0:02

have you ever shared something online that turned

0:04

out? to be true i in

0:07

fact not that long ago, i shared a photo

0:09

of an adorable horse named sugar who

0:11

supposedly pretends to be asleep to avoid

0:14

being that photo was shared

0:16

over

0:16

40,000 times on to and

0:18

i i thought, well, it must be true,

0:20

but as it turns out, sugar

0:23

was just taking a nap

0:26

okay so mistakes happen

0:28

but misinformation at scale can

0:30

also be really dangerous especially

0:32

when it relates our democracies

0:35

my disinformation a speech new highs

0:37

because it's not just a human problem it's

0:39

an algorithmic problem too one

0:42

that exploited by murky political forces

0:44

in your country and in mine in

0:47

all of our elections in different languages

0:49

in different political contacts our

0:51

platform is stupid enough to fight it

0:54

these companies have not allocation of resources

0:56

to protecting all actions at once

0:59

much less societies between elections

1:01

because if you're paying attention to a country

1:04

three weeks before the vote that in the die

1:06

is cast malicious people

1:08

have said a year creating

1:10

you know soccer fans eat is that the slowly

1:13

politicize

1:14

that's the harm masashi he

1:16

used to be a data engineer on facebooks

1:18

integrity team working on election

1:21

more hear more from him in a bit

1:33

it episodes on the perils and promise

1:36

of artificial intelligence or the

1:38

internet and him realize

1:40

we're leading ai builders and policy

1:42

folks who make who make more trustworthy

1:45

in the special season that doubles as mozilla

1:47

twenty twenty two internet help report

1:49

this time it's a i elections

1:52

and disinflation how can we

1:54

create healthier

1:55

the nation

2:02

reading this information is about

2:05

unraveling mysteries it's

2:07

about spot in clues and pattern

2:09

second lead you to

2:10

source

2:11

let's meet someone

2:13

who

2:14

on dismantling descend from than across

2:16

more than twenty countries and now

2:19

that an employee of twitter facebook

2:21

or tic tac just

2:23

and aren't time the founder and chief

2:25

executive of code for africa this

2:27

is africa's largest network of city the

2:30

an open data group that includes

2:32

investigative reporters that setting

2:34

groups and data scientist

2:36

whether it be as things around climates

2:38

or around religion nor around kind

2:41

of reproductive issues and gender

2:43

right

2:44

you can hire sophisticated

2:46

small agile seems

2:48

they're not able to build campaigns

2:50

for you including creating

2:53

sake accounts that kind of

2:55

reference each other and look like and and

2:57

function like coherent communities

3:00

i'm and sec people in and

3:02

build quite a bit of momentum

3:05

and it's from south africa he's

3:07

currently based in tbilisi georgia he

3:09

wants us to understand that there is a global

3:12

industry of dissent and for higher

3:14

worth tens of millions of dollars

3:17

like a well oiled machine at these networks

3:20

make fake content and lots of it

3:22

more importantly they create artificial

3:25

surges of attention on topics

3:27

the you spot and network social

3:29

media accounts out

3:31

content in a way that's designed to game

3:33

social media algorithms which

3:36

in turn it amplify these messages

3:38

eventually humans and media

3:40

organisations begin to genuinely engage

3:43

with that and this is how social media

3:45

is weaponized

3:48

like you have a military industrial complex

3:50

there is a can of disinformation

3:53

industrial complex and only

3:55

way we gain to descendants is

3:57

by d minute sizes

3:59

this information networks become more powerful

4:02

the influence the messages we here and

4:05

how we interact with one another the

4:07

corrupting democratic discourse throwing

4:10

them out of the shadows and taking down their networks

4:12

threatens their business model that

4:14

shines a light on those who better to politically

4:17

for misinformation

4:18

south africa we've seen the

4:20

developments of these kinds of networks

4:23

using xenophobia

4:25

as as can as a rallying

4:28

call we south africans believe

4:30

that nigerians and kenyans

4:32

and zimbabweans mozambicans are taking jobs

4:35

that to that's a long to them and

4:37

hitting on very many of the same trigger points

4:39

to julia in conversations in the us so

