Transcriber: Gm Choi Reviewer: Lucas Kaimaras
It’s that time of week again, Friday night, pandemic style.
Αround this time last year, like most of you,
I decided to settle in for a cozy night on the couch with the TV remote
instead of any wild Friday night adventures with COVID hanging around.
As I browse through Netflix.
I passed the movie I watched yesterday,
a cheesy rom-com recommended by a friend,
completely not my style.
I quickly moved past it and landed on the Recommended For You category.
I eagerly began browsing through this one,
since this is where I found most of the good movies I ended up liking.
Suddenly, though, I sat bolt upright.
Since yesterday, almost all of the movies in my Recommended For You
were now rom-coms, each one cheesier than the last.
I’m sure I’m not the only one that feels
like Netflix seems to know me better than myself sometimes,
but this scenario got me thinking.
How did Netflix know what I watched yesterday
and how were they able to recommend movies similar to that one for me to watch today?
To answer that question,
we need to think about what an algorithm is.
This is a pretty popular buzzword many of you might’ve heard before,
but what actually is an algorithm?
An algorithm is simply a list of steps to solve a problem.
In the technology world algorithms consist of computer implementable commands
that allow you to perform computations.
In fact, algorithms are what Netflix uses
to generate the movies in the Recommended For You category
that I had such a bitter experience with last year.
Oftentimes, algorithms that are used
in artificial intelligence or machine learning,
where we try to get the computer to imitate human behavior,
are created using data.
The best way to mimic human behavior
is to analyze how we humans behave through our actions.
For instance, when Netflix writes their movie recommendation algorithm,
they likely harness data
from the millions of people who use their services
to predict how humans behave when it comes to watching movies.
For example, if one user such as myself, watches the first To All the Boys movie,
then they’ll likely watch the second one and then the third one.
What Netflix does is compile this trend of movie watching into a big database,
which it then sends to the computer to look for patterns.
Most people, after they’ve seen the first part of a movie series,
will likely watch the second.
The algorithm’s job is to recognize this pattern from the data,
use it to create a model of what the correct output is, given an input,
and then apply this model to other users.
This is one of the ways in which Netflix’s algorithm
produces the movies we see in the Recommended For You section.
Which is why those movies are often similar to ones we’ve seen previously,
whether that is by content, genre or actors.
The idea is that computers can learn on their own
when given instructions to follow or data to mine,
and thus mimic human behavior.
But what this idea doesn’t encompass
is the fact that computers are fundamentally not humans.
As such, they lack many of the things we humans take for granted
when it comes to making decisions
such as common sense, ethics and logical reasoning.
This is where the problem arises.
Before algorithms existed, humans made all the decisions.
Now computers either make decisions for us
or heavily influence how we make decisions.
Whether it’s something simple, like deciding which movie to watch next
or something more serious,
like determining the fiscal credit worthiness of an individual
to give them a loan.
But these computers aren’t learning from thin air.
They’re learning from us.
Machine learning algorithms rely on human data.
They need data to understand how we humans behave and act
in terms of concrete, hard behaviors.
They then use this data to replicate human behavior and make tasks simpler.
The problem arises when the data that is being passed to these machines is flawed.
This is when we run the risk of teaching a computer the wrong thing,
or in other words, create a flawed algorithm.
The fundamental cause behind why such a phenomenon occurs, is this:
computers rely on data and data comes from human behavior.
However, human behavior often represents what is,
rather than what should be,
and what is is often racist, sexist, xenophobic and so on.
As recent events in the US have illustrated, our society
holds systems of institutionalized discrimination and oppression
that have been at play for generations.
As such, it isn’t shocking
that any data we collect from people in our society will be biased,
whether that is against certain ideas, beliefs, groups of people or traditions,
The machines we are more heavily relying on day by day are biased.
Why?
Because they’re learning from us and we are biased.
It may seem like this issue is so far removed from society.
“What does bias in tech algorithms have to do with us?”
Well, aside from influencing the movies Netflix recommends for us,
there are other uses for algorithms that can impact our lives quite heavily.
