as imagery data collected by Closed circuit television (CCTV), a detailed map of the city,
traffic signal information, and vehicle Global Positioning System (GPS) details. This is a prime
example of the collection and hosting of open data and enabling analytics for traffic safety [28]
[19][44].

Open data enables private entrepreneurs, startups, and industries to develop applications or
design algorithms to achieve Sustainable Development Goals (SDGs) such as safe transportation.
However, there are still challenges in data collection, cleaning, and preprocessing which hinder
the opening of data for everyone. A well-designed open data strategy would make sure high-
quality data is available for scholars, developers, and analysts to design solutions based on
real-world problems, thus enhancing the impact of AI on society.
2) Access to Research

The equal importance of domain-specific research and the application of advanced AI models
in predicting with accuracy is brought out by examples such as predicting intoxication levels
and modeling safe driving. Analysis of biological and medical data using domain-specific, and
AI-specific research is important for the use case [8] [10].

For example, while assessing the safe driving behaviors under the influence (see Clause 4.2.2),
not only monitoring of driver behavior was considered, but even biological data such as chest
movement and breath were collected. Chest movement was collected, and analyzed, and the
predicted heartbeat would serve as reference data for mapping the blood alcohol level.

A prime example of a collaborative initiative is the “AI for Road Safety" [4] launched by ITU,
the UN Secretary-General's Special Envoy for Road Safety, and the UN Envoy on Technology.
This initiative promotes an AI-enhanced “safe system" approach to reduce fatalities based on

4