Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Enhancing The Explainability of Neural Network

Enhancing The Explainability of Neural Network

Arulprakash, Enoch
Aruldoss, Martin

Paperback

Business General

ISBN10: 6208064341
ISBN13: 9786208064341
Publisher: LAP Lambert Academic Publishing
Published: Sep 30 2024
Pages: 204
Weight: 0.67
Height: 0.47 Width: 6.00 Depth: 9.00
Language: English
Artificial Intelligence (AI) driven by neural networks is crucial in many applications like recommendation systems, language translation, social media, chatbots, and spell-checking etc. However, these networks are often criticized for being black boxes, raising concerns about their explainability, especially in sensitive domains like healthcare, autonomous driving etc. Existing methods to enhance explainability, such as feature importance, often lack clarity and interpretability.To address this, the Object-Oriented Neural Network for Improved Explainability (OONNIE) was developed. OONNIE uses object-oriented modeling to combine loss and connection weight for computing feature importance and integrates domain-specific rules through OOP's extendability. The model emphasizes algorithmic transparency by detailing every training step. Evaluated on XOR and XNOR functions, OONNIE shows promising results in feature importance, faster loss reduction, and improved predictions after integrating domain rules. This marks a significant contribution to explainable AI, making OONNIE a valuable tool for developing trustworthy AI systems.

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