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Course
Machine Learning
Description
This presentation, "Machine Learning Applications and Sustainable Development," explores the intersection of machine learning and its impact on privacy, equity, and societal well-being. It delves into the potential for re-identification of "anonymized" data through various techniques like k-anonymity, L-diversity, and the vulnerabilities of large language models, illustrating these concepts with real-world examples such as the AOL search data, Netflix Prize dataset, and Strava's fitness tracking. The presentation also discusses solutions to enhance data privacy, including differential privacy and the emerging field of machine unlearning, highlighting their applications and limitations. Finally, it addresses the broader implications for civil rights and ethical considerations in AI development, referencing recent initiatives and executive orders aimed at ensuring the safe, secure, and trustworthy deployment of artificial intelligence.
Video Recording: https://www.youtube.com/watch?v=GyYWvyLjfvg&list=PLLsxQYv4DdJkI1SshLhWCdVdBZfn8SrXl&index=7
Publication Date
Fall 11-6-2023
Document Type
Presentation
Keywords
Machine Learning, Sustainable Development, Data Privacy, De-anonymization, Differential Privacy, Machine Unlearning, AI Ethics
Disciplines
Computational Engineering | Computer Engineering | Engineering
Creative Commons License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 License.
Recommended Citation
Pendyala, Vishnu, "Machine Learning Applications and Sustainable Development" (2023). Open Educational Resources. 8.
https://scholarworks.sjsu.edu/oer/8
