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Questions and Answers
True or false: XAI teknikleri, yalnızca belirli veri yöntemlerine uyarlanabilir.
True or false: XAI teknikleri, yalnızca belirli veri yöntemlerine uyarlanabilir.
True or false: İyi açıklamalar, insan yorumlanabilirliği ile model sadakati arasında denge sağlamalıdır.
True or false: İyi açıklamalar, insan yorumlanabilirliği ile model sadakati arasında denge sağlamalıdır.
True or false: XAI, otomatik oluşturma ve büyük sayıda parametreleri nedeniyle ML modellerinin anlaşılması zor olduğu için geliştirilmiştir.
True or false: XAI, otomatik oluşturma ve büyük sayıda parametreleri nedeniyle ML modellerinin anlaşılması zor olduğu için geliştirilmiştir.
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Study Notes
- Explainable Artificial Intelligence (XAI) is a field of machine learning focused on developing and analyzing tools to understand ML models.
- ML models are often difficult to understand due to their automatic generation and large number of parameters.
- XAI has seen an increase in techniques proposed for various data modalities due to the rise of ML models in high-risk decision-making.
- Good explanations balance model fidelity with human interpretability and offer meaningful insights.
- Different stakeholders have different perspectives on what constitutes a good explanation, including end-users and regulators.
- XAI techniques must be tailored to the appropriate data method.
- XAI techniques are not a magic solution and must be evaluated in conjunction with human interpretation.
- Human factors are emphasized in XAI, as explanations must be appropriately visualized and interpreted.
- This book provides a comprehensive overview of XAI techniques and their practical application.
- Readers are advised to appreciate the unique strengths of different XAI techniques rather than relying on a single "magic" method.
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