Watch / Machine Learning Resistance for Human Rights on the Blockchain.

Machine Learning Resistance for Human Rights on the Blockchain.

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Machine Learning Resistance for Human Rights on the Blockchain.

Duration: 00:32:25

Speaker: Santiago Siri

Type: Breakout

Expertise: Beginner

Event: Devcon 5

Date: Oct 2019

Bitcoin’s original white paper description of “one CPU one vote” (Nakamoto, 2008) shaped the software industry to think governance centered around machines, instead of humans. Although a fundamental right to privacy bent early blockchain design toward anonymity, this property facilitates the malicious use of Sybil Attacks (Douceur, 2002) significantly undermining the governance capacity of protocols. The importance of formalizing human identities can be inferred from centralized networks: major social media platforms were established when they achieved a sufficient level of consensus over their identity protocols, thus creating a trust layer on top of which a myriad of social applications could be built. This networked social infrastructure has in turn facilitated the emergence and spread of multiple borderless political movements. However, being highly vulnerable to several artificial intelligence exploits such as addictive algorithms, bots and deep fake technology, the underlying architecture of current protocols exposes society to political manipulation as well as data theft. On this talk we'll explore the different approaches to reach a “one human one node” graph, providing protocol specifications able to serve as a source of legitimacy in the governance of digital networks and strategies that can keep these protocols free from AI.
About the speakers


Santiago Siri

Founder of Democracy Earth Foundation, a non-profit organization backed by Y Combinator that has built Universal Basic Income on Ethereum and launched the Proof of Humanity protocol. In the past he started the Partido de la Red, the first political party born out of the Internet that ran for elections in Argentina. Author of "Hacktivismo", published in 2015 by Random House.

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