Towards Designing Computer Vision-based Explainable-AI Solution: A Use Case of Livestock Mart Industry

TL;DR

This paper discusses developing an explainable AI system for livestock pricing in online markets, aiming to improve transparency and trust by revealing key animal features influencing prices.

Contribution

It introduces a novel approach to integrating explainable AI techniques into a video analytics platform for livestock market pricing.

Findings

Initial prototype of a smart video analytic platform

Enhanced understanding of animal features affecting pricing

Improved transparency in AI-driven livestock valuation

Abstract

The objective of an online Mart is to match buyers and sellers, to weigh animals and to oversee their sale. A reliable pricing method can be developed by ML models that can read through historical sales data. However, when AI models suggest or recommend a price, that in itself does not reveal too much (i.e., it acts like a black box) about the qualities and the abilities of an animal. An interested buyer would like to know more about the salient features of an animal before making the right choice based on his requirements. A model capable of explaining the different factors that impact the price point is essential for the needs of the market. It can also inspire confidence in buyers and sellers about the price point offered. To achieve these objectives, we have been working with the team at MartEye, a startup based in Portershed in Galway City, Ireland. Through this paper, we report our work-in-progress research towards building a smart video analytic platform, leveraging Explainable AI techniques.