4:42

elsewhere or eastern europe they're

4:44

using of playbook that it's been proven

4:47

to work elsewhere the dressing

4:49

it in local language and and

4:51

often generating manipulated media

4:53

to supports local plans

4:56

certain set of this information adds

4:59

fuel to the fire in countries where there

5:01

are already electoral tooth religious

5:03

insurgencies

5:04

and foreign mercenaries

5:06

trying to fact check everything is whack a mole it's

5:09

very worthwhile and we need to do it and

5:11

it does not scale because

5:14

we cannot operate at the same level that these

5:16

machine generated hate machines

5:18

do

5:19

oh for africa coordinates fact checking

5:21

teams in more than a hundred and seventy news

5:23

comes across africa they're journalism

5:26

becomes training data for machine learning tool

5:28

we operate across twenty one countries

5:31

and there's probably and twenty

5:33

one countries probably half a billion people

5:36

we're the largest organization in

5:39

the space in africa take this

5:41

information would and were only ninety three people

5:44

and you have an outsized impacts i mean the

5:46

sec checking that are sick chicken sandwiches only

5:48

city people produces may be two

5:50

thousand section so yeah which is not

5:52

a big number at that in turn has

5:55

a multiplier impacted just say spoke

5:57

labels or removes either

5:59

six million posts per year

6:02

based on those two thousand sections

6:04

that could acknowledges that physicist a drop

6:06

in a sea of disinformation

6:09

why is a small non profit in africa

6:12

or him to clean up the platforms of the world's richest

6:15

internet companies

6:16

what's your social media companies to be doing

6:19

to use sites to this problem

6:21

as scale i mean this is clips

6:23

but they should be more collaboration not just

6:25

between social media companies

6:28

themselves or technology companies

6:30

the platform should be doing more sharing

6:33

and more joint problem solving to

6:35

solve a problem that ultimately they've created

6:39

these big platforms should be collaborating

6:42

with each other sharing intelligence

6:45

mitigating this information from spreading from

6:47

one channel to the other

6:48

they'll never be able to be everywhere the

6:51

time and so they

6:53

need to figure out productive

6:55

sustainable ways

6:57

collaborating with a wider sense

6:59

as sits also com of watch dogs

7:02

in the media in fact checking

7:05

in kind of political and

7:07

watchdog ecosystems that

7:09

, point as important african

7:12

countries are divorced and culture

7:13

the and spoken languages in

7:16

nigeria alone over five

7:18

hundred languages are spoken hold

7:20

for africa's team speak local languages

7:23

they , local history and geopolitics

7:26

they're building science data sets of me

7:29

it had had from the region and they

7:31

use a i to supercharge

7:33

their work the machine learning tools

7:35

that we use and the the natural language

7:37

processing tools

7:39

the to use to

7:40

not just track the use of

7:42

specific terms but understand

7:45

emerging narratives of conversations

7:47

and the site geist that almost

7:50

makes people

7:52

okay people to be susceptible

7:57

that's a coma societies

7:59

we

7:59

be blind to all of that if we

8:02

didn't have and his

8:04

machine learning to be able to analyze

8:06

millions of online articles

8:09

and how spots the trains or the outliers

8:12

so we use a lot of tools

8:16

the platforms do collaborate

8:18

with and even pay local organizations

8:21

certain has only the largest

8:24

fact checking networks are invited by platforms

8:26

to partner with them he believed the

8:28

work code for africa and other researchers

8:30

do developing and sharing a i

8:32

tools and create a positive ripple

8:34

effect when power smaller group

8:37

we need to figure out ways of cascading

8:39

down these ecosystems and creating

8:42

and as layered interactions

8:44

wage grassroots media

8:46

have access to resources and see

8:48

techniques and tools they

8:51

will concede into canada and

8:53

it was cascading up series

8:55

of more complex organizations

8:57

who can start using a eyes

8:59

the kinds of technologies that will never be

9:02

a in the reach of people operating done it's

9:04

really simple

9:05

that is big platforms like facebook credit

9:07

for the worth they're doing and misinformation even

