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Trustworthy AI Systems : Engineering Secure, Scalable, and Responsible Intelligence for Real Applications / edited by Vaishnavi Gudur, Bishwajeet Pandey, Advait Patel.
- Format:
- Book
- Author/Creator:
- Gudur, Vaishnavi.
- Series:
- Engineering Series
- Language:
- English
- Subjects (All):
- Electronic circuit design.
- Machine learning.
- Engineering--Data processing.
- Engineering.
- Electronics Design and Verification.
- Machine Learning.
- Data Engineering.
- Local Subjects:
- Electronics Design and Verification.
- Machine Learning.
- Data Engineering.
- Physical Description:
- 1 online resource (359 pages)
- Edition:
- 1st ed. 2026.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2026.
- Summary:
- This book bridges the gap between leading-edge AI innovation and real deployment, by offering a practical guide to engineering secure, scalable, and responsible AI. The authors describe a unified framework that merges engineering principles with ethical design, cybersecurity, explainability, and policy alignment. Through expert insights, case studies, and technical guidance, the book empowers researchers, developers, and decision-makers to build AI that users can trust. Provides an interdisciplinary reference on AI security, interpretability, ethics, governance, and reliability Uniquely combines hands-on engineering strategies with ethical and governance considerations Enables readers to understand the full lifecycle of trustworthy AI, from model design to post-deployment monitoring.
- Contents:
- Introduction to Trustworthy AI
- Ethical Principles and Global Guidelines
- AI Governance and Risk Management Frameworks
- Security in AI Systems
- Explainable AI: Tools and Techniques
- Robustness and Reliability in Machine Learning
- Bias Detection and Fairness Evaluation
- Responsible Data Engineering
- Trust and Safety in Financial AI Systems
- AI for National Security and Defense
- Autonomous Vehicles and Embedded Systems
- Designing for Human-Centered AI
- Regulatory Compliance and Auditability
- Scaling Trustworthy AI in Startups and Enterprises
- Open Source, Community-Driven Best Practices
- The Future of Trustworthy AI: Trends and Predictions.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 3-032-15606-8
- 9783032156068
- OCLC:
- 1574807672
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