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Neo4j - Responsible AI

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Mark Needham - Neo4j

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Neo4j - Responsible AI

  1. 1. 2 Recruiting Tools Amazon recruiting tool shut down for bias against women after it codified discriminatory practices due to narrow data sets 99-100% 65-79%93-98% 88-94% Recognition AI Calls for regulation on use of facial recognition after consistently higher error rates for darker-skinned and female faces
  2. 2. 3 Black Box Risk Assessments 10+yrs of model behavior but denied parole due to high-risk assessment Details on over 100 factors and weights protected as commercially proprietary Single, subjective question lowered risk scores from an 8 (of 10) down to 1
  3. 3. 4 China Social Credit System Ranks citizens’ behavior to determine their social and credit worthiness 1.4B people will have a score by 2020 which will impact their social and economic rights
  4. 4. Myth: Shiny is better Myth: There’s an unavoidable trade-off between accuracy and interpretability/privacy Myth: All we need is more data Myth: It’s ok to transfer AI created for non-critical tasks to high-stakes decisions
  5. 5. As creators of artificial intelligence systems, we have a duty to guide the development and application of AI in ways that fit our social values Responsible AI Accountability Fairness Public Trust
  6. 6. PROBABILISTIC EATS LOTS OF DATA 8
  7. 7. 9 We observe, collect adjacent data, and make connections We process the connections and to learn and make informed, in-context decisions We make tens of thousands of decisions daily, most of which depend on surrounding circumstances and context. 45
  8. 8. 10 AI must access and process a great deal of contextual, connected information • Learn from adjacent information • Make and refine judgements • Adjust to circumstances The fastest, most reliable way to manage data connections is with graph technology45
  9. 9. ? Narrowly focused Subpar predictions Limited transparency
  10. 10. 12 “AI is not all about Machine Learning. Context, structure, and reasoning are necessary ingredients, and Knowledge Graphs and Linked Data are key technologies for this.” Wais Bashir Managing Editor, Onyx Advisory
  11. 11. 14 Graphs are built for relationships – with relationships Imbue individual entities with connections as a fabric Enriches data so it is more useful
  12. 12. 15
  13. 13. 16 EMPLOYEE name: Amy Peters date_of_birth: 1984-03-01 employee_ID: 7875 COMPANY CITY :HAS_CEO start_date: 2008-01-02 :LOCATED_IN start_date: 2008-01-02 Neo4j invented the property graph in 2002 using a napkin sketch – the connected-data model that still works today. We can process millions of data connections per second and perform analytics on billions of nodes
  14. 14. 17 4,000 3,000 2,000 1,000 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 Graph Technology Used AI Research Papers Featuring Graph are on the Rise
  15. 15. 18
  16. 16. Situational flexibility Predictive accuracy Fairness Reliability and Explainability 19 Robustness Trustworthiness Incorporating context and connections improves the quality and value of AI systems
  17. 17. $72.5 Billion Opioid Insurance Fraud per Year In frauds rings drugs are improperly prescribed by doctors and filled by cooperating pharmacists, all of whom pocket illegal payments 20
  18. 18. 21 Graph algorithms reveal clusters of interactions in large networks to detect communities for ML Predicting fraud accurately requires extreme insight into the relationships among entities Predictive accuracy
  19. 19. Driverless Cars Must Be Foolproof Tesla autonomous car tricked into changing lanes with stickers 22
  20. 20. 23 Adjacent data helps widen and deepen the scope of AI systems so they are more broadly applicable in their environments Situational awareness is crucial when context-based learning and actions are part of AI systems 4 5 Situational flexibility
  21. 21. 24 Gaming the System High-stakes criminals misrepresented and manipulated input data to fly under the radar Detecting evolutionary financial statement fraud
  22. 22. Reliability and Explainability 25 When data is stored as a graph, it’s easy to track how it changes, who changes it and where it is used For AI solutions to be viewed as reliable the underlying data needs to be reliable Example from Neo4j Risk Mgmt. Solutions
  23. 23. Past and Current Data Amplifies Bias Data skewed by discrimination and demographics creeps into policing, programs and sentencing 26 COMPAS Scores at Booking
