artificial intelligence needs explanation ai · 2020-06-12 · copyright librt 2019 3 fundamental...

Copyright LibRT 2019

AIX

Why and how transparency increases the success of

Artificial Intelligence solutions.

Silvie Spreeuwenberg

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Artificial Intelligence needs eXplanation


AI EXPLAINED

AI is a technology trend referencing any

development to automate or optimize tasks that

traditionally have required human intelligence.

Society is demanding transparency

StatisticsMachine learning

Rule-based systems

IT

Algorithms


AI & IT

Since the invention of the computer, AI and IT have

been closely related.

A picture of the analytical machine. Description generated by Microsoft’s AI engine: A picture containing wall, indoor, chair, table.


AI & IT

A machine could be programmed to solve

problems of any complexity or even compose music.

Lady Lovelace (1843)


AI HYPE

Startups in AI raise more money.

Everyone will interact daily with AI-based within next

decade.

Gartner Hype Cycle for emerging technologies 2018

HYPE CYCLE FOR EMERGING TECHNOLOGIES, 2018

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THREE AI WAVES1

19901960

Connectionism Expert systems Machine learning

2020


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Why XAI is important?

I am not an expert

I am an expert


WHAT AI CAN DO

The deceased artist Dali is talking to you, explaining about his life and making selfies with visitors as if

he is still alive.

Dali Museum, St. Petersburg, Florida


DEMYSTIFY

It’s a lot more fun to think of robots that can think than to realize the cold

hard truth that they (currently) can’t.

Shamoon Siddiqui’s Shallow Thoughts About Deep

Learning.

WHAT IS THE HELLO WORLD EXAMPLE OF A

NEURAL NETWORK?


A NEURON

Artificial Neural Networks are “biologically inspired.”

We have neurons in our brains (over 100 billion!) and they connect to each other in many interesting

ways.


BASIC MATH

Algebra is solving equations with unknowns

(called “variables.”)

If you have one variable it is easy to solve… but the

more variables you add the more complex it get’s

https://medium.com/shallow-thoughts-about-deep-learning/why-deep-neural-networks-dont-actually-think-b5768f751f43


STRATEGY

Try to come up with numbers for the

(potentially) tens of thousands of variables.

Since you can’t come up with numbers that

DEFINITELY work, it’s finding numbers that give

the fewest errors.


ARCHITECTURE OF A SIMPLE NEURAL NETWORK


EXAMPLE CASE

Hello world example of NN

Recognize the numbers 1, 2,3,4,5,6,7,8 and 9 in a

grid consisting of 64 pixels.

Youtube channel 3Blue1Brown


ACHITECTURE64 input nodes

- one for each pixel -

64 hidden nodes- the knowledge -

9 output nodes- one for each number -

all nodes are connected to each other (not shown)

1

2

3

4

5

6

7

8

9

1 …….. 64 1 …….. 64


Weights of the links are adjusted based on the

activation in the previous layer and the predicted

outcome.

1

2

3

4

5

6

7

8

9

1 …….. 64 1 …….. 64

LEARNING


Perhaps a set of neurons is activated for

each horizontal line in the input.

1

2

3

4

5

6

7

8

9

1 …….. 64 1 …….. 64

KNOWING


DEEP-NN

Multiple layers, multiple neural networks connected.

Lot’s of computing power.


AI IS NOT…

The system that beat the best human players in the ancient game of Go was

REALLY good at… playing Go. That’s it.

It isn’t able to take that incredible expertise and

apply it to ANY other problem.


WHAT AI BRINGS TO FINANCE ?

Real time transaction monitoring to detect fraud by data analysis of past

transactions, expert knowledge and machine

learning.

It is not acceptable to close an account based on the risk

score of a model that we don’t understand.


Only 52%

of graduates get graduate-level jobs,

CIPD reveals

other type of jobs

https://www.recruitment-international.co.uk/blog/2017/11/ only-52-percent-of-graduates-get-graduate-level-jobs-cipd-reveals

48 % 52%

graduate-level jobs

Google’s algorithm that was trained to help them

selecting new hires turned out to be highly gender biased towards man.

AI FAILS

POOR DATA

BIASED DATA

MISSING FEATURES

MISSING SITUATIONS


“Should I marry Ann?”

_David

“NO!”

_Oracle

“Nobody would say, ‘I'm voting for this guy

because he's got the stronger chin,’ but that, in fact, is

partly what happens.”

PsychologistKahneman,

Nobel prize 2002

HUMAN DECISION MAKING


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Why XAI is important?

It will be required

Better ROI

More trusted

Enhanced decision making

It is feasable

5 REASONS


1Systems that explain themselves

have a better ROI

Explanations make a solution easier adaptable

to changes

Explanations increase the success rate of

AI solutions

1


BUSINESS & ITIT

… has a tendency to make things more complex than

it really is.

Business … have a tendency to

easily defer their responsibility to IT.


We can’t just use AI instead of understanding

but must under first and then add AI!

Troubled relationship with IT

The numbers in the above graph, based on the CHAOS Report from the Standish Group, list the percentage of IT projects from 2011 – 2015 that are delivered On Time, On Budget, with satisfactory result (called the Modern Resolution).


Your business will be more trusted

2

Make more credible decisions

Being a responsible business


MONITORING

TRANSACTIONS

BIG DATA

AI MODEL

LIQUIDITY BUFFERS

EXPLANATION

The financial sector is commonly held to a higher

standard as trust is essential for the system.

When it comes to AI, strong operational controls

could be more important then capital and liquidity buffers.

De Nederlandse Bank report on AI

in financial sector 2019


Explanations enhance decision making3

Unwanted decision biases come to the surface

Missing commonsense knowledge is detected


AI model presented with a foil makes the wrong

decision.

When it sees 'a cat in front of a dog house' it

concludes that it’s a dog.

When it sees a 'dog playing with a ball' it concludes

that it’s a cat.

etc.


To accurately forecast the number of passengers on the first day of a holiday season you expect this

common knowledge to be part of the explanation.


Explainable AI is feasible using a six-step approach

41. Get a shared

understanding of the domain

2. Understand the task and select the right scope

3. Collect the right data and improve its quality

4. Select AI technology that deliver results

5. Generate good explanations

6. Evolve the system over time


Explainable AI is feasible using a six-step approach1. Get a shared understanding of the domain

Businesses that can state clearly who they target and

what it means to be successful are likelier to be

efficient, effective and successful in the

application of AI and decision support systems.


Perhaps your organization is working on an enterprise

ontology or data dictionary?

These are good initiatives to align terminology

between departments. Hopefully your business

stakeholders are involved!


Explainable AI is feasible using a six-step approach2. Understand the task and select the right scope

To collect the right data for your solution (the next step), you first have to

understand the decision task that you want to

support.


Break-up the question into potential influencers and phrase them as a testable

hypothesis.

Test them using basic statistical analysis on the available data and create

a root-cause analysis.


Explainable AI is feasible using a six-step approach3. Collect the right data and improve its quality

The good news about this decennium is that most

organizations are already collecting data.

Are we collecting the right data for our objectives?


A model to predict the orientation (North, South or East West) of a highway in

the US based onthe Highway number will be

very complex....

while a model that is allowed to use the ‘feature’

indicating if the highway number is an even number (or not) will be very simple.


Explainable AI is feasible using a six-step approach4. Select AI technology that deliver results

It is the task that determines which

technique should be used.

Use a hammer if you are dealing with a nail and don’t try the screwdriver because

it is available!


Deterministic AI methods generate results for a

task using a recipe that is completely designed in

advance.

There are many problems that are non- deterministic by nature but can be well- supported using deterministic methods.

Non-deterministic methods generate re-

sults for a task based on patterns that are found

using advanced statistics and (big) data.


XAI combines the technology of the three AI waves

DEEP LEARNING

NEURAL NETWERK

STATISTIC

EXPLAINABILITY

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BAYESIANNETWERKS

MARKOVCHAINS

DECISION TREES

RULES


Explainable AI is feasible using a six-step approach5. Generate good explanations

If the explanation is complicated, nonsensical

or is difficult to understand, the distrust towards the system’s decision will

increase.


Instead we need to develop seperate models to support communication of

an explanation in a context

Accuracy, robustness and precision are not the way to convince a domain expert.

DEEP LEARNING

NEURAL NETWERK

STATISTIC

EXPLAINABILITY

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NIN

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FOR

MA

NC

E

BAYESIANNETWERKS

MARKOVCHAINS

DECISION TREES

RULES

DEEP LEARNING

NEURAL NETWERK

STATISTIC

EXPLAINABILITY

LE

AR

NIN

G P

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FOR

MA

NC

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BAYESIANNETWERKS

MARKOVCHAINS

DECISION TREES

RULES

“Should I marry Ann?”

_David

“NO!”

_Oracle

MACHINE LEARNED MODEL EXPLANATION MODEL INTERACTION MODEL


An explanation does not need to be complete


22

Are we in a catch 22?


Society is demanding transparency

People have ethical concerns

Governments prepare legislation demanding

explanations

5


AI technology can be used for the good and the bad.

… it can fake images, fake videos and make fake news.

… it can detect lies and guide people to make better

decisions by using large amounts of data.

www.thispersondoesnotexist.com

www.fakenewschallenge.org

http://www.fakenewschallenge.org


1/3 of the population would like a computer saying:

"I love you"

2/5th of the population believe “AI can behave

morally”

1/4th of the population believes “AI will take over

the world”.

recent public opinion study on AI by Pega

I love you …


People assign intentionality to systems whose internal workings they do not understand.

Philosopher, Deniel Dennet

"the computer says no", "the computer is not cooperative today", "he must be cleaned," "my computer loves to crash"


3 fundamental rights of democracy are threatened:

1.Freedom of choice

2. Access to information

3.Equality of opportunity

European CommissionReport on trustworthy AI

July, 2019 Suggested readings and echoed or homogenized shared opinions provide us with a tunneled view based on our own patterns.


Make a 1 milion loan!

Are you sure?!


Don’t make that loan.

Why?

KEEP THE HUMAN IN THE LOOP


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AIx

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artificial intelligence needs explanation ai · 2020-06-12 · copyright librt 2019 3 fundamental...

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