AI : Animal Like Abilities in Applied AI, What can go wrong?

© 2018 Fair Isaac Corporation. Confidential. 1
© 2018 Fair Isaac Corporation. Confidential.
This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
ANIMAL LIKE ABILITIES ! WHAT COULD GO WRONG WITH THAT!
Jari Koister, Ph.D
VP FICO, UC BERKELEY

Content
1. Introduction
2. Applied AI, examples.
3. Fairness and Interpretability
4. Security in Applied AI
5. Concluding Remarks

© 2018 Fair Isaac Corporation. Confidential. 3© 2018 Fair Isaac Corporation. Confidential. 3
Introduction

Fly On A Plane
TSA Secure Flight uses
FICO software to
protect airline travel
Southwest uses FICO
software to schedule
your flights, assign your
gates and crews
Technology
Identity Resolution
Link Analysis
Optimization
Use Your Credit Card
FICO Predicts fraud in
2.5 billion credit cards
worldwide
Technology
Neural Networks
Collaborative Profiles
Apply For A Loan
FICO Credit Score
powers 10 billion credit
decisions a year
Technology
Predictive Models
Model Management
Order Online
FICO software helps
Retailers deliver on-
time packages,
managing their
inventory, scheduling,
workforce, supply chain
and revenue
Technology
Optimization
Predictive Models
Buy Groceries
FICO software helps
leading supermarket
chains present timely
offers based on
purchase patterns
Technology
Collaborative Profiles
Optimization

Three Categories of Artificial Intelligence
General
Human
AI
AI
playing
Games
Human
Like
Abilities

Testing General Intelligence
‘It was suggested tentatively that
the question, "Can machines
think?" should be replaced by "Are
there imaginable digital computers
which would do well in the
imitation game?”’
COMPUTING MACHINERY AND INTELLIGENCE
Alan Turing, 1950

Testing General Intelligence

Animal and Human Abilities

AI ”abilities” are Improving Rapidly…

Example SQuAD: The Stanford Question Answering Dataset

CAN AI BE TRUSTED?

Applications:
Bringing Animal-like abilities to systems

AI in DEBT COLLECTION

Robotics: Virtual Agent
Triage Incoming, Handle Calls
Voice to
Text
Natural
Language
Understanding
Natural
Language
Generation
Text to
Voice
Intent
Voice
/
SMS
/
Chat
Decision
Previous
Calls
Customer
Account Data
Fraud
Expertise
Machine
Learning
Optimized
Channel
Strategy
Compliance
Rules
DMSComponents
Transcription
& Tagging
Cognitive
Analysis
Consumer

© 2018 Fair Isaac Corporation. Confidential. 15 © 2018 Fair Isaac Corporation. Confidential. 15
Demo
Agent on Telephone with Borrower
Greeting &
Identity
Check
AI
Confirm
IdentityAgent
Contact
info to be
confirmed
Check or add
contact info
Inform of
money owed
Record Paying
/ Not-paying
Ask reason
for not
paying
Consumer

IDENTITY IN THE DIGITAL AGE

Identity
fraud; Card
not present
Transaction
Fraud

Objectives and Business Value
Reduce Fraud, Fraudster Id
Identity Theft Detection/Fraud
Investigation
Fraud Prevention
Voice-based Banking
Emotion Detection

Comprehensive Approach to Identifying Fraudsters
AI-based
Fraudster
Detection
Multi-
Layer
Security
Omni-
channel
capability

Anatomy of a Voice-Authentication System

Voice processing Architecture
Phonetic features
from speaker’s voice

Speaker Recognition using 3D CNN

AUTOMATING RISK AND REWARD
Credit line approvals
Product offers with financing
Debt collection or settlement
Credit increases
Overdraft treatment

Automotive Alternative Deal Structures

Collections Treatment Optimization
Project Goals
• Minimize credit losses
• Control Collections Operating Expenses
• Buy a wider spread of business as collections becomes more effective
Project Realization – Achievements
• Millions in losses avoided in the first year, more in subsequent years
• Nearly 1600 Repossession Units avoided
• Over 150 Skips (Full Balance Charge-offs) prevented
• 10,000 customers did not roll to 30+
• 9% portfolio growth, with minimal incremental collections operating expenses
• As the originations mix has gotten deeper, collections volume has stayed flat
and effectiveness has increased

Collections Treatment Optimization
Data
Predictive
Modeling
Optimization
Scenario
Selection
Strategy

AI will change
Computer Science

B-Trees and Bloom Filters
1 5 17 20 219 10 12
17 20 8 11 4 6 67 23 1 9
1010010001010010
Key Present?
Find Record by Key
8 16

Learned Index Structures
1 5 17 20 219 10 12
17 20 8 11 4 6 67 23 1 9
[0,1]
Key Present?
Find Record by Key
Predict Position
(Neural Network)
Classification
(Neural Network)

Challenges

Applied AI Systems Make Decisions
Sever financial impact
Impact on peoples lives
Are influence by past and present behavior
Emotional Intelligence
As fair as the data they use Manipulated?

