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
Citations
IRE Journals:
Nevindra Ibnazhifi , Mufid Nilmada
"Interpretability to Enhance Transparency in Computer Vision System" Iconic Research And Engineering Journals Volume 7 Issue 1 2023 Page 541-545
IEEE:
Nevindra Ibnazhifi , Mufid Nilmada
"Interpretability to Enhance Transparency in Computer Vision System" Iconic Research And Engineering Journals, 7(1)
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IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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