0:00:17.491,0:00:22.457
Almost one year ago -[br]it was February, I believe, 2019 -

0:00:22.457,0:00:26.391
I was browsing in the Internet,[br]and I saw this.

0:00:27.989,0:00:30.137
Women built the tech industry.

0:00:30.357,0:00:32.305
Then they were pushed out.

0:00:33.775,0:00:36.323
Pretty powerful headline, don't you think?

0:00:37.283,0:00:38.776
Actually, this is true.

0:00:39.326,0:00:42.137
Women were at the forefront[br]of the tech industry

0:00:42.137,0:00:47.021
back when technologist jobs[br]were considered menial, akin to typists.

0:00:47.391,0:00:51.964
In 1946, the US military[br]selected a team of people

0:00:51.964,0:00:54.260
to work on the world's first computer.

0:00:54.440,0:00:55.819
Guess what.

0:00:55.819,0:00:58.069
More than 50 percent were women.

0:00:58.789,0:01:01.732
The lady in the picture[br]is Margaret Hamilton.

0:01:02.132,0:01:07.232
She was the head of the coding team[br]that charted Apollo 11's path to the moon.

0:01:07.851,0:01:12.841
Another most renown lady [br]of those times was Grace Hopper.

0:01:13.171,0:01:17.819
Grace Hopper was a US Admiral[br]who invented the first compiler.

0:01:18.389,0:01:21.395
It was actually her programming[br]and coding skills

0:01:21.395,0:01:26.667
that enabled the United States[br]to model the impact of the atomic bomb.

0:01:27.837,0:01:29.797
Let's look at some numbers.

0:01:30.717,0:01:35.023
Thirty-seven percent is the estimated,[br]by the US federal government,

0:01:35.023,0:01:37.135
number of women, percentage of women,

0:01:37.135,0:01:42.318
that were taking computer studies[br]between the '70s and the '90s.

0:01:43.088,0:01:45.325
This was actually the peak.

0:01:46.125,0:01:48.620
Thirty-seven percent, early '90s,

0:01:48.620,0:01:53.902
was the peak in the percentage of women[br]who attended computer science fields.

0:01:54.642,0:01:57.776
At that time, the conditions[br]of working in the tech industry

0:01:57.776,0:01:59.750
were not so glamorous,

0:01:59.750,0:02:03.189
but the women were known[br]for their meticulous work ethic

0:02:03.189,0:02:04.965
and their attention to detail.

0:02:05.511,0:02:08.682
One of the great anecdotal[br]stories of that time

0:02:08.682,0:02:14.530
was how Grace Hopper[br]identified the first-ever computer bug.

0:02:15.120,0:02:21.514
She traced a glitch in the system[br]to a moth trapped in a relay wire.

0:02:22.124,0:02:27.127
Today in the US, there's an annual[br]conference every year named after her

0:02:27.127,0:02:29.234
to celebrate women in computing.

0:02:30.004,0:02:31.590
What happened afterwards

0:02:31.590,0:02:34.307
is that the computer industry[br]became more lucrative.

0:02:34.407,0:02:37.163
The tech jobs were of a bit better status

0:02:37.163,0:02:38.601
and higher paid.

0:02:38.771,0:02:42.314
So the tech companies[br]were looking to find the best profile

0:02:42.314,0:02:44.529
that would fit an engineering job.

0:02:44.529,0:02:47.955
But they didn't know,[br]because the coding skill was really new.

0:02:48.025,0:02:52.100
So what they did is they developed[br]a personality test,

0:02:52.340,0:02:54.750
a personality test[br]that would help them identify

0:02:54.750,0:02:57.760
what a good computer engineer[br]would look like.

0:02:57.990,0:03:01.260
That personality test[br]was actually favoring people

0:03:01.260,0:03:05.449
who were not so social,[br]who were not so extroverted.

0:03:05.679,0:03:08.842
It was the birth of the geeky profile.

0:03:08.842,0:03:10.194
(Laughter)

0:03:10.194,0:03:15.404
A computer engineer[br]could only be nerdy and antisocial.

