ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications 09/26/2020
Editor's comments and enhancements are shown in green. [✓ Reviewed]
The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based on 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.)
 Functional aspects, trustworthiness, and societal concerns
 AI life cycle components include acquire/process/apply.
These characteristics are identified in red in the use case.
Use Case Name:
AI to understand adulteration in commonly used food items
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
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.
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.
Peer-reviewed scientific/technical publications on AI applications (e.g. ).
Patent documents describing AI solutions (e.g. , ).
Technical reports or presentations by renowned AI experts (e.g. )
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:
 B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.
 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.
 M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.
 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