Designing ethical artificial intelligence
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The document discusses the importance of designing ethical artificial intelligence by focusing on human behavior, societal impacts, and the need for transparency and accountability in data science. It emphasizes human-centered approaches, the relationship between humans and machines, and the significance of addressing bias and ethical standards in AI development. Key areas include ensuring trust, collaboration, and enhancing human potential while considering the broader societal consequences of AI technology.
Designing ethical artificial intelligence
- 1. Designing ethical artificial intelligence @jivanvirdee & @hollielubbock Fjord May 2018, #StrataData
- 3. Artificial intelligence in the news (Google trends) 0 25 50 75 100 Date 2008-07 2009-02 2009-09 2010-04 2010-11 2011-06 2012-01 2012-08 2013-03 2013-10 2014-05 2014-12 2015-07 2016-02 2016-09 2017-04 2017-11 2018-05 Instance Rising interest
- 5. Fitter, Happier, More Productive
- 6. Lonelier, Less satisfied, Less productive
- 8. The issue of Dark Patterns https://darkpatterns.org/hall-of-shame
- 10. AI will save us/kill us https://twitter.com/Mark__Zukerberg | https://twitter.com/elonmusk I think you can build things and the world gets better, with AI especially, I’m really optimistic Until people see robots going down the street killing people, they don’t know how to react Marks understanding of the subject is limited.
- 11. “We are morphing so fast that our ability to invent new things outpaces the rate we can civilise them.” Kevin Kelly, The Inevitable
- 12. So how do we shift the trajectory?
- 13. 13 Human Centred Created from an understanding of human behaviour, motivations, and needs. Is it the best way to solve that problem? How can we make their lives better, easier and more fulfilling? Are the user needs put before the business needs? Humanity centred Considering the effect on society as a whole. What if everyone used your product or service? What the worst thing that could happen to society because of it? Does it intrinsically favour one group of people over another?
- 14. Fair & transparent data science Responsibility & accountability Trust & human machine collaboration Human first 3 key areas
- 15. Fair & transparent data science
- 17. Accuracy vs. explainability Accuracy Explainability Deep learning Ensemble methods SVMs Graphical models Decision trees https://arxiv.org/pdf/1802.00603.pdf
- 19. Methods for Explainability Interpretable models • Regression • Decision trees • Classification rules Models for interpretability • LIME • BETA • LRP
- 20. Any sufficiently advanced technology is indistinguishable from magic ― Arthur C. Clarke
- 21. Increasing levels of context
- 22. Humble machines
- 23. Transparency is key to trust
- 24. “Transparency does not happen on its own: it has to be consciously audited, understood, designed and implemented to take root” ― Andy Polaine
- 26. 800 million jobs lost to automation by 2030 Source
- 27. There’s even an algorithm to check if you’ll lose your job to automation 27 https://www.fastcompany.com/3047269/this-calculator- will-tell-you-if-a-robot-is-coming-for-your-job
- 29. AI replaces workforces AI can enable super human powers
- 30. Human Machine Together 96% 99.5% 92% Human & Machine Source
- 32. Trust = credibility + reliability + authenticity self interest
- 34. HUMAN INTELLIGENT AGENT Augmentation Automation Define the Relationship
- 35. Problem Complexity Bounded Open ended Consequence of Failure Negligible Critical Responsibility Machine Human Independent Autonomy Collaborative Management by exception Supervision Continuous Engagement
- 36. Bounded Open ended Negligible Critical Machine Human Independent Collaborative Management by exception Continuous Engagement Content Moderation Problem Complexity Consequence of Failure Responsibility Autonomy Supervision
- 37. Bounded Open ended Negligible Critical Machine Human Independent Collaborative Management by exception Continuous Engagement Movie Recommendation Service Problem Complexity Consequence of Failure Responsibility Autonomy Supervision
- 38. Problem Complexity Bounded Open ended Consequence of Failure Negligible Critical Responsibility Machine Human Independent Autonomy Collaborative Management by exception Supervision Continuous Engagement Three Mile Island Nuclear Plant
- 39. One of the team, play to your strengths http://humanrobotinteraction.org/journal/ index.php/HRI/article/view/173
- 40. Creativity is going to be far more important in a future where software can code better than we can. Tom Hulme
- 41. Automation could make us more human
- 42. “This is not a race against the machines. If we race against them, we lose. This is a race with the machines.” ― Kevin Kelly, The Inevitable
- 44. It takes momentum to get noticed
- 47. An issue of scale
- 48. Mind the bias
- 49. You’re only as good as your data So how do we make it better?
- 50. From unknown to known
- 51. Codes of ethics Do you have one?
- 52. Data for Democracy Its my job to understand, mitigate and communicate the presence of bias in algorithms. Be responsible for maximizing social benefit and minimizing harm. Practice humility and openness. I will know my data and help future users know it as well. Make reasonable efforts to know and document its origins and document its transformation. Bias will exist. Measure it. Plan for it. Thou shalt document transparently, accessibly, responsibly, reproducibly, and communicate. Engaging the whole community. Do you have all relevant individuals engaged? People before data - data scientists should use a question driven approach rather than a data-driving or methods approach. Consider personal safety and treat others the way they want to be treated. Exercise ethical imagination. Open by default - use of data should be transparent and fair. I will not over/under represent findings. You are part of an ecosystem understand context and provenance. Respecting human dignity. Respect their data even more than your own. Understand where its sources and think about the consequences of your actions. Protecting individual and institutional privacy. Diversity for inclusivity. Attention to bias. Respect for others/persons. Be intentional as you work to create value. https://github.com/Data4Democracy/ethics-resources
- 53. So what do we do with them?
- 54. Ethics Overview Informed Consent Data Ownership Privacy Anonymity Data Validity Algorithmic Fairness Societal Consequences https://www.edx.org/course/mind-of-the-universe-robots-in- society-blessing-or-curse https://www.coursera.org/learn/data-science-ethics
- 55. Designing AI with human values in mind
- 56. Video Link
- 57. Fair & transparent data science Responsibility & accountability Trust & human machine collaboration Human first 3 key areas
- 58. No one’s coming. It’s up to us. — Dan Hon @jivanvirdee @hollielubbock