  24. 24. 27 Fairness Understanding our data can reveal bias inherit in the information, in how it’s collected or in how it’s used to train our models Graphs adds contextual information to our ML data and reveals relationships within data – which are often better outcome predictors than raw data “…data without context is just organized information.” Albert Einstein
  25. 25. 28 Human Interaction is Crucial Boeing fails to incorporate pilot reactions into 737 Max auto-pilot system
  26. 26. 29 AI systems can be over-fitted to tight scenarios and idealized situations that don’t account for the range of human interactions Graphs encapsulate the way we think about the world, making it easier to incorporate human responses and explain outcomes / processesRobustness Trustworthiness
  27. 27. 30
  28. 28. AI guidelines that promote societal values AI solutions will increase situational appropriateness, tamper-proofing, explainability and transparency Faster adoption of AI solutions as they become more trustworthy
  29. 29. 32 Financial Crimes Recommendations Cybersecurity Predictive Maintenance Customer Segmentation Churn Prediction Search & MDM Drug Discovery
  30. 30. 33 The inclusion and use of adjacent information as context for AI will become a standard This will drive more reliable, accurate and flexible AI solutions
  31. 31. “The idea is that graph networks are bigger than any one machine-learning approach. Graphs bring an ability to generalize about structure that the individual neural nets don't have.”
  32. 32. 35 Implements machine learning in a graph environment Native graph learning will move today’s AI from a rigid, black box approach to extremely flexible, accurate and transparent models Lets users input connected data Learns while preserving transient states Produces outcomes in graph format Enables experts to track and validate AI decision paths More accurate with less data, learning important features
  33. 33. 36 “Coders are the most empowered laborers that have ever existed.” Anil Dash @anildash Glitch CEO Ethical technology activist
  34. 34. 37 Know & Track Data (Graphs for data lineage) De-Bias Data (AI Fairness 360 toolkit) Learn/Ask for Help (Algorithmic Justice League) Involve Domain Experts (Predictors, data, success) Planning & Data Collection Train & Model Results to Implementation Add Relationships (Graph features, Counterfactual search) Look at Model Exchanges (ONNX, MAX) Use Interpretable Models Where You Can (Prediction Lab at Duke) Add Context to AI Predictions & Heuristic AI (Knowledge graphs) Use Formal & Independent Risk Assessments (Checklists to committees) Insist on Explanations in High-Stakes Decisions (Accurate, complete, faithful)
  35. 35. 38 Know & Track Data (Graphs for data lineage) De-Bias Data (AI Fairness 360 toolkit) Learn/Ask for Help (Algorithmic Justice League) Involve Domain Experts (Predictors, data, success) Planning & Data Collection github.com/IBM/AIF360 youtube.com/watch?v=Y0KA5U81w3U youtube.com/watch?v=Y0KA5U81w3U ajlunited.org/
  36. 36. 39 Train & Model Add Relationships (Graph features, Counterfactual search) Look at Model Exchanges (ONNX, MAX) Use Interpretable Models Where You Can (Prediction Lab at Duke)
  37. 37. 40 Results to Implementation Add Context to AI Predictions & Heuristic AI (Knowledge graphs) Use Formal & Independent Risk Assessments (Checklists to committees) Insist on Explanations in High-Stakes Decisions (Accurate, complete, faithful) ec.europa.eu/digital-single-market/en/ news/ethics-guidelines-trustworthy-ai fujitsu.com/global/documents/about/res ources/publications/fstj/archives/vol5 5-2/paper14.pdf
  38. 38. 41 Know & Track Data (Graphs for data lineage) De-Bias Data (AI Fairness 360 toolkit) Learn/Ask for Help (Algorithmic Justice League) Involve Domain Experts (Predictors, data, success) Planning & Data Collection Train & Model Results to Implementation Add Relationships (Graph features, Counterfactual search) Look at Model Exchanges (ONNX, MAX) Use Interpretable Models Where You Can (Prediction Lab at Duke) Add Context to AI Predictions & Heuristic AI (Knowledge graphs) Use Formal & Independent Risk Assessments (Checklists to committees) Insist on Explanations in High-Stakes Decisions (Accurate, complete, faithful)
  39. 39. 42 “A lot of times, the failings are not in AI. They're human failings... …if you’re not thinking about the human problem, then AI isn’t going to solve it for you.” Vivienne Ming Executive Chair & Co-Founder, Socos Labs 

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