9:10

though he thinks they could be doing more

9:12

they at least are doing things that we

9:14

can see and we can criticize

9:16

the problem of the closed channels

9:19

the signals of what sets were

9:21

it's invisible the talk social

9:29

dark souls

9:31

that sounds much spooky or than what i see

9:33

and my what's have sat with my family but

9:36

messaging apps are seized sectors of

9:38

political and electoral disinclination

9:40

and the countries from our

9:42

and to end updated that make them

9:44

more secure it also

9:46

makes them harder to study from the outside specially

9:50

when companies are not transparent

9:53

part of what was happening and radiating with the

9:55

the with a lot of attention on facebook and twitter

9:57

and all that have had some health

9:59

not come to a global

10:02

attention but for many of us ala

10:04

like an indian or than other countries that one

10:05

the dominant blackballed

10:08

that to run i'm a pro backer she's

10:10

the resource lead and cofounder of the open

10:12

source projects cattle that's

10:14

a community of technologists and researchers

10:16

in india they build machine learning

10:19

tools and datasets to understand and

10:21

respond to misinformation they

10:23

began their work three years ago

10:25

trying to crack a puzzle how

10:28

can they help people verify information

10:30

on what's that

10:31

the company was

10:33

on what up a lot of the content is in

10:35

audio visual format or you don't

10:37

necessarily have you already use of images

10:40

and voice recording that a shared in the black

10:42

phone we knew that if you wanted

10:44

to do any automation

10:46

the it around like linking

10:48

it to have fact checked report or

10:50

on thinking it to contend that has

10:52

been said in the past you have

10:54

to be able to work with the language

10:57

that content with being said in as

10:59

well as

11:00

the modality which have weathered like image video

11:03

on your for that's where

11:05

the started with oh beating the

11:07

the machine learning to have to just process

11:09

the content in the modality and the language

11:12

of that i used in in india and

11:14

every other told archiving a lot of the

11:16

contents we started archiving gone from that was

11:18

located on shot out

11:20

they started creating a data set off a condom

11:22

that had been fact checked in india

11:24

they collected those fat sacks using a technique

11:27

called scraping and the whatsapp

11:29

messages were from large public group

11:31

then they developed a suitable depository

11:34

for cross referencing sat messages with

11:36

the fact sex the

11:38

process of doing this work they were stolen

11:41

by the close connection between hate speech

11:43

and misinformation

11:45

you're collecting of feeding data

11:47

from some of these other thought and that while we

11:49

were just thought of shocked at how much heat with

11:51

there was on the platform and

11:53

though we're thinking about why platform

11:56

had not address these issues

11:58

what do that before

11:59

the opened some of the data set for a cd

12:02

thought or journalistic storytelling we wanted to filter

12:04

out the he'd be to we found other than a place on

12:06

the we need to the point in moderation reported

12:09

even the theater and we didn't have the booth to do it

12:12

the paddles like expanded to include tools

12:14

and plug ins for moderating content in their

12:16

own open datasets in hindi camel

12:19

and indian english but why

12:21

haven't the platforms address these issues

12:24

with so much now as and resource in

12:26

the hate speech and disinclination there's

12:28

there's still so much of it one

12:31

reason is language

12:33

i couldn't be more platform the that

12:35

they're just not geared to hand

12:38

the indian language and very well

12:43

let's talk about content moderators the

12:45

people whose job it is to see the most gruesome

12:48

content on the internet part

12:50

, their job involves labeling content

12:53

so becomes training data for automated

12:55

content moderation this is important

12:57

work we know from facebook whistleblowers

13:00

like francis how good that only a small

13:02

proportion of moderation happens outside

13:04

of the u s even though ninety percent

13:06

of facebook users live elsewhere in

13:09

other words most languages are under

13:11

resourced and even for english

13:13

there are many different dialects even