Mission Critical AI
Models that are trusted and robust
Counter adversarial attacks
Models that are tracked and governed

Regulations, Trust, Validation, Explanations

Validation: Inference of Criminality Using Face Images
Ref. Automated Inference on Criminality using Face Images. Xiaolin Wu and Xi Zhang. 2016

Trust: It works, but how? Algorithmic Trading
“The engineers couldn’t explain why the AI was executing
the trades it was making.
The creation was such a black box that even its creators
didn’t fully understand how it worked.”
”Funds
managing
$12.3 billion.”

Regulation: GDPR
Article 22. Automated individual decision making, including profiling
1. The data subject shall have the right not to be subject to a decision based solely
on automated processing, including profiling, which produces legal effects concerning him or her or similarly
significantly affects him or her.
2. Paragraph 1 shall not apply if the decision:
...
3. In the cases …, the data controller shall implement suitable measures to safeguard the data subject’s rights and freedoms
and legitimate interests, at least the right to obtain human intervention on the part of the
controller, to express his or her point of view and to contest the decision.
4. Decisions referred ….

Trust: Decisions has real impact on lives, literally
https://www.technologyreview.com

Validation: COMPAS: Correctional Offender Management Profiling for Alternative
Sanctions
137 Questions
Score
According to “Algorithms in the Criminal Justice System” at epic.org
Most states have a risk assessment system, 5 uses COMPAS.

Validation: Undetected Machine Bias

And it affects all of us…
… and now congress knows

The Goal of Explainable AI
Ref. Explainable Artificial Intelligence (XAI). DARPA-BAA-1653
Explainable
Model
Today
Training
Data
Learning
Process
Learned
Function
User with
a Task
Output
This is
a cat
Tomorrow
Training
Data
New Learning
Process
User with
a Task
Explainable Interface
This is
a cat It has fur,
whiskers
and claws
likely
because

Explaining complex models is hard- Lets take a look

The World Wants Both High Accuracy and High Explainability
Image: Defense Advanced Research Projects Agency
Text: http://nautil.us/issue/40/learning/is-artificial-intelligence-permanently-inscrutable
WHAT VS. WHY: Modern learning algorithms show a tradeoff between human interpretability, or explainability, and their
accuracy. Deep learning is both the most accurate and the least interpretable.
Apply high
accuracy and high
explain ability

Validating and Explaining Models
Validation
Linear Optimization
Algorithms
Decision Flow
DMS
Analytics Workbench
Explanations
Decision
Explanation

Robust and Secure ML

Possible Threats
•Serious Security Breaches: Influence Decisions.
•Unfair and Biased Systems.
•Serious Security Breaches: Unintended data sharing.
•Hard to detect [terrorist] attacks.
•Undetected changes in behavior and performance.
•Detrimental behavior from unexpected data.

Attack Surfaces
Towards the Science of Security and Privacy in Machine Learning
Adversarial Inputs Data Poisoning
Model Skewing
Feedback Weaponization
Model Stealing
Reconstruction

Adversarial Inputs: Physical and Digital
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A SurveyRobust Physical-World Attacks on Deep Learning Visual Classification

Poisoning

Input Robustness

Data Poisoning: Model Skewing
https://elie.net/blog/ai/attacks-against-machine-learning-an-overview

Sharing of Secrets
a-g-h-t : 3
d-g-v-h : 6
j-g-x-e : 8
….
Input
Forward
Loss
BackwardWeight
Biases
a-g-*-tScore
Result a-g-h-t : 3

Concluding Remarks

Trends and Research in Systems for AI
*A Berkeley View of Systems Challenges for AI. I Stoica et. Al. 2017

Key Points
State of AI: Adding Animal-like abilities to Systems.
Claim: Transparency, Trust, Fairness a must for Applied AI
Prediction: Model Security will become a concern for most
applications.
1
2
3

This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
Thank you for listening
jarikoister@fico.com
jari@acm.org
jarikoister@berkeley.edu
Excited to hear what you are working on

AI : Animal Like Abilities in Applied AI, What can go wrong?

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