Machine Learning and Social Participation
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The document discusses the role of machine learning and digital platforms in enhancing democratic participation through collaborative decision-making processes. It highlights examples of public consultations and innovations in law-making, emphasizing the need for transparency, simplicity, and the involvement of multiple stakeholders. The document also stresses the importance of capacity building and regulatory frameworks to support open-source initiatives for increased civic engagement.
Machine Learning and Social Participation
- 2. Hello! Yasodara Córdova @yaso Fellow researcher at the Digital Kennedy School Affiliate at the Berkman Klein Center at Harvard Former W3C Specialist Former UN Tech Consultant for Innovation in Democracy Researcher | Product Manager | Industrial Designer Innovation and Democracy Internet-based collaborative Decision making
- 3. PARTICIPATION Ideally. Democracy depends on participation. Direct democracy relies on massive participation.
- 5. 1. 2009 Public consultations Sharing the development of our guidelines: bills go to public scrutiny. Open participation. Open collaboration.
- 6. Text: same structure, easier collaboration ■ Bills are text ■ Texts that share structure ○ Sections ○ Titles ○ Chapters etc Output can be changed with the collaboration of many hands. Even when they don't write text. Even when they just like it.
- 7. Marco Civil Principles and rights for Internet use. More than 2500 comments and suggestions. (more details at http://bit.ly/29MbOiv) Ministry of Justice platform: http://bit.ly/2h0l36q Data Privacy Bill More than 1200 comments + articles in .pdf format -59 different countries (more details at http://bit.ly/2fLALCl) Data from where?
- 9. 1. 2015 Machine Learning Boost the contributions; Human in the loop processes; Law making process can be faster and more efficient
- 10. Actors involved People collaborating Everyone can contribute, using e-mail as identification. Tools have to prevent abuse. Institutions Big articles, academic papers, uploading excerpts in pdf. All these scenarios need to be possible and foreseen in the collaboration processes. Reorganizer Responsible for reorganize, arrange, put new text into the older text - is the maintainer. Her job is to keep the bill coherent and accommodate suggestions and comments of the collaborators. New text The new text can be submitted. Everyone hopes that it's going to be a plausible text, embracing and cohesive. Interactive Interface A "Dashboard", Interactive in a way that results can be edited. Here, one can see and simulate different situations. Algorithms Platform to support the reorganization. It has to be transparent, open. The datasets have to present provenance information.
- 11. ~$ sudo apt-get install reorganizer Reorganizer would break down contributions sent through PDF into .txt ++ Elaborate a data dictionary with key units of analysis per axes ~$ sudo apt-get purge reorganizer The data gathered on the platform, plus the data generated by the reorganizer formed the input of analysis for the web expert team Intimacy Private life Image Dignity Secrecy Breach
- 12. A series of algorithms to work with text An open platform with processes of ML to give insights about data. Questions as: "how many times the answers was negative" - we tried to answer with sentiment analysis, as an example. Transparency An interface so that people could analyse the output of the reorganizer and suggest new modifications based in data visualization. Output New rules to write laws: simpler, open and collaborative. Simplified language appears as key. Three products - with Virgilio Almeida, UFMG
- 13. TransparentOpen Source Reliable Human in the Loop - - > Society in the Loop
- 14. Dashboard Graphics and datavis shows relationships that aren't noticeable if data isn't visually explained Place your screenshot here
- 15. Opinion-forming and Influence The “louder voices” and stakeholder engagement
- 16. Sentiment Analysis Overall negative opinion about the items in consultation
- 17. Hope
- 18. What we needed BigData Massive participation Institutionalization Instruments that use digital technology to improve participation need Laws and regulations Open Source Software Massive participation, massive hacking
- 19. 1. 2016 Machine Learning - accountability ML to follow democratic processes? Where the money goes Why isn’t that applied to the right issues
- 20. An Artificial Intelligence Platform to track expenses? BRAZIL: A COUNTRY THE SIZE OF A CONTINENT 79th on Transparency International’s Corruption Perceptions Index
- 21. An Artificial Intelligence Platform to track expenses?
- 22. An Artificial Intelligence Platform to track expenses? 8th on the global open data index by the OKF https://index.okfn.org/place/
- 23. An Artificial Intelligence Platform to track expenses? OPENNESS CORRUPTION
- 24. Humans in the loop
- 25. Humans in the loop -- Society in the loop
- 26. Humans in the loop -- Society in the loop
- 27. Humans in the loop -- Society in the loop
- 28. What we need BigData → MORE DATA! Massive participation Institutionalization Instruments that use digital technology to improve participation need laws and regulations Open Source Software Massive participation, massive hacking
- 29. What we need
- 30. What we need Capacity building Knowledge! Better institutions Who doesn’t? Open Source Software Massive participation, massive hacking
- 32. Complexity frameworks Context and Implementation of Complex Interventions Making sense of complexity in context and implementation: the Context and Implementation of Complex Interventions (CICI) framework