Events of interest to the Cyber Initiative community
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Cyber Initiative and Related events:
Cyber researcher Herb Lin has published a new edited volume on offensive cyber operations, called Bytes, Bombs, and Spies, based on a Cyber Initiative-supported workshop on this topic held in 2016. Read more about the book at https://cisac.fsi.stanford.edu/news/scholars-examine-new-era-cyber-warfare-new-book
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Stanford Bug Bounty Program Launch - Saturday, January 19th, 10am-4pm, in Lathrop 282. Lunch provided
Stanford Blockchain Conference - Jan. 30th - Feb. 1st, 2019. Stanford, CA: Arrillaga Alumni Center
The Stanford blockchain conference (https://cyber.stanford.edu/sbc19) will take place on Jan. 30 - Feb. 1st. This conference will explore the use of formal methods, empirical analysis, and risk modeling to better understand security and systemic risk in blockchain protocols. We aim to foster multidisciplinary collaboration among practitioners and researchers in blockchain protocols, distributed systems, cryptography, computer security, and risk management. To register please visit the registration page (http://web.stanford.edu/~aberke/sbc19.fb).
Understanding the limitations of AI: When Algorithms Fail - Jan. 18th, 1:15pm, Packard 202
Automated decision making tools are currently used in high stakes scenarios. From natural language processing tools used to automatically determine one’s suitability for a job, to health diagnostic systems trained to determine a patient’s outcome, machine learning models are used to make decisions that can have serious consequences on people’s lives. In spite of the consequential nature of these use cases, vendors of such models are not required to perform specific tests showing the suitability of their models for a given task. Nor are they required to provide documentation describing the characteristics of their models, or disclose the results of algorithmic audits to ensure that certain groups are not unfairly treated. I will show some examples to examine the dire consequences of basing decisions entirely on machine learning based systems, and discuss recent work on auditing and exposing the gender and skin tone bias found in commercial gender classification systems. I will end with the concept of an AI datasheet to standardize information for datasets and pre-trained models, in order to push the field as a whole towards transparency and accountability. Timnit Gebru is a research scientist in the Ethical AI team at Google and just finished her postdoc in the Fairness Accountability Transparency and Ethics (FATE) group at Microsoft Research, New York.