Name: Algorithmic Bias and Fairness: Crash Course AI #18
Uploaded: 2020-03-30T03:15:08.000Z
Duration: 11 min 20 s
Description: Thousands of YouTube videos with English-Chinese subtitles! Now you can learn to understand native speakers, expand your vocabulary, and improve your pronunciation...

Hi, I'm Jabril and welcome back to CrashCourse AI.

Adverbs of degree

Algorithms are just math and code, but algorithms are created by people and use our data, so

Basic passive voice

biases that exist in the real world are mimicked or even exaggerated by AI systems.

Our brains try to take shortcuts by finding patterns in data.

So if you've only seen small, tiny dogs, you might see a Great Dane and be like “Whoa

This doesn't become a problem unless we don't acknowledge exceptions to patterns

or unless we start treating certain groups of people unfairly.

As a society, we have laws to prevent discrimination based on certain “protected classes” (like

gender, race, or age) for things like employment or housing.

So it's important to be aware of the difference between bias, which we all have, and discrimination,

And knowing about algorithmic bias can help us steer clear of a future where AI are used

There are at least 5 types of algorithmic bias we should pay attention to.

First, training data can reflect hidden biases in society.

For example, if an AI was trained on recent news articles or books, the word “nurse”

is more likely to refer to a “woman,” while the word “programmer” is more likely

And you can see this happening with a Google image search: “nurse” shows mostly women,

while “programmer” mostly shows mostly men.

We can see how hidden biases in the data gets embedded in search engine AI.

Of course, we know there are male nurses and female programmers and non-binary people doing

For example, an image search for “programmer 1960” shows a LOT more women.

But AI algorithms aren't very good at recognizing cultural biases that might change over time,

and they could even be spreading hidden biases to more human brains.

t's also tempting to think that if we just don't collect or use training data that

categorizes protected classes like race or gender, then our algorithms can't possibly

But, protected classes may emerge as correlated features, which are features that aren't

explicitly in data but may be unintentionally correlated to a specific prediction.

For example, because many places in the US are still extremely segregated, zip code can

A record of purchases can be strongly correlated to gender.

And a controversial 2017 paper showed that sexual orientation is strongly correlated

with characteristics of a social media profile photo.

Second, the training data may not have enough examples of each class, which can affect the

For example, many facial recognition AI algorithms are trained on data that includes way more

examples of white peoples' faces than other races.

One story that made the news a few years ago is a passport photo checker with an AI system

to warn if the person in the photo had blinked.

But the system had a lot of trouble with photos of people of Asian descent.

Being asked to take a photo again and again would be really frustrating if you're just

trying to renew your passport, which is already sort of a pain!

Or, let's say, you got a cool gig programming a drone for IBM… but it has trouble recognizing

your face because your skin's too dark… for example.

Third, it's hard to quantify certain features in training data.

There are lots of things that are tough to describe with numbers.

Like can you really rate a sibling relationship with a number?

You love them, but you hate how messy they are, but you like cooking together, but you

In many cases, we try to build AI to evaluate complicated qualities of data, but sometimes

we have to settle for easily measurable shortcuts.

One recent example is trying to use AI to grade writing on standardized tests like SATs

and GREs with the goal to save human graders time.

Good writing involves complex elements like clarity, structure, and creativity, but most

So, instead, these AI focused on easier-to-measure elements like sentence length, vocabulary,

and grammar, which don't fully represent good writing… and made these AIs easier

Some students from MIT built a natural language program to create essays that made NO sense,

but were rated highly by these grading algorithms.

These AIs could also potentially be fooled by memorizing portions of “template” essays

to influence the score, rather than actually writing a response to the prompt, all because

of the training data that was used for these scoring AI.

Fourth, the algorithm could influence the data that it gets, creating a positive feedback

A positive feedback loop basically means “amplifying what happened in the past”… whether or

An example is PredPol's drug crime prediction algorithm, which has been in use since 2012

in many large cities including LA and Chicago.

PredPol was trained on data that was heavily biased by past housing segregation and past

So, it would more frequently send police to certain neighborhoods where a lot of racial

Arrests in those neighborhoods increased, that arrest data was fed back into the algorithm,

and the AI would predict more future drug arrests in those neighborhoods and send the

Even though there might be crime in neighborhoods where police weren't being sent by this

AI, because there weren't any arrests in those neighborhoods, data about them wasn't fed

While algorithms like PredPol are still in use, to try and manage these feedback effects,

there is currently more effort to monitor and adjust how they process data.

So basically, this would be like a new principal who was hired to improve the average grades

of a school, but he doesn't really care about the students who already have good grades.

He creates a watchlist of students who have really bad grades and checks up on them every

week, and he ignores the students who keep up with good grades.

If any of the students on his watchlist don't do their homework that week, they get punished.

But all of the students NOT on his watchlist can slack on their homework, and get away

with it based on “what happened in the past.”

This is essentially what's happening with PredPol, and you can be the judge if you believe

Finally, a group of people may mess with training data on purpose.

Subtitles ListPlay Video

Algorithmic Bias and Fairness: Crash Course AI #18

recognize

critical

controversial

positive