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

arXiv 2103.03096 ↗First posted 8 Feb 2021cs.CV cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 10 references that are in the map. First posted on arXiv 8 Feb 2021.

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6 of 10 references.

References · 6

  1. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  2. 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 612
  3. 2018Techniques for Interpretable Machine Learningcited by 237
  4. 2019Towards Explainable Deep Neural Networks (xDNN)cited by 61
  5. 2017Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Mediacited by 9
  6. 2017Building Interoperable and Cross-Domain Semantic Web of Things Applicationscited by 1

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