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Gordon, M. L., Lam, M. S., Park, J. S., Patel, K., Hancock, J., Hashimoto, T., & Bernstein, M. S. (2022, April). Jury learning: Integrating dissenting voices into machine learning models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-19).
Gordon, M. L., Lam, M. S., Park, J. S., Patel, K., Hancock, J., Hashimoto, T., & Bernstein, M. S. (2022, April). Jury learning: Integrating dissenting voices into machine learning models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-19).
(How is a juror instructed to eliminate implicit bias? What would be the specifics of a course that changed their minds? This is fairly easy to trigger in practice, e.g. as subtext to invoke irony.)
#Machine Learning#Legal#Bias#Paper#PDF
·dl.acm.org·
Gordon, M. L., Lam, M. S., Park, J. S., Patel, K., Hancock, J., Hashimoto, T., & Bernstein, M. S. (2022, April). Jury learning: Integrating dissenting voices into machine learning models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-19).
NIST Artificial Intelligence (AI) 100-2 E2023, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations
NIST Artificial Intelligence (AI) 100-2 E2023, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations

First impression: This lit review boiled down to availability, integrity, abuse, and privacy. Poisoning and evasion. The motivation seems to be to formalize attacks so that another AI can respond, e.g. GANs. AVs are sketchy. Deepfake was left out. They already have competing supply chains and sanctions or economic interventions. It does not really get into the yet-to-be-released versions like for robotics. What makes ML different? Or Generative AI? This identifies flaws in existing systems and infrastructure, not what the next would be. Other versions might be evolutionary or deterministic. Look at personas and proxies. Some of this would come out of the ways they already beat tech. Capitalism continues to disrupt itself, but those become new standards. The same transcript might be interpreted by copilots as consumers, clinics, or culprits. "I didn't know anything about crypto when I first got involved. I barely knew what a blockchain was." -- SBF.

Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations
#Machine Learning#Taxonomy#NIST#Paper#PDF#Cybersecurity
·csrc.nist.gov·
NIST Artificial Intelligence (AI) 100-2 E2023, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations