www.EazyML.com

Guidelines For Augmented Intelligence

www.EazyML.com

What is Augmented Intelligence?

Augmented Intelligence mines data to

extract insights about the dynamics of your

business – automatically - to assist humans

in learning and decision making. It's about

productive collaboration of human

intelligence and artificial intelligence.

Why is it Useful for the Enterprise?

1. Many enterprises have vast amounts of

data in the cloud and on-prem, yet their

decisions are made with incomplete,

sometimes anecdotal, information -

partially because not all the relevant

information is readily available, and partly

because the terabytes of information that's

available is opaque, making it difficult to

mine the insights and the clues that lie

buried deep, hidden from executives. BI

tools require manual effort, can miss out

key insights.

www.EazyML.com

EazyML’s Augmented Intelligence makes

insights quicker, and more importantly,

comprehensive - this makes enterprises

discover insights about their business

dynamics that otherwise may have gone

unnoticed by analysts – potentially,

worth millions of $s to P&L That’s why

Gartner projects Augmented Intelligence

to generate a whopping $2.9T of value-

add for businesses by EOY:

Gartner: Gartner Says AI Augmentation

Will Create $2.9 Trillion of Business Value

And why’s EazyML’s Augmented

Intelligence unique? Because it generates

a confidence score for each insight to

make data-driven decision-making

actionable. Please do review my recent

article on Forbes about it - hot off the

press: https://www.linkedin.com/posts/

deepak-dube-phd-7217a0131_council-

post-transparent-machine-learning-

activity-6753436239724081152-SrVj

If that was known, then the edits to the

training data bias could be specific and

targeted, fixing the issue. This is precisely

what EazyML’s explainable AI does,

explaining the reasons for each prediction;

very importantly, it accompanies the

reasons with a confidence score so as to

not mislead.

offending records to

the training data to

influence the new

model; it fixes the

error, but now another

error surfaces that was

earlier correct – the

struggle for data

scientists goes on.

What was precisely the

reason why the model

predicted incorrectly?

2. As we mentioned above, enterprises have

tons of data. Data, unfortunately, has biases

– referred to as the training data bias. The

model suffers from biases, making incorrect

predictions. If only an ML platform could

help find out these biases in advance!

EazyML’s Augmented Intelligence does

exactly that. It mines the data to extract the

insights (the rules of how predictors

combine to determine the value of an

outcome). Subject matter experts can study

the insights, convince themselves that it

follows the principles of science, the best

area practices, and if it doesn’t, work on the

training data to remove the bias, before

using it.

There are additional ways in which EazyML

battles data bias comprehensively –

importance of predictors and comparing test

to training data.

www.EazyML.com

3. Let’s say the model still makes predictionsthat are incorrect. Data scientists add the

EAZYML MAKES YOUR DATA FROM PUZZLING TO INSIGHTFUL IN MINUTES

Why EazyML for Augmented Intelligence?

1. Which predictors are important, how

important, how do they interplay with

each-other (sequence and threshold) –

comprehensive intelligence from data:

Typically, most ML systems use Shapleys

to accurately display which predictors are

important, and how important are they,

for an outcome; EazyML goes a step

beyond to assign thresholds to each

predictor so that the analysts understands

the precise range in which the results

hold, vitally important for data-driven

decision-making.

2. Lucid explanation with Rules and

Thresholds – easy to understand in a form

humans typically relate to: According to

Jim Guszcza, Chief Data Scientist at

Deloitte, humans comprehend

explanations best as rules-n-thresholds;

EazyML displays it in that format via GUI

and API.

www.EazyML.com

3. Correctness of explanation evaluated by

Confidence Score – makes it actionable: A

prediction has to be accompanied by a

confidence score so that it doesn’t mislead.

ML platforms don’t always do that. When

they do, they report on the accuracy of the

model – RMSE or kappa, for instance – as a

measure of how well does the model

predicts, in general. What’s the equivalent

measure of how accurate is the explanation

for a prediction? Or for the insight? No

such measure exists. EazyML has had to

work hard to invent a measure, its

confidence score, and put it through a large

number of experiments to validate its

efficacy. Without the all-important score,

the explanations derived by Augmented

Intelligence is academic, not actionable.

4. Fits well in the enterprise workflow by

integrating with your existing BI tools using

standard APIs – easy to trial, easy to

deploy: To democratize Augmented

Intelligence, you need an ML platform

that’s easy to use, intuitive to work.