Speaker: Amy Hodler, Analytics & AI Program Manager, Neo4j

1. Responsible AI
Amy Hodler
Analytics and AI Program Manager
Neo4j

2. October 5, 1960
NORAD thinks there’s a massive
Soviet nuclear attack
. . . 99.9% certainty
Source: Command and Control by Eric Schlosser

3. Belief in Magical Technology
is Influencing Today’s AI Systems
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

4. Biased AI
5
Recruiting Tools
Amazon recruiting tool shut down for bias against
women after it codified discriminatory practices due
to narrow data sets
Sources: Thompson Reuters, 2018, The Verge 2019, The Seattle Times, 2019, Gender Shades Project
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

5. Unknowable AI
6
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
Source: The New Your Times, 2017

6. Inappropriate AI
7
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
Source: CBS News, 2018

7. 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

8. AI Needs Context

9. Artificial Intelligence
is the WHAT
PROBABILISTIC EATS LOTS OF DATA
10
Machine Learning
is the HOW

10. Decisions Require Context and Connections
11
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

11. AI Requires Context and Connections, Too
12
45
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 technology

12. “We saw
her duck.”
AI is Limited Without
Context
?
Narrowly focused
Subpar predictions
Limited transparency

13. Graph Is Accelerating AI Innovation
14
4,000
3,000
2,000
1,000
0
2010 2011 2012 2013 2014 2015 2016 2017 2018
Graph Technology Used
graph neural network
graph convolutional
graph embedding
graph learning
graph attention
graph kernel
graph completion
AI Research Papers Featuring Graph are on the Rise
Source: Dimension Knowledge System
with over 100 research organizations

14. Graphs as Context

15. There Is No Isolated
Data in Nature
16
Graphs are built for relationships –
with relationships
Imbue individual entities with
connections as a fabric
Enriches data so it is more useful

16. 17

17. Neo4j and the Property Graph Model
18
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.
Neo4j built a graph database that can process
millions of data connections per second

18. Graph Context
for Responsible AI
19

19. Situational
flexibility
Predictive
accuracy
Fairness
Reliability and
Explainability
Graphs Facilitate Responsible AI
20
Robustness Trustworthiness
Incorporating context and connections improves
the quality and value of AI systems

20. $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
Prescriptions for Peril
21

21. 22
Graph algorithms reveal clusters of interactions in large networks
to detect communities for ML
Graph Analysis for Detecting Fraud, Waste, and Abuse in Health Care Data
Predicting fraud accurately requires extreme insight into the
relationships among entities
Prescription Fraud Detection
with Graphs and ML

22. Driverless Cars Must Be Foolproof
Tesla autonomous car tricked into changing lanes with stickers
It’s disturbingly easy to trick AI into doing something deadly
23

23. 24
Autonomous Decisions
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

24. 25
Gaming the System
High-stakes criminals misrepresented and manipulated input data
to fly under the radar
Detecting evolutionary financial statement fraud

25. 26
Prevent Data Manipulation
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

26. Past and Current Data Amplifies Bias
Data skewed by discrimination and demographics creeps into
policing, programs and sentencing
To Predict and Serve? Predictive Policing Systems
Machine Bias report on COMPAS Software by ProPublica
27
COMPAS Scores at Booking

27. 28
Reveal and Eliminate Bias
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
Connected by James Fowler
“…data without context is just
organized information.”
Albert Einstein

28. 29
Human Interaction is Crucial
Boeing fails to incorporate pilot reactions into
737 Max auto-pilot system
Too many human errors brought down the Boeing 737 Max

29. 30
Human Centric
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 /
processes

30. What’s Next
31

31. Human Values
Increasingly Impact AI
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
Sources: NIST, Univ of Oxford, The Verge

32. Graphs Already Bring Context to
Data Science, Machine Learning and AI
33
Financial
Crimes Recommendations
Cybersecurity
Predictive
Maintenance
Customer
Segmentation
Churn
Prediction
Search
& MDM
Drug
Discovery

33. Context for AI Will Be Standard
34
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

34. “The idea is that graph networks are bigger than any
one machine-learning approach.
Graphs bring an ability to generalize about structure that the
Next Major Advancement in AI: Graph Native Learning

35. Next Major Advancement in AI: Graph Native Learning
36
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

36. 37
“Coders are the most
empowered laborers that
have ever existed.”
Anil Dash @anildash
Glitch CEO
Ethical technology activist

37. 38
Practical Tips for More Responsible AI
Know & Track Data
(Graphs for data lineage)
De-Bias Data
(AI Fairness 360 toolkit)
Involve Domain Experts
(Predictors, data, success)
Learn/Ask for Help
(Algorithmic Justice League)
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)

38. Parting Thoughts About
the Future of AI

39. 40
“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

40. 41
“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

41. 42
“Your future hasn’t been written yet.
No one’s has.
Your future is whatever you make it.
So make it a good one.”
Doc Brown
Crazy Professor, Back to the Future

42. April 20-22, 2020 | New York
Connect Your Data.
Build The Future.
graphconnect.com

43. Responsible AI
Amy Hodler
@amyhodler
amy.hodler@neo4j.com

44. 45
“… not everything that can be
counted counts, and not everything
that counts can be counted.”
William Bruce Cameron
Sociologist
Informal Sociology: a casual introduction to sociological
to sociological thinking 1963

45. 46
“Context-awareness is a core
requirement …
. . . sufficient perception of the user’s
environment, situation, and context to
reason properly.”
Oliver Brdiczka
AI Architect, Adobe
Professor, Georgia Tech