ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications
   04/24/2019
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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. 19 ID: Use Case Name:  AI to understand adulteration in commonly used food items 
Application
Domain
Wellness 
Deployment
Model
StatusInitial validation done 
ScopeUnderstand the patterns in hyperspectral / NIR or visual imaging specifically for adulteration in milk, banana and mangoes 
Objective(s)To device a simple , cost effective tool to identify the adulteration in food items at point of purchase 
Short
Description
(up to
150 words)
Food adulteration is one of the big evil of modern society. Hyperspectral technology was evaluated to find out adulteration in food items 
Complete Description Food adulteration is becoming menace specially with adulterants that are either carcinogenic or harmful to body parts like kidney. To give few examples, Milk is adulterated with Soda, Urea and detergents. Whereas mangoes and bananas are quickly ripened by calcium carbide and so on. Common man cannot live without these items. There is no frugal way to identify these type of adulterations. Experiment of controlled adulteration was done and hyperspectral reflectance reading were taken. AI helped to find the patterns in hyperspectral signature and was able to reliably classify ( 90% ++) samples that were unadulterated and adulterated.
Stakeholders
Stakeholders'
Assets,Values
Systems'
Threats &
Vulnerabilities
Performance
Indicators (KPIs)
Seq. No. Name Description Reference to mentioned
use case objectives
1  Features related to adulterants in radio spectrum  Intensities around NIR range
AI Features Task(s)Prediction 
Method(s) Machine learning 
HardwareHyperspectral camera
Topology
Terms &
Concepts Used
Machine Learning 
Standardization
Opportunities
Requirements
Challenges
& Issues
 Challenges: Large scale data collection, Miniaturization of frugal NIR / Hyperspectral senso 
Societal Concerns Description Adulterated milk is hazard for children, many aliments including cancer / kidney failures due to consumption of adulterated food. If the AI system is rolled out and taken as reliable then it should be able to perform in all cases and scenarios.
SDGs to
be achieved
Data Characteristics
Description Hyperspectral signatures ( 300 nm to 1300 nm @ 30 nm band) 
Source Hyperspectral camera 
Type
Volume (size) about 500 samples 
Velocity
Variety
Variability
(rate of change)
Quality
Scenario Conditions
No. Scenario
Name
Scenario
Description
Triggering Event Pre-condition Post-Condition






Scenario Name Training
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Specification of training data
Scenario Name Evaluation
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Input of Evaluation
Output of Evaluation
Scenario Name Execution
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Input of Execution
Output of Execution
Scenario Name Retraining
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Specification of retraining data
References
No. Type Reference Status Impact of
use case
Originator
Organization
Link
1 Conference
Proceedings
Published in SPIE Proceedings Vol. 9860: Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2016 David P. Bannon, Editor(s)  Tata Consultancy Services Limited  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