ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications - Input Form (Test)
   July 21, 2019
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Submitter First Name:   Last Name:    Country:    Email:


The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based the application area, relevant AI technologies, credible reference sources (see References section), and the following characteristics:

  • Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
  • Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
  • Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
  • Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
  • Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
No. To be assigned Use Case Name:
Application
Domain
Deployment
Model
Status
Scope
Objective(s)
Short
Description
(up to
150 words)
Complete Description
Stakeholders
Stakeholders'
Assets,Values
Systems'
Threats &
Vulnerabilities
Key
Performance
Indicators (KPIs)

ID Name Description Reference to mentioned
use case objectives
AI Features
Task(s)
Method(s)
Hardware
Topology
Terms &
Concepts Used
Standardization
Opportunities/
Requirements
Challenges
& Issues
Societal Concerns
Description
SDGs to
be achieved
Data Characteristics
Description
Source
Type
Volume (size)
Velocity
Variety
Variability
(rate of change)
Quality
Process Scenario Conditions    
No. Scenario
Name
Scenario
Description
Triggering Event Pre-condition Post-Condition
Training Scenarios     
Specification of Training Data
Evaluation Scenarios     
Input of Evaluation
Output of Evaluation
Execution Scenarios     
Input of Execution
Output of Execution
Retraining Scenarios     
Specification of Retraining Data
References     
No. Type Reference Status Impact of
use case
Originator
Organization
Link

  • Peer-reviewed scientific/technical publications on AI applications (e.g. [1]).
  • Patent documents describing AI solutions (e.g. [2], [3]).
  • Technical reports or presentations by renowned AI experts (e.g. [4])
  • High quality company whitepapers and presentations
  • Publicly accessible sources with sufficient detail

    This list is not exhaustive. Other credible sources may be acceptable as well.

    Examples of credible sources:

    [1] B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.

    [2] S. Hong. "Artificial intelligence audio apparatus and operation method thereof". N US 9,948,764, Available at: https://patents.google.com/patent/US20150120618A1/en. 2018.

    [3] M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.

    [4] J. Hendler, S. Ellis, K. McGuire, N. Negedley, A. Weinstock, M. Klawonn and D. Burns. "WATSON@RPI, Technical Project Review".
    URL: https://www.slideshare.net/jahendler/watson-summer-review82013final. 2013