One such example is employee hiring algorithms.
A recent prime example of how this algorithm became flawed
lies with tech giant Amazon.
Taken together, Amazon’s global workforce is over 60% male,
with 75% of managerial positions also held by males.
The data that was fed to their employee hiring algorithm
allowed it to learn that a woman were a minority at this company,
and thus it penalized any resumé it came across
that contained the word “woman” in it,
leading to gender bias in how employees were hired at this top company.
Another example can be seen with offender risk assessments.
What are these?
Well, US judges use automated risk assessments to determine things
like bail and sentencing limits for individuals accused of a crime.
These assessments rely on large data sets that go back ages
and include variables like arrest records, demographics,
financial stability and so on.
The algorithms that these assessments are based on
have been found to be inherently biased against African-Americans
by recommending things such as detainments,
longer prison sentences and higher bails for them,
in comparison to an equally likely to reoffend white counterpart.
When we think about it, this isn’t entirely surprising,
though completely atrocious.
There has been a long history of marginalisation and racism in our society,
that the data set to use to create this algorithm most likely reflect.
As such, the algorithm has learned to recommend actions
that continue to oppress African-Americans
because that is what the data set it was trained on shows.
A more close to home example can be seen with facial recognition software.
We use this algorithm every time we unlock our iPhones.
However, what we may not know is that facial recognition,
though outwardly proven to be over 90% accurate,
is actually not this accurate for everyone.
As the image illustrates, facial recognition varies in accuracy
depending on the demographic of the user
and is most inaccurate for darker females,
illustrating the bias hidden within this algorithm.
Though companies that make the software have since announced commitments
to modify testing and improve data collection,
it’s important to note
the widespread prevalence of facial recognition in our society today,
and as such, the damage that this flawed algorithm has already caused.
Not only do we use it in our phones,
but law enforcement, employment offices, airports, security firms and more.
All use facial recognition in multiple capacities.
What if law enforcement incorrectly flagged an innocent female as a criminal
while failing to identify the real perpetrator?
What if this happened over and over and over again?
Can we imagine the terrible effects that would have?
It may seem bleak to begin scratching the surface
of how technology can be taught to imitate human fallacies and bias,
creating long lasting and far reaching negative impacts.
But there are ways we can improve the situation.
One way is to widen the breadth of data used to create these algorithms.
If we give computers more varied and diverse data to learn from,
we can help ensure that they are learning correct patterns within human behavior
that accurately reflect what we want them to do.
Another way is to rally together in demanding increased transparency
when it comes to creating these algorithms.
Within the past few decades, tech giants like Facebook and Google
have harnessed tremendous amounts of personal data
to create powerful machine learning technology
with little insight or oversight from the public and government.
This means that only a small subset of people
oversaw the creation of something that a large subset of society is using,
which can inherently lead to bias.
We can fix this by instituting policies
that govern when and how personal data can be used,
as well as by incorporating the work of diversity and equity leaders
in the creation of these algorithms.
A final way we can improve the situation is to explore more deeply
into the idea of teaching machines societal ethics.
For instance, widely held beliefs like “innocent until proven guilty”,
common sense reasoning and the elimination of logical or emotional fallacies.
It is more important now than ever
for us to educate ourselves about bias in machine learning algorithms,
a dangerous phenomenon that gives us a dark insight
into how technology, often thought to be created by humans for humans,
can turn ugly.
As we’ve seen, our society has systems of bias embedded within it.
It is our job to ensure that this inequity and unfairness
does not widen and spread into the realm of technology,
specifically within artificial intelligence.
I hope for you all today to leave not only with new knowledge
about an issue within the technology world,
but also a heart filled with empathy
towards those oppressed by systems in place,
and what we can do to combat those systems
and create meaningful change using computer science.
Technology is a powerful field.
One that only seems to be getting stronger and stronger
as our society pivots and remains in a remote workspace.
It is incredibly important for us to recognize and understand this power
so we can use it to our advantage.
It is up to us to ensure
that technology and artificial intelligence is working for us,
not against us.
Thank you.