Speaker: Amy Hodler, Graph Analytics and AI Program Manager, Neo4j

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

2. AI Gone Mean
2
Microsoft Tay Twitter Bot
Learned from users how to respond
Learned offensive language and slurs
Treated loud, outrageous opinions as
the norm
Source: The Verge, 2016

3. Biased AI
3
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

4. Inappropriate AI?
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
Source: CBS News, 2018

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. AI Needs Context

7. Artificial Intelligence
is the WHAT
Computer processes that
have learned to accomplish
specific tasks in ways that
mimic human decisions
PROBABILISTIC
Algorithms train models
via specific examples and
progressive improvements
without explicit direction
EATS LOTS OF DATA
7
Machine Learning
is the HOW

8. Decisions Require Context and Connections
8
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

9. AI Requires Context and Connections, Too
9
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

10. Graph Is Accelerating AI Innovation
10
4,000
3,000
2,000
1,000
0
2010 2011 2012 2013 2014 2015 2016 2017 2018
AI Publications with
Graph in Title
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

11. 11
AI is Limited Without Context
?
Narrowly
focused
Subpar
predictions
Limited
transparency

12. Graphs as Context

13. There Is No Isolated
Data in Nature
13
Graphs are built for relationships
– with relationships
Imbue individual entities with
connections as a fabric
Enriches data so it is more useful

14. 14

15. Neo4j and the Property Graph Model
15
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

16. Graph Context
for Responsible AI
16

17. Situational
flexibility
Predictive
accuracy
Fairness
Reliability and
Explainability
Graphs Facilitate Responsible AI
17
Robustness Trustworthiness
Incorporating context and connections improves
the quality and value of AI systems

18. $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
18

19. 19
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

20. 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
20

21. 21
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

22. 22
Gaming the System
High-stakes criminals misrepresented and
manipulated input data to fly under the radar
Detecting evolutionary financial statement fraud

23. 23
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

24. 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
24
COMPAS Scores at Booking

25. 25
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

26. 26
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

27. 27
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

28. What’s Next
28

29. Graphs Already Bring Context to
Data Science, Machine Learning and AI
29
Financial
Crimes Recommendations
Cybersecurity
Predictive
Maintenance
Customer
Segmentation
Churn
Prediction
Search
& MDM
Drug
Discovery

30. Context for AI Will Be Standard
30
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. “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.”
Next Major Advancement in AI: Graph Native Learning

32. Next Major Advancement in AI: Graph Native Learning
32
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
“Coders are the most
empowered laborers
that have ever existed.”
Anil Dash @anildash
Glitch CEO
Ethical technology activist

34. 34
Getting Started Training/Modeling Outcomes
Add Context to
AI Decisions
(Knowledge graphs)
Stop, Think and Acknowledge
Tools for More Responsible AI
Know & Track Data
(Graphs for data lineage)
De-Bias Data
(AI Fairness 360 toolkit)
Add Relationships to
ML Training
(Graph feature engineering
Counterfactual search)
Model Exchanges
(ONNX, MAX)
Learn/Ask for Help
(Algorithmic Justice League)

35. Human Values
Will 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

36. Parting Thoughts About
the Future of AI

37. 37
“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

38. 38
“Context-awareness is a core
requirement …
. . .that it has sufficient
perception of the user’s
environment, situation, and
context to reason properly.”
Oliver Brdiczka
AI Architect, Adobe
Professor, Georgia Tech

39. 39
“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. April 20-22, 2020 | New York
Connect Your Data.
Build The Future.
graphconnect.com

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