0:03:15.404,0:03:19.824
That was the industry demographic[br]from then on, okay?

0:03:19.824,0:03:23.587
So what we know today as a stereotype,

0:03:23.587,0:03:27.834
of the male who's nerdy[br]and anti-social as a programmer,

0:03:27.834,0:03:33.251
was actually part of this vicious circle[br]that started at that time.

0:03:33.811,0:03:36.788
As the personality test favored more men,

0:03:36.788,0:03:40.284
then the industry got more[br]representation of men,

0:03:40.284,0:03:42.731
then that fueled the public perception

0:03:42.731,0:03:45.865
that computer engineering[br]is only suitable for men.

0:03:46.095,0:03:48.119
So where we are today.

0:03:49.039,0:03:53.957
In 2018, it was March, I think -[br]March 8th, International Women's Day -

0:03:53.957,0:03:57.013
the European Commission announced a study.

0:03:57.283,0:04:00.023
The study was named[br]"Women in the digital age,"

0:04:00.023,0:04:02.735
and, actually, it has[br]a lot of concerning facts

0:04:02.735,0:04:06.834
about the representation of women[br]in the European tech industry.

0:04:06.834,0:04:10.279
So today, there are four times more men

0:04:10.279,0:04:14.861
taking up computer-related fields[br]or tech-related fields.

0:04:15.931,0:04:17.772
Based on the same study,

0:04:17.772,0:04:23.024
out of 1,000 female graduates[br]of universities in Europe,

0:04:23.024,0:04:26.684
24 are studying in tech-related fields,

0:04:26.684,0:04:30.809
but then only six[br]end up working in a digital job.

0:04:31.479,0:04:34.224
For every 1,000 of male graduates,

0:04:34.224,0:04:39.230
49 end up working in a digital job.

0:04:40.000,0:04:41.734
What's even more concerning

0:04:41.734,0:04:46.835
is that women are leaving the digital jobs[br]more often than men.

0:04:47.435,0:04:50.133
Actually, in 2015, it was measured

0:04:50.133,0:04:54.769
that nine percent of women[br]left the industry.

0:04:55.159,0:04:59.847
The same year, the percentage[br]for men was 1.2 percent.

0:05:00.477,0:05:04.899
And this happened[br]at the age between 30 and 44,

0:05:04.899,0:05:07.304
which is, of course,[br]the primary working age.

0:05:07.473,0:05:12.947
At the same time, it is the age that most[br]Europeans are having their first child.

0:05:13.357,0:05:15.905
So the gender gap is increasing.

0:05:16.075,0:05:18.894
If we talk about digital entrepreneurship,

0:05:19.124,0:05:21.470
the European Startup Monitor

0:05:21.940,0:05:26.907
measures the startups[br]that have female founders to 14.8 percent.

0:05:27.695,0:05:30.317
Startups that have only female founders

0:05:30.317,0:05:34.776
got only five percent of the global[br]venture capital investment.

0:05:34.776,0:05:38.108
And the average investment[br]on the female-founded startups

0:05:38.108,0:05:40.828
is dropping every year[br]of the last five years.

0:05:43.078,0:05:46.666
I graduated computer engineering[br]in the early '90s.

0:05:46.896,0:05:50.788
It was the time that the gender gap[br]was not so visible in the numbers,

0:05:50.788,0:05:56.323
but, of course, the unconscious[br]and sometimes conscious bias was there.

0:05:57.063,0:06:02.148
I had to sign up for volunteering work[br]at the computer center in the university

0:06:02.148,0:06:06.672
and, of course, miss a lot of parties[br]outside of school to be taken seriously

0:06:06.672,0:06:09.990
by my professors and my classmates.

0:06:10.711,0:06:13.994
You see, I was not the geeky profile,

0:06:13.994,0:06:20.513
and femininity at that time was actually[br]adversely related to brilliance.

0:06:21.783,0:06:27.547
When I got into the professional life,[br]things seemed easier and better.

0:06:27.856,0:06:30.408
I was learning everyday,[br]I was growing every day,

0:06:30.408,0:06:32.634
and I had respect from my colleagues.

