Reimagining Trust in AI

[Music]

you may have heard the same

common sense is not so common it happens

every day around us where people break

simple rules

and exemplify this notion for instance

don’t text while you drive only cross

the street when it’s green light

apparently not so common in new york

which is the very

first thing i learned from there we all

have cognitive biases

as to what common sense is and this

notion

also applies between human intelligence

and artificial intelligence

here’s an example how would you label

the sentiments of this conversation

good morning your flight is rescheduled

to 3 am

tomorrow perfect

is it positive neutral or negative to

you

in natural language processing the

algorithm will put

a positive label because it recognizes

the positive words like

good and perfect but it might not be

able to capture any

negative signal in fact detecting

sarcasm or

irony is one of the most challenging

topics in this field

then when responses from ai machines

don’t make sense

would you trust ai it is time to

re-imagine

the trusting ai to start with

it’s worthy to see what ai really is and

what we should expect from it

let’s do a little experiment think about

the phrase artificial intelligence

what is the first image that pops up in

your mind

is it something like these

every time when we talk about ai

we use images with those

octarian brainy sci-fi robots

that have little to do with ai

we should really stop using those

because

ai actually looks like this

artificial intelligence is artificial

after all

at its core ai is ultimately a process

of optimization

based on given inputs it learns the

patents from given information

and makes optimal decisions through

computations

but when ai is overly humanized or

sometimes

overhyped they seem to have the magic

power

beyond human intelligence however

is it a realistic expectation we’re

approaching but generally

we’re not there yet now let’s take a

step back

and think about why is that let’s

compare how

us humans make decisions versus how ai

does

here’s a decision for you to make say

that you

and your partner have been living in

seattle for five years

today you finally got a job offer from

your dream company

but it requires you to relocate to

boston

how would you decide there’s definitely

a long list to consider

your personal development your

relationship

timing expenses lifestyle and on and on

if you have kids 10 more items added to

your list

as you can imagine decision making is a

fairly complicated process for us

then how do ai algorithms decide

it’s utterly simple finding the value of

either

minimum cost or maximum rewards through

mathematical

equations and for this process to work

all the complexity of human

considerations has to be simplified

and quantified into several metrics or

sometimes

even a single number therefore it’s

really unrealistic

to expect a perfect transition from the

decision making of human beings

to the decision making of machines

so far ai is a hardcore optimizer with

strong computing power

and also with limitations in its

optimization

mechanism now let’s just say we do have

a perfect

algorithm today can you fully trust it

now

don’t forget there’s a condition

everything is processed

upon given inputs for machines these

inputs

are the only learning materials and the

foundation

of any trustworthy outputs

that’s why we need to fit in sufficient

unbiased

and representative data in the first

place which is

quite challenging to be honest because

in reality

we may not have enough data there are

always random factors

the data may now fully describe the real

world and the world is changing

dynamically so naturally there are

limitations

in the given inputs with that being said

with limitations in ai algorithms and in

training data

how could we trust ai well

never just trust but validate

there are a bunch of examples that ai

could bring troubles in real cases

if without validation i noticed the

project in healthcare

the motto was to predict whether a

patient would have

negative or positive diagnosis by

looking at their x-ray images

at first the predictions were extremely

accurate

after a while the doctors find out why

and it’s because

some images were taken from portable

x-ray machines

and some were taken from regular

machines

what does that mean think about what

kind of patients

get x-rayed from portable machines

the patients who couldn’t make it to the

regular machine and those are the

patients who are more likely to have

unfavorable diagnosis

turned out that this model is merely

looking at the type

of the x-ray device rather than the

actual pathology

as a matter of fact it is so common in

healthcare that patients who have severe

symptoms

get different treatments than those who

are less sick but apparently

this should not be the only evidence for

diagnosing

that’s why when given a model we should

always

validate ask what drives the model

predictions

and whether those drivers make sense to

you

and validation exercises can be done by

not only ai practitioners

it can be done by anyone for example my

