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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Explainable Artificial Intelligence |
| Authors Name: | Pawar Naitik Ashok , Mule Mugdha Prabhakar , Mukhmale Sanika Gajanan , Saikumar Anil Madel |
| Download E-Certificate: | Download |
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IJNRD_213104
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| Published Paper Id: | IJNRD2402034 |
| Published In: | Volume 9 Issue 2, February-2024 |
| DOI: | |
| Abstract: | Explainable artificial intelligence is often discussed in relation to deep learning and plays an important role in the FAT -- fairness, accountability and transparency -- ML model. XAI is useful for organizations that want to build trust when implementing an AI. XAI can help them understand an AI model's behavior, helping to find potential issues such as AI biases. XAI counters the "black box" tendency of machine learning, where even the AI's designers cannot explain why it arrived at a specific decision. XAI helps human users understand the reasoning behind AI and machine learning (ML) algorithms to increase their trust. Machine learning (ML) algorithms used in AI can be categorized as "white-box" or "black-box". White-box models provide results that are understandable to experts in the domain. Black-box models, on the other hand, are extremely hard to explain and can hardly be understood even by domain experts. XAI algorithms follow the three principles of transparency, interpretability, and explainability. |
| Keywords: | Black-bok Explainable Artificial Intelligence (XAI) Machine learning (ML) artificial intelligence (AI) CML arXiv LIME SHAP Fairness Bias Mitigation Causality Ethical AI Local/global Explanations |
| Cite Article: | "Explainable Artificial Intelligence", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 2, page no.a282-a307, February-2024, Available :http://www.ijnrd.org/papers/IJNRD2402034.pdf |
| Downloads: | 000118760 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2402034 Registration ID: 213104 Published In: Volume 9 Issue 2, February-2024 DOI (Digital Object Identifier): Page No: a282-a307 Country: pune, maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2402034 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2402034 |
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ISSN: 2456-4184
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