0:06:33.050,0:06:37.203
And then I decided that it was about time[br]to move up with my career,

0:06:37.203,0:06:39.932
to move on, to develop my career.

0:06:39.932,0:06:42.792
"You can't - you are a mother.

0:06:43.432,0:06:45.723
What would your husband say?

0:06:46.083,0:06:48.015
Who would take care of your kids?

0:06:48.395,0:06:50.978
How will you manage the long hours?"

0:06:51.918,0:06:57.513
Surprisingly, nobody asked me before[br]how I managed the long hours

0:06:57.513,0:07:01.388
when I had to deliver a complex project[br]with quality and on time.

0:07:01.868,0:07:04.454
(Applause)

0:07:07.334,0:07:12.273
My husband is in this room today,[br]and I guess he's smiling.

0:07:12.273,0:07:14.466
(Applause)

0:07:18.850,0:07:23.066
Today, I am thankful[br]for this painful moment of truth

0:07:23.066,0:07:25.386
because it made me stronger,

0:07:25.386,0:07:27.707
because it fueled my determination.

0:07:27.807,0:07:29.919
So I worked really hard to make it happen.

0:07:29.919,0:07:32.524
I worked really hard[br]to make my dream come true.

0:07:33.198,0:07:35.025
And, of course,[br]there was advancements,

0:07:35.025,0:07:36.752
and, of course, there were setbacks,

0:07:36.752,0:07:40.142
and, of course, I was faced[br]with unconscious bias many times,

0:07:40.142,0:07:42.657
which was not only gender,[br]I have to tell you.

0:07:42.897,0:07:45.188
In the years of the Greek crisis,

0:07:45.188,0:07:51.669
I had to to overcome the unconscious bias[br]that the Greeks are incompetent, okay?

0:07:51.849,0:07:56.038
Very incompetent and lazy[br]and corrupt, okay?

0:07:56.038,0:07:57.840
(Laughter)

0:07:57.840,0:07:59.151
But I made it.

0:07:59.151,0:08:04.497
And today, today I feel[br]I need to share this story

0:08:04.621,0:08:11.358
because I want to empower young women[br]to follow up on her dreams.

0:08:12.016,0:08:14.561
So this evolution that I have lived,

0:08:14.561,0:08:19.480
because technology has been growing[br]exponentially over the past years,

0:08:19.480,0:08:21.536
was not an inclusive one.

0:08:21.536,0:08:26.313
More than 50 percent of the population[br]has not been part of that.

0:08:26.803,0:08:30.824
But is it only gender that was left out?

0:08:31.064,0:08:32.649
It was not only gender,

0:08:32.649,0:08:37.598
because diversity doesn't have to do[br]only with whether you are male or female.

0:08:37.848,0:08:39.944
It has to do with age.

0:08:40.034,0:08:41.880
It has to do with ethnicity.

0:08:41.950,0:08:43.648
It has to do with color.

0:08:43.698,0:08:46.651
It has to do with religion or culture.

0:08:48.501,0:08:51.681
Take for example the older people today.

0:08:51.781,0:08:53.923
Today, we are living in a time

0:08:53.923,0:08:56.426
that we are having for the first time

0:08:56.426,0:08:59.830
five generations at the same time[br]in the workplace.

0:09:00.710,0:09:04.535
In the next five years, analysts expect

0:09:04.535,0:09:09.216
that 25 percent of the workforce[br]will be over the age of 55.

0:09:10.126,0:09:13.607
How do we make sure[br]these people are not excluded?

0:09:13.877,0:09:17.053
How do we drive[br]cross-generational collaboration?

0:09:17.313,0:09:20.687
How do we give opportunities[br]for them to thrive

0:09:20.687,0:09:23.917
and to leverage[br]the benefits of digitization?

0:09:24.267,0:09:26.811
Why is this discussion[br]happening right now?

0:09:26.811,0:09:30.941
It is happening first of all because[br]there is a business case around it.