13:16

though a lot of indian social media users

13:18

when he was english they with you that in you very distinct

13:21

way with way lot of mixing and matching

13:23

with regional languages

13:25

and using words in ways that you

13:27

wouldn't use that an american english

13:32

here that requested

13:34

which country

13:35

the most facebook users

13:38

the answer is india the us

13:40

and indonesia but the number

13:42

of users that are placed doesn't always mean

13:45

that a platform is going to be more accountable for them

13:47

things that are done to protect elections in one place

13:50

never habit and others and

13:52

that's true for all platforms

13:55

should it be the case that

13:57

accompany has at least a million

14:00

people speaking a language that no

14:02

one in the company understands the

14:05

very least you need to have some people who

14:07

understand the language and we're paying attention to

14:09

it as their job

14:11

harm a fatty is the cofounder of

14:13

the nonprofit integrity institute in the u

14:15

s it's a new member organization

14:17

for people who work at integrity teams at

14:19

social media platforms her

14:22

work at facebook as a data into their ended

14:24

up technical tools to protect elections

14:27

i silly accurate infant

14:28

listen to voters

14:29

the also looked at real time dashboard

14:32

and what they called a war room to identify

14:34

spikes in misinformation these

14:37

can be caused by parker

14:39

well known at that the

14:41

we share button is really dangerous

14:43

or the retrieval yeah and

14:46

if a thing is we shared many times very

14:48

likely to be bad you could

14:50

imagine putting in place

14:53

sort of lake stevens for that are

14:56

, to sort of make it harder to photo

14:58

either quoted or between a retreat a retreat

15:00

that's not technically very hard the

15:03

real challenge is like arguing about weiss be

15:05

allowed to launch and

15:07

almost ninety percent the job can be to sort of diplomacy

15:10

around being allowed to do the research on

15:12

i diplomacy zahara means

15:14

they need to negotiate for changes

15:17

integrity teams are considered costs center's

15:20

at odds with other teams focused on growth

15:22

you're always to be that including a person

15:25

who points out why the easy thing

15:27

or the growth making saying may not be a good idea

15:30

it's a really awkward position to be

15:32

the company like facebook a lot

15:34

of different teams were com policies and moderation

15:37

integrity work is just one piece of the

15:39

puzzle

15:40

i think it's fair to say that in general integrity

15:43

teams are new or

15:45

and less well resourced then we would like

15:48

he said he's especially proud of

15:50

the work his team at facebook dead i'm

15:52

a twenty eight team brazilian presidential

15:54

election and us midterm elections

15:57

now that there were missed out but

16:00

he describes intense periods of work

16:03

where they grew in terms of skills and capacity

16:06

we're pulling fourteen hour shifts and

16:08

dislike windowless stinky

16:10

room you know you are like

16:13

build a tool on day one show

16:16

up on day two for your shift and

16:18

so when had upgraded it i d

16:20

five so had to the whole new tool i

16:22

did a better job you know by the

16:24

seven so i might have actually remember documentation

16:26

about how to use it

16:28

the harsh that it was rewarding when different parts

16:30

of the company would pull together

16:33

different teams around the company really

16:35

cared about it and you're able to pull the men and

16:37

say we really need you to

16:39

teach us how to use this tool that you built that we can use

16:41

it or we really need use

16:43

it seems your apps are

16:45

your products temporarily so

16:47

that it is safer and they would do it because

16:50

he really wanted to do the right thing

16:53

companies are secretive about what they do

16:55

or don't do

16:56

a fight this information saw

16:59

her says this this collaboration

17:01

with outside groups difficult even

17:03

when they could really help the

17:05

integrity institute will serve as a bridge

17:07

to the inside

17:08

every company's probably different one

17:11

we the companies are different is in the way that the you think

17:13

about the outside world how control

17:15

the are for their workers to talk to the

17:17

outside world and a

17:19

level of paranoia or

