How I'm fighting bias in algorithms | Joy Buolamwini

312,131 views ・ 2017-03-29

TED


Please double-click on the English subtitles below to play the video.

00:12
Hello, I'm Joy, a poet of code,
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on a mission to stop an unseen force that's rising,
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a force that I called "the coded gaze,"
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my term for algorithmic bias.
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Algorithmic bias, like human bias, results in unfairness.
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However, algorithms, like viruses, can spread bias on a massive scale
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at a rapid pace.
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Algorithmic bias can also lead to exclusionary experiences
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and discriminatory practices.
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Let me show you what I mean.
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(Video) Joy Buolamwini: Hi, camera. I've got a face.
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Can you see my face?
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No-glasses face?
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You can see her face.
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What about my face?
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I've got a mask. Can you see my mask?
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Joy Buolamwini: So how did this happen?
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Why am I sitting in front of a computer
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in a white mask,
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trying to be detected by a cheap webcam?
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Well, when I'm not fighting the coded gaze
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as a poet of code,
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I'm a graduate student at the MIT Media Lab,
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and there I have the opportunity to work on all sorts of whimsical projects,
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including the Aspire Mirror,
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a project I did so I could project digital masks onto my reflection.
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So in the morning, if I wanted to feel powerful,
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I could put on a lion.
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If I wanted to be uplifted, I might have a quote.
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So I used generic facial recognition software
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to build the system,
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but found it was really hard to test it unless I wore a white mask.
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Unfortunately, I've run into this issue before.
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When I was an undergraduate at Georgia Tech studying computer science,
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I used to work on social robots,
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and one of my tasks was to get a robot to play peek-a-boo,
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a simple turn-taking game
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where partners cover their face and then uncover it saying, "Peek-a-boo!"
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The problem is, peek-a-boo doesn't really work if I can't see you,
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and my robot couldn't see me.
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But I borrowed my roommate's face to get the project done,
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submitted the assignment,
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and figured, you know what, somebody else will solve this problem.
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Not too long after,
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I was in Hong Kong for an entrepreneurship competition.
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The organizers decided to take participants
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on a tour of local start-ups.
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One of the start-ups had a social robot,
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and they decided to do a demo.
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The demo worked on everybody until it got to me,
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and you can probably guess it.
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It couldn't detect my face.
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I asked the developers what was going on,
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and it turned out we had used the same generic facial recognition software.
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Halfway around the world,
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I learned that algorithmic bias can travel as quickly
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as it takes to download some files off of the internet.
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So what's going on? Why isn't my face being detected?
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Well, we have to look at how we give machines sight.
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Computer vision uses machine learning techniques
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to do facial recognition.
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So how this works is, you create a training set with examples of faces.
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This is a face. This is a face. This is not a face.
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And over time, you can teach a computer how to recognize other faces.
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However, if the training sets aren't really that diverse,
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any face that deviates too much from the established norm
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will be harder to detect,
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which is what was happening to me.
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But don't worry -- there's some good news.
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Training sets don't just materialize out of nowhere.
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We actually can create them.
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So there's an opportunity to create full-spectrum training sets
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that reflect a richer portrait of humanity.
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Now you've seen in my examples
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how social robots
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was how I found out about exclusion with algorithmic bias.
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But algorithmic bias can also lead to discriminatory practices.
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Across the US,
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police departments are starting to use facial recognition software
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in their crime-fighting arsenal.
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Georgetown Law published a report
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showing that one in two adults in the US -- that's 117 million people --
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have their faces in facial recognition networks.
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Police departments can currently look at these networks unregulated,
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using algorithms that have not been audited for accuracy.
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Yet we know facial recognition is not fail proof,
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and labeling faces consistently remains a challenge.
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You might have seen this on Facebook.
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My friends and I laugh all the time when we see other people
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mislabeled in our photos.
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But misidentifying a suspected criminal is no laughing matter,
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nor is breaching civil liberties.
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Machine learning is being used for facial recognition,
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but it's also extending beyond the realm of computer vision.
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In her book, "Weapons of Math Destruction,"
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data scientist Cathy O'Neil talks about the rising new WMDs --
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widespread, mysterious and destructive algorithms
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that are increasingly being used to make decisions
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that impact more aspects of our lives.
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So who gets hired or fired?
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Do you get that loan? Do you get insurance?
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Are you admitted into the college you wanted to get into?
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Do you and I pay the same price for the same product
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purchased on the same platform?
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Law enforcement is also starting to use machine learning
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for predictive policing.
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Some judges use machine-generated risk scores to determine
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how long an individual is going to spend in prison.
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So we really have to think about these decisions.
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Are they fair?
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And we've seen that algorithmic bias
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doesn't necessarily always lead to fair outcomes.
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So what can we do about it?
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Well, we can start thinking about how we create more inclusive code
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and employ inclusive coding practices.
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It really starts with people.
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So who codes matters.
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Are we creating full-spectrum teams with diverse individuals
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who can check each other's blind spots?
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On the technical side, how we code matters.
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Are we factoring in fairness as we're developing systems?
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And finally, why we code matters.
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We've used tools of computational creation to unlock immense wealth.
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We now have the opportunity to unlock even greater equality
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if we make social change a priority
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and not an afterthought.
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And so these are the three tenets that will make up the "incoding" movement.
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Who codes matters,
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how we code matters
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and why we code matters.
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So to go towards incoding, we can start thinking about
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building platforms that can identify bias
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by collecting people's experiences like the ones I shared,
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but also auditing existing software.
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We can also start to create more inclusive training sets.
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Imagine a "Selfies for Inclusion" campaign
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where you and I can help developers test and create
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more inclusive training sets.
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And we can also start thinking more conscientiously
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about the social impact of the technology that we're developing.
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To get the incoding movement started,
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I've launched the Algorithmic Justice League,
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where anyone who cares about fairness can help fight the coded gaze.
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On codedgaze.com, you can report bias,