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Machine Made Goods: Civil society, philanthropy & AI

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This is a slide deck for a session I did for the European Foundation Centre in Dec 2018

Published in: Government & Nonprofit
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Machine Made Goods: Civil society, philanthropy & AI

  1. 1. 1 Machine Made Goods Civil Society, Philanthropy & Artificial Intelligence Rhodri Davies Head of Policy & Programme Director, Giving Thought
  2. 2. Current Disruptive Technologies 2 Artificial Intelligence Blockchain Cryptocurrency Big Data 3D Printing Virtual & Augmented Reality (VAR) Internet of Things (IoT) Autonomous Vehicles & Drones CRISPR/ Biotech Wearables Robotics Human Augmentation Quantum Computing
  3. 3. Why should funders and CSOs care about disruptive tech? 3 New ways of achieving mission 1) Change the way organisations operate 2) Create new problems to address 3)
  4. 4. 4 1) Tech for Good
  5. 5. AI for Good
  6. 6. Blockchain for good 6
  7. 7. VAR for Good
  8. 8. 8 Convergence: “Curing” Visual Impairment?
  9. 9. 9 2) Impact of AI on operating environment for civil society
  10. 10. Artificial Intelligence (AI) 10 Number of key factors in recent AI growth: 1)More powerful algorithms (Deep Learning) 2) Data explosion 3) Greater processing power 4) Investment NB: Narrow/Domain Specific AI, not Artificial General Intelligence (AGI) Yes No
  11. 11. Data & Transparency “The world’s most valuable resource is no longer oil, but data” The Economist (2017)
  12. 12. Data, Robotic Process Automation & Grantmaking
  13. 13. AI & RegTech Enhanced, automated due diligence Predictive/preventative regulation Continuous monitoring of legal & regulatory environment Risk modelling & Predictive analytics CAPABILITIES APPLICATIONS Automation of processes ML for risk profiling and predictive models Natural Language Processing (NLP) Using unstructured data
  14. 14. Chatbots & Conversational AI By 2020, the average person will have more conversations with bots than with their spouse. 30% of web browsing will be done by voice. Chatbots will power 85% of all customer service interactions by the year 2020 Source: Gartner Awareness Info & Services Donations NONPROFIT APPLICATIONS
  15. 15. AI Philanthropy Charity recommender algorithms Social Impact Robo-advisors Collective Intelligence philanthropy Philgorithms
  16. 16. Advice and Recommendations We are increasingly accustomed to algorithmic recommendations
  17. 17. Philanthropy Recommender Algorithms Charity recommendations based on past preferences or peer group behaviour “If you liked Cancer Research UK, you’ll love RNLI!”
  18. 18. Making Philanthropy Advice Mass- Market 18 “AI has the potential to become a great equalizer. Access to services that were traditionally reserved for a privileged few can be extended to the masses.” PWC (2017) Bot.Me: A revolutionary partnership: How AI is pushing man and machine closer together
  19. 19. A bit of context 19
  20. 20. How likely is this to take off? 20
  21. 21. No really, how likely? 21
  22. 22. Philgorithms 22 Algorithm which identifies most pressing needs at any given time + most effective interventions for addressing them & effects automated, rational matching of philanthropic supply and demand
  23. 23. Are Philgorithms Feasible? 23 You can’t remove the element of heart from charitable giving, so this will never happen! A) We will become accustomed to algorithmic advice in all areas of life, so why not charity? Objection: But… B) There has always been a desire among some to make giving more rational C) There will be contexts in which giving is only feasible without human oversight
  24. 24. Making Philanthropy Rational 24 Then… …And now
  25. 25. The Machine-to-Machine (M2M) economy 25 The Internet of Things market is going to be huge, with vast numbers of M2M transactions Could we harness some of this for philanthropy?
  26. 26. M2M Philanthropy? 26
  27. 27. M2M Philanthropy? 27
  28. 28. Collective Intelligence, Intelligence Augmentation & “Centaur Philanthropy”? 28
  29. 29. Breakout 1 29 Where do you see the most likely short and long- term impacts of AI on: 1) Your function? 2) Your organisation? 3) The external operating environment?
  30. 30. 30 3) Now for the bad part…
  31. 31. Weaponised AI
  32. 32. Algorithmic Bias When machine learning algorithms are taught using data sets that contain statistical biases for e.g. race, gender, they exhibit and strengthen those biases over time
  33. 33. Filter Bubbles • Technology such as social media allows us to build ‘filter bubbles’ around our experience • Likely to get worse as increasing reliance on AI-based interfaces tailors our experience of the world to fit existing preferences and biases.
  34. 34. Deepfakes Crisis: Response?Tech: Law Media:
  35. 35. The Attention Economy “The only factor becoming scarce in a world of abundance is human attention.” -Kevin Kelly  Need to compete in this “attention economy” has led to new problems: How do charities compete for our attention without adopting techniques that cause long-term harm?
  36. 36. Child Development Current concerns: Emerging concerns: Future concerns?
  37. 37. Psychological Distance & Desensitisation Brundage, M. et al (2018) The Malicious Use of Artificial Intelligence: Forecasting, Prevention and Mitigation
  38. 38. Inequality Inequality is already a massive economic problem Key question for development of tech: does it reduce or increase inequality?
  39. 39. Key Cross-Cutting Themes 39 Disintermediation Networks & Platforms Filtered experience Radical Transparency Digital Assets Algorithms Data, data, data Urbanisation Inequality New models for social good Attention Economy Automation of work Ageing Population
  40. 40. Key Questions for Civil Society about Disruptive Technologies 40 Will it offer new ways for existing CSOs to run more efficiently or effectively? Could it give rise to new kinds of donations? Will it make it easier or harder to identify potential donors? Could it give rise to entirely new classes of donors? Will it offer new ways of engaging donors and supporters? Could the development of this technology itself be seen as a charitable cause? Could it create new ways for existing CSOs to solve social & environmental problems? Could it disrupt the existing governance structures of CSOs? Could it create entirely new problems that CSOs will have to address? Will it reduce or increase inequality? Could it create new challenges for existing beneficiaries? Will it lead to new organisations emerging to compete with existing CSOs?
  41. 41. What can Funders do about AI? 41  Fund AI Research  Partner with tech firms  Develop in-house expertise Harnessing the Potential Navigating the Operating Environment Addressing the Negative Impact  Pro Bono resources  Country-level advocacy  EU/UN influencing  Work with tech sector on FATML  Support knowledge sharing  Explore RegTech  Mapping/evidence of AI impact  Upskilling civil society  Take lead on open & ethical data usage  Assess automation potential within your org  Track public & private sector uses of AI  Support existing tech for good initiatives
  42. 42. Breakout 2 42 What impact can you see AI having on the people and communities you serve? How will you respond?
  43. 43. Where to find more 43
  44. 44. Rhodri Davies Head of Policy & Programme Director, Giving Thought Charities Aid Foundation rdavies@cafonline.org

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