0:09:31.321,0:09:33.051
Based on McKinsey,

0:09:33.051,0:09:37.621
companies who have top female executives

0:09:37.621,0:09:40.637
and have good diversity[br]rate in their executive boards

0:09:40.637,0:09:45.337
are 35 percent more likely[br]to be more profitable.

0:09:45.977,0:09:49.293
The European Union has estimated[br]the annual productivity costs

0:09:49.293,0:09:52.069
of this nine percent of women[br]leaving the workplace

0:09:52.069,0:09:54.845
to be 16 billion euros.

0:09:55.895,0:09:59.464
But it's not only the country[br]or the company financials.

0:10:00.914,0:10:02.951
Diversity is an action.

0:10:03.361,0:10:05.418
Inclusion is a culture.

0:10:05.628,0:10:07.416
Belonging is a feeling.

0:10:08.126,0:10:11.639
And the need for all three[br]is deeply human.

0:10:11.639,0:10:13.142
When we empower people,

0:10:13.142,0:10:15.206
when we give them the opportunity

0:10:15.206,0:10:18.656
to bring their strengths[br]and the best talents,

0:10:19.136,0:10:20.968
then we all win.

0:10:21.861,0:10:24.681
And today, it's even more than that.

0:10:26.778,0:10:29.817
With the advance[br]of technology, such as AI,

0:10:29.817,0:10:34.778
a lot of things, a lot of activities -[br]like, for example, recruiting -

0:10:34.778,0:10:36.800
have been outsourced.

0:10:37.080,0:10:40.112
So in 2018, there was a Reuters article

0:10:40.112,0:10:45.485
that revealed that one of the major[br]e-commerce companies globally,

0:10:46.165,0:10:50.410
a machine-learning-fueled recruiting bot,

0:10:51.580,0:10:55.996
was disqualifying women over men.

0:10:57.562,0:10:59.003
Τhe machine-learning algorithm

0:10:59.003,0:11:04.327
used CVs that were gathered in the company[br]for a period of more than 10 years

0:11:04.327,0:11:07.119
to learn and identify patterns

0:11:07.119,0:11:10.621
that would drive[br]what would a good candidate be.

0:11:10.947,0:11:14.796
Of course, those CVs[br]were coming mostly from men.

0:11:15.906,0:11:20.136
So the bot, when it was finding[br]words like "women" in the CV -

0:11:20.136,0:11:23.282
like participation[br]in a women's soccer club -

0:11:23.282,0:11:25.693
it would disqualify that CV.

0:11:28.723,0:11:31.788
Biases that exist in the data

0:11:32.278,0:11:35.934
in a world that is becoming[br]more and more data-driven,

0:11:35.934,0:11:39.533
in a world the decisions[br]are taken based on this data

0:11:39.873,0:11:44.015
can be reproduced, can be reinforced,

0:11:44.015,0:11:47.366
can become a self-fulfilling prophecy.

0:11:47.816,0:11:50.792
I am Greek; I was raised in Greece.

0:11:50.792,0:11:56.045
I was educated with the principles coming[br]from the ancient Greek philosophers.

0:11:56.915,0:12:01.114
And as old-fashioned[br]as this might look today,

0:12:01.114,0:12:05.060
it is actually some ethical values,[br]some timeless values

0:12:05.060,0:12:07.103
that we need all to go back to.

0:12:07.103,0:12:09.660
We need a new ethical coach[br]in technology today

0:12:09.660,0:12:11.322
that would earn trust,

0:12:11.322,0:12:13.964
that would help technology be trustworthy,

0:12:13.964,0:12:19.009
that would make us believe[br]that it will be used in a fair way.

0:12:20.839,0:12:24.998
So when we talk about new technologies,[br]like artificial intelligence,

0:12:24.998,0:12:29.868
we want them to be fair,[br]to not have biases.

0:12:29.868,0:12:31.601
We want them to be transparent

0:12:31.601,0:12:35.650
for us to understand how data are used,[br]how decisions are made.

0:12:36.020,0:12:40.517
We want them to work[br]in a safe and reliable way.

