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# Dual Use of AI

📗 Drug discovery: flip the objective function to make optimization find many highly toxic compounds: Link (drug discovery), PDF (toxic chemical).
📗 Image generation: deepfake (fake videos and face images): Wikipedia, Link (this person does not exist), Link (2024 election).
📗 Robotics: Wikipedia, Link (autonomous weapons), Link (UW Robotics).



# AI Ethics

📗 Bias and fairness: Link (fairness and bias), PDF (gender bias), PDF (racial bias).
➩ Collect representative data from minority groups.
➩ Remove bias associations
➩ Add fairness constraints to the optimization problem for learning.
📗 Fake content
📗 Privacy: Link (Netflix de-anonymization attack), Link (social network)
➩ Right to be forgotten (deep networks need to unlearn)
➩ Differential privacy (done by adding noise to dataset)
📗 Adversarial robustness: Link (turtle or rifle), Link (black box attack), Link (LLM attack), Trojan/Backdoor attack.
➩ Training time attack (fake training data)
➩ Test time attack (adversarial training to defend)
📗 Value alignment: Link (value alignment).
📗 Other recommended readings:
➩ Weapons of Math Destruction: Wikipedia
➩ Concrete Problems in AI Safetfy: PDF
➩ On the Dangers of Stochastic Parrots: Link



📗 Notes and code adapted from the course taught by Professors Jerry Zhu, Yudong Chen, Yingyu Liang, and Charles Dyer.
📗 Content from note blocks marked "optional" and content from Wikipedia and other demo links are helpful for understanding the materials, but will not be explicitly tested on the exams.
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Last Updated: August 22, 2025 at 10:06 AM