17:22

being locked down in one company freely

17:24

can surprise you if you come

17:27

from a different company harder

17:29

what the heck institute is trying to do in

17:33

really speak to that and six that and

17:35

we say that we're representing integrity workers

17:38

though that we are the one place where

17:40

and years academics and and

17:42

the restless

17:43

the world can come talk to us and amazon

17:45

seventy to channel workers are

17:47

the technique professionals for our members

17:49

into their day jobs

17:54

let me introduce you to one more person

17:57

rashid six center coordinates global contribute

18:00

the to a crowdsourced data set up

18:02

online hate speech called shape base

18:04

through an initiative called the citizen linguists

18:06

lab this is all run

18:09

by the senses or project and canada but

18:11

rossi works remotely from bangalore

18:13

india

18:14

the sitter remember slab is

18:16

the for any one across the

18:18

board that wants to contribute

18:21

and amplify and

18:23

of meant odd database

18:24

we in many cases

18:27

might lack the social and cultural

18:29

and linguistic context of things

18:32

and who better to contribute than

18:35

the locals who live in particular

18:37

setting

18:39

hippies now covers and ninety eight languages

18:42

going across many countries they

18:44

work with hundreds of universities to research

18:47

the impact of hate speech and information

18:49

particularly leading up to to elections

18:52

right he explains the connection like this

18:55

hate speech not the gun

18:57

but misinformation pulls the trigger

19:00

hate speech in itself might

19:02

not contribute to offline violence

19:04

but it kind of said

19:05

the tone and the environment else

19:07

backgrounds hostility towards a particular

19:10

community or ethnicity and

19:12

then the woman in the form

19:14

or malignant information on full

19:17

information that circulate around

19:20

that can best lead to jacksonville

19:22

and

19:23

sentinel project has documented this dynamic

19:25

in

19:25

whereas countries

19:27

in kenya the democratic

19:29

republic of congo sudan

19:32

sri lanka the me and man

19:34

their mission is to prevent mass atrocities

19:37

too early warning and cooperation the

19:40

global repository of hate speech enables

19:42

them to perform automated sightings of

19:45

the offensive term across the internet nearly

19:48

a million of them they labels rooms

19:50

that a way that lets them take the time

19:51

you're a conflict

19:53

every contribute as call a citizen linguist

19:56

and they can also help

19:57

by offering the assessment of the

19:59

offense

19:59

the midst of a particular dome which is

20:02

then calculated with all other inputs

20:04

this , us to sort of crowdsource

20:07

sentimental analysis and

20:09

acts as one part of the output ah

20:12

for the system so the

20:14

offensiveness rating kind of helps us to

20:16

understand or social understand political

20:19

environment

20:21

the data can be accessed by local and

20:24

right groups free of charge i'll

20:26

be platforms can pay for access

20:28

it's a resource to help moderate online

20:30

conversations is designed to keep

20:32

people safe

20:33

in real life

20:38

we can pardon elections taking place around

20:40

the world it is vital that

20:42

platforms get a handle on misinformation

20:45

this isn't something that anyone company can

20:47

handle alone the can't be

20:49

solved in secrecy with content moderation

20:51

algorithms are underpaid and

20:53

unprotected moderators we

20:56

need companies to practice meaningful transparency

21:00

so they can collaborate better with each other and

21:02

local groups this would empower

21:04

researchers to uncover harmful this information

21:07

that transcends platform as languages

21:09

and yet ecosystems and those

21:11

policies that platforms create for transparency

21:14

and safety during elections they

21:16

shouldn't just apply in some countries

21:18

they should apply as

21:19

the were

21:22

this is i around in original podcast

21:24

mozilla a nonprofit behind firefox

21:28

this season of iran doubles as the internet

21:30

self report you

21:32

can be more about our guests and a i

21:34

i visited internet health report that

21:36

it

21:37

i'm bridget had thanks so much for listening

21:40

for more on what can be done look at

21:42

mozilla minimum election standards for flat