Assessing Trustworthiness of AI Systems using Z-Inspection®
Santa Clara, California, USA
May 5-7, 2025
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Tutorial overview
Assessing the trustworthiness of AI systems has become essential in today's rapidly evolving technological landscape. Despite this necessity, practical evaluation frameworks that address the entire AI lifecycle remain limited. The Z-Inspection® methodology bridges this gap, offering a comprehensive and actionable framework for ethical AI assessment across diverse sectors, including business, healthcare, and the public sector.
Rooted in applied ethics, Z-Inspection® adheres to the European Commission's guidelines on Trustworthy AI, emphasizing four foundational principles: respect for human autonomy, prevention of harm, fairness, and explicability. As a globally recognized initiative, it has catalyzed the adoption of ethical AI practices and is listed in the OECD Catalogue of AI Tools & Metrics, underscoring its significance in the field.
This tutorial introduces participants to the Z-Inspection® framework through engaging, hands-on exercises based on real-world scenarios. Designed for students and professionals involved in the development, deployment, or use of AI systems, the session provides practical skills for evaluating the ethical dimensions of AI. Upon completion, attendees will receive a Z-Inspection® certificate, granting access to an exclusive professional network and ongoing initiatives in ethical AI.
3. TUTORIAL FORMAT/TIMELINE
☐ 4 hours
4. TARGET AUDIENCE
Target Audience
This tutorial is designed for:
Students in AI-related fields
Professionals developing, deploying, or using AI tools and systems
Users of AI systems seeking to understand ethical implications
Z-Inspection® is currently taught as a master's level course. The full lecture series, "Ethical Implications of AI," is available online.
Preparation
To make the most of this tutorial, participants can:
Review the basics of Z-Inspection®, which will be summarized during the session.
Watch selected videos from the lecture series.
Recommended Videos
For additional context, we recommend the following YouTube presentations:
How to Assess Trustworthy AI in Practice https://www.youtube.com/watch?v=uP02B3KsX9E
· EU Ethics Guidelines for Trustworthy AI
· Part I: https://youtu.be/ACKFKOccipE
· Part II: https://youtu.be/H1dvXImYtWY
Further Resources
For more comprehensive information on Z-Inspection®, including teaching certification details, please visit:
Z-Inspection® in a nutshell
About the teachers:
PRESENTER(S)
PRESENTER #1
Dr. Jesmin Jahan Tithi, AI Research Scientist/Engineer, Intel Labs
Shraban03@gmail.com, jesmin.jahan.tithi@intel.com
Bio of Dr. Jesmin Jahan Tithi
Dr. Jesmin Jahan Tithi is an AI Research Scientist/Engineer at Intel Corporation, where she
focuses on high-performance computing (HPC) and hardware-software codesign. She received her Ph.D. in Computer Science from Stony Brook University, New York, and her B.Sc. in Computer Science and Engineering from Bangladesh University of Engineering and Technology with Honors. She has also interned at Google, Intel, and the Pacific Northwest National Laboratory. Dr. Tithi is a leading expert in HPC and has made significant contributions to the field. She is the author of over 35 peer-reviewed publications and 13 approved patents, and her work has been featured in top academic conferences and journals. She is a founding member of Z-Inspection®, a certified Z-Inspection® teacher and the head of education in North America for Z-Inspection® which is an assessment process for trustworthy and ethical AI.
Google Scholar: https://scholar.google.com/citations?user=ySqvmSQAAAAJ&hl=en
LinkedIn: https://www.linkedin.com/in/jesmin-jahan-tithi/
PRESENTER #2
Partha Deka, Senior Staff Engineer, Intel Corporation
partha.pritamdeka@gmail.com, partha.deka@intel.com
Bio of Partha Pritam Deka
Partha Deka is a seasoned Data Science Leader with over 15 years of experience in semiconductor supply chain and manufacturing. As a Senior Staff Engineer at Intel Corporation, Partha has led high-impact teams developing AI and machine learning solutions, resulting in substantial cost savings and process optimizations. His notable work includes creating a computer vision system that greatly improved logistics efficiency at Intel, earning his team a finalist spot for the CSCMP Innovation Award.
Previously, Partha made key contributions at General Electric, where he utilized machine learning to solve complex industrial challenges. He filed several patents, including those on delivery status diagnosis and data throttling, which collectively garnered over 30 citations.
A recognized thought leader in the AI community, Partha is a Senior IEEE Member, a published author, and a frequent speaker at industry conferences. He co-authored "XGBoost for Regression Predictive Modeling and Time Series Analysis," providing comprehensive insights into XGBoost's advanced applications. He also serves as a reviewer for the NeurIPS conference, further contributing to the advancement of AI research.
Partha is actively involved with the Z-Inspection™ initiative, ensuring ethical AI assessment and promoting trustworthy AI systems. His expertise continues to shape semiconductor manufacturing through the application of advanced analytics and AI-driven innovation.
LinkedIn: https://www.linkedin.com/in/parthapritamdeka
Google Scholar: https://scholar.google.com/citations?user=qO_1wJgAAAAJ&hl=env
Book Press release: https://www.odbms.org/2024/09/on-xgboost-for-regression-predictive-modeling-and-time-series-analysis-qa-with-partha-deka/
Resources
Help students and parents help themselves by making resources easily accessible.
Student sites
Papers with summary of lessons learned:
Lessons Learned from Assessing Trustworthy AI in Practice | Digital Society
YouTube talks that can be watched:
EU Ethics Guidelines for Trustworthy AI. Part I. https://youtu.be/ACKFKOccipE
EU Ethics Guidelines for Trustworthy AI. Part II. https://youtu.be/H1dvXImYtWY
More details: https://z-inspection.org/ethical-implication-of-ai-assessing-trustworthy-ai-in-practice-snu-spring-2024/
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