0:12:40.517,0:12:43.502
We want them to respect our privacy.

0:12:43.962,0:12:47.627
We want them to make sure[br]they include and they empower everyone.

0:12:48.455,0:12:51.660
So we know we need inclusive technology.

0:12:51.660,0:12:56.445
To do that, I would refer[br]to Simon Sinek's talk "Start with why."

0:12:57.095,0:12:58.969
"Why" matters.

0:12:59.519,0:13:01.670
Why are we building technology?

0:13:02.030,0:13:05.600
Technology is here[br]to enhance our capabilities,

0:13:05.600,0:13:08.388
to help us make smarter decisions faster,

0:13:08.448,0:13:11.297
to help us make less mistakes.

0:13:11.527,0:13:13.038
"Why" matters.

0:13:13.498,0:13:14.769
Then "who."

0:13:14.769,0:13:17.459
Who's involved[br]in building the technology?

0:13:17.659,0:13:21.970
We need more diverse teams[br]building the technology.

0:13:22.185,0:13:23.862
"Who" matters.

0:13:25.132,0:13:27.419
And then, finally, "how,"

0:13:28.529,0:13:33.574
how transparent we are about[br]the way we are building technology.

0:13:34.154,0:13:36.390
How are we using the data?

0:13:36.390,0:13:38.279
How are the machines learning?

0:13:38.599,0:13:40.149
"How" matters.

0:13:40.709,0:13:42.613
In an evolving digital world,

0:13:42.613,0:13:47.112
as we welcome robots,[br]holograms, artificial intelligence

0:13:47.112,0:13:49.521
and more and more automation,

0:13:49.747,0:13:53.291
people are getting more and more scared[br]what the future will be.

0:13:53.851,0:13:56.320
What will be the future of jobs?

0:13:56.320,0:13:58.789
What will be the future of us as humans?

0:13:59.059,0:14:01.286
The future is as good as we make it.

0:14:01.376,0:14:04.866
Technology has always been here[br]to enhance our capabilities.

0:14:04.866,0:14:07.554
If you ask me, what would be[br]our future as humans?

0:14:07.554,0:14:09.387
I would tell you,

0:14:09.387,0:14:12.860
we need to get back to our core identity,

0:14:13.840,0:14:16.201
to the core of who we are,

0:14:16.201,0:14:17.722
and that's empathy.

0:14:19.272,0:14:21.568
Empathy is not a "nice to have."

0:14:22.144,0:14:28.697
Empathy is absolutely essential[br]to us as humans into the future.

0:14:29.371,0:14:32.306
Empathy is about overcoming biases.

0:14:33.056,0:14:35.052
It's about respecting each other.

0:14:35.142,0:14:37.697
It's about understanding[br]or trying to understand.

0:14:37.797,0:14:39.835
It's about listening.

0:14:39.885,0:14:41.974
It's about showing compassion.

0:14:43.344,0:14:45.697
It's about offering support.

0:14:47.297,0:14:51.049
The future will be good

0:14:51.049,0:14:57.472
if it only includes and embraces[br]every talent on earth.

0:14:58.452,0:15:03.030
The future will be good[br]if it only relies on empathy.

0:15:05.000,0:15:09.702
In a digital world that would respect[br]every individual's talents,

0:15:09.702,0:15:15.676
that would empower everybody[br]to get high, higher, highest,

0:15:15.676,0:15:18.732
these headlines will not be there anymore.

0:15:19.562,0:15:22.151
Today, I have one ask of you.

0:15:22.891,0:15:26.060
Let's make this decision collectively.

0:15:27.020,0:15:29.051
Inclusion starts with I.

0:15:29.851,0:15:32.421
It starts with each and every one of us.

0:15:32.861,0:15:37.653
Let's act, let's listen, let's understand,[br]let's respect each other.

0:15:37.653,0:15:39.474
Let's empathize.

0:15:39.774,0:15:44.098
Let's make this evolving[br]digital world a more inclusive place.

0:15:44.168,0:15:45.664
Thank you.

0:15:45.717,0:15:48.264
(Applause)