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Interpretability to Enhance Transparency in Computer Vision System
Subject area: Science,Engineering and Technology · Area of research: Computer Vision
Abstract
The rise of AI adoption in diverse fields has raised concerns about the transparency and accountability of decision-making processes. Computer vision companies face challenges in data transparency, bias mitigation, traceability, and AI decision explication. This research addresses these issues by investigating the implementation of explainability in computer vision systems. This study aims to provide valuable insights for improving the understanding and trustworthiness of AI models in computer vision applications by emphasizing explainability. The findings potentially foster wider acceptance of AI solutions across various industries.
Keywords
Computer Vision, Explainability, Transparency
How to cite this paper
@article{1704914,
author = {Nevindra Ibnazhifi, Mufid Nilmada},
title = {Interpretability to Enhance Transparency in Computer Vision System},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {7},
number = {1},
pages = {541-545},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/17049141.pdf},
abstract = {The rise of AI adoption in diverse fields has raised concerns about the transparency and accountability of decision-making processes. Computer vision companies face challenges in data transparency, bias mitigation, traceability, and AI decision explication. This research addresses these issues by investigating the implementation of explainability in computer vision systems. This study aims to provide valuable insights for improving the understanding and trustworthiness of AI models in computer vision applications by emphasizing explainability. The findings potentially foster wider acceptance of AI solutions across various industries.},
keywords = {Computer Vision, Explainability, Transparency},
month = {July},
}