lovely younger sister she’s a dancing

teacher

once i asked her to think about this and

she looked at me

how am i supposed to do is there an

easier way to validate ai

i told her yes there are ways for you to

do it easily and critically

for example validate with corner cases

corner cases adjust the situations that

occur outside of the normal cases

if a technology works in both normal and

corner cases

we know we could trust it to a larger

extent

a couple of months ago i was researching

a very popular

object detection technology it is an

algorithm that can locate and identify

objects

in images or videos so that we can do

image annotation

face recognition and so forth so i was

testing the model by

throwing out some images let’s take a

look

take the typical rush hour afternoon on

new york friday

we can see that in this busy picture the

algorithm captured a lot of items

they are located in bounding boxes with

labels

indicating the categories and the

respective probabilities

essentially those probabilities are

telling us how certain the algorithm is

so here the model is 80 sure that

in this yellow box this is a car it also

detected

the pedestrians traffic lights etc

this model is by no means perfect but it

did a fair job in general

now let’s take it up to another level by

testing some corner cases

what if we apply a picture with a

different angle

for example from an overhead view like

this one

when the shapes of items twist when we

look at them from above

would the model still hold up

now i got a bit confused you can see

that in the center

the people and the chairs were correctly

and confidently detected

while in this corner the motor view that

person as a teddy bear

it also means the flag the picture in

the frame and the table

those are the variance objects that the

model is supposed to detect

but it wasn’t able to identify them in

weird angles

let’s try another case what if we

fit in an image with incomplete

information

for example a partially covered cat face

would the detector figure this out

somehow it totally missed the cute cat

and magically detected

three ties instead i know the kitty

feels the same

astonished as you can see by

thinking critically asking what-if

questions and testing corner

cases it’s almost effortless to validate

and the bonus point here is that being

critical of science

inspires science just like we saw it

didn’t work with the covered cat face

in july 2020 the u.s national institute

of standards and technology

has tested 89 facial recognition

algorithms

and find that the error rate spiked

up to 50 percent for faces wearing musks

plenty of developer teams were actively

working on this during

covet 19 and six months later there was

a huge

improvement in january 2021

the u.s department of homeland security

tested a new algorithm

and it can identify airline passengers

with masks

96 percent of the time

wherein musks is a corner case for

facial recognition

and being aware of this corner case

accelerated

technology advancement

yet still a lot of times ai developers

might not be aware of the corner cases

or the potential consequences

it really relies on everyone to dive

deeper

and validate from different perspectives

it’s not only

one ai expert needing validation from

other ai experts

but also inputs from people outside of

this field

even if your expertise is in something

completely different

if you are a musician a nurse a lawyer

a factory worker or someone in sports

your input

matters start asking questions towards

the ai technologies

that tries to impress you or the data

conclusion that tries to convince you

we’re at a time when everyone is close

to data

close to ai technologies while we’re

also at a time

when people and ai developers are far

apart

so i’m asking for both sides to come

closer and bridge the gap

for ai practitioners in order to collect

more

corner cases and be more aware of the

impact

feedback loops and platforms should be

created and easily accessible to the

audience

the audience on the other hand is also

responsible to participate not only just

as a pure consumer a passenger

but as a feedback provider this teamwork

really matters to help ai make more

sense

and more importantly it matters for

advancing ai

as a product as a service as a policy

and

as a society now let’s go back to the

beginning of this talk

reimagining trust in ai

just like how trust is built up between

us humans

we spend some time getting to know each

other through both glorious and

imperfections

test each other with corner cases

subconsciously or consciously

see each other experiencing fun time and

down time

and work on challenges together

then eventually you know if you trust

that person

it’s the same with ai we build trust

with ai through

understanding what it really is

acknowledging its limitations

through interactions and validations and

more crucially

through collaborations trustworthy ai

is always the result of joint efforts

we need collective human intelligence to

progress

machine intelligence we’re all in this

revolutionary disruption

together and that is the idea

worth spreading thank you