- What if our phones turned out to be racist,
misogynistic, homophobic pricks?
Oh, right.
They have been all that.
Artificial intelligence such as Siri and Alexa
are great for convenience
and offer a number of benefits,
but they also have a lot of flaws,
and discrimination is one of them.
Google, Microsoft, and Facebook
have all admitted to this,
and they are trying to perfect this tech
so we can have more ethical, humane AI in our lives.
But if the data we input into this system
comes from a society filled with biases,
how can we develop an unbiased AI?
(electronic music)
Artificial intelligence has been around
for more than 60 years, now.
The term was first coined in 1956
by computer scientist John McCarthy.
Research centers across the US
then started exploring the creation of systems
that could efficiently solve problems
and that could learn by themselves.
Machine learning algorithms have been developed
to analyze data, learn from it,
then make predictions and decisions
based on those insights.
AI started in programs used to solve
general common sense scenarios,
mathematical problems, and geometrical theorems.
It was also used to master checkers.
Today, we see it in search engines,
face recognition, social media, smart phones, cars,
and the virtual assistants we put inside our homes.
- [Google Assistant] Hi, what can I do for you?
- And while it could determine anything
from stock prices to medical diagnoses,
it can still struggle telling apart a knee from a face.
Really.
Facebook has asked users to tag their knees,
thinking they were people's faces.
Truth is,
computer scientists have much bigger challenges
than teaching these systems to process data.
You see, artificial intelligence is just like a child,
meaning they'll learn from our behavior.
The more data you feed into a machine learning algorithm,
the smarter it gets.
Unfortunately, most of the data
we're feeding these algorithms comes from Silicon Valley,
and as we know, they have a lot of issues with diversity.
(electronic music)
Companies such as Facebook, Google, IBM, and Microsoft
have realized that their algorithms are filled with biases.
The fault here is with the people who input the data,
not the AI algorithms themselves.
Think about it this way.
If you're using AI to hire someone,
you feed it data of successful candidates
you've hired in the past.
The AI will then recommend new candidates
based on that data set.
There's just one problem.
If all your past hires are mostly white men
from Ivy League schools,
then the AI will only recommend candidates
that meet that criteria,
because it's all it knows.
This isn't something I just made up.
We've seen this behavior play out time and time again.
Siri, for starters,
has provided homophobic answers
to questions relating to LGBTQ+ topics.
It's also offered offensive replies to the question,
what is an Indian,
and it's been under fire for how it responds
to sexual harassment remarks.
Then there's facial recognition,
one of the best-known applications of AI,
which has proven to single out people of color
because they're disproportionately represented
in mug shot databases.
3D software has also favored white candidates
in a contest in which more than 6,000 people
submitted their pictures
to have their attractiveness evaluated.
Out of the 44 winners, only one was dark skinned.
Another AI thought names such as Emily and Matt
were more pleasant than names such as Jamal and Ebony.
Law enforcement in Florida used software
to predict future criminals,
just like Minority Report,
except this algorithm was found
to disproportionately target black defendants
and wrongly label them as future criminals
at almost twice the rate as white defendants.
In some cases, the algorithm got it wrong.
Low-ranking white defendants
ended up committing more crime.
Then there are things like Google's algorithm
which tagged black people as gorillas,
Microsoft's lovable chatbot named Tay
who turned into a Nazi in just one week,
and online ads that have been found
to hide high-paying jobs from women.
The list just goes on and on.
What are we doing to fix this problem,
and is there even a solution?
(electronic music)
Erasing bias from databases,
it's complicated,
especially when there's no type of regulation
or standard governing the data
used to train machine learning algorithms.
So, I spoke to Shiv Singh,
founder of Savvy Matters and an expert in AI,
to get some insight on what Silicon Valley is doing
to creating more humane and ethical AI.
- There needs to be many more ethicists
and thinkers and psychologists
at the senior levels of these companies,
so that every time decisions are being made,
it has been looked at
not just through a technology lens or a business lens
or, you know, servicing a specific customer need lens,
but also through the psychology
and the ethical implications.
- Right now, every tech firm is looking for ways
to diversify its data on customers and users,
but they also need to make sure
they're being transparent with this data
and how they gather it,
because as long as humans are involved,
you can't really get the bias out.
Yet these companies have all invested heavily
in having balanced data sets.
A notable effort in this regard
is the Partnership on Artificial Intelligence
between Facebook, Amazon, Google, IBM, and Microsoft.
This partnership aims to address the ethical issues of AI
and make sure that a more diverse set of eyes
are looking at AI before it reaches the public.
Then there's the initiative from Elon Musk, OpenAI,
which aims at making AI more transparent,
expand access to it,
and prevent it from becoming a tool to do evil things.
- Everyone's learning,
and I think that's probably my biggest message,
where we need to be patient.
We need to let the technology mature,
and we need the companies behind the technologies
to have large enough data pools.
- Best case scenario,
we develop an AI that's more of an enhancement
and not a replacement for human beings.
Worst-case scenario,
AI takes over most of our jobs and rules humanity,
much like the late Stephen Hawking theorized,
only this time, the AI is a bigot.
But it all depends on us.
I don't mean just tech companies.
We, as a society, a human species,
need to be more responsible for what we teach our robots
just like we do with our kids.
So, choose love and not hate.
Hey, y'all, thank you so much for watching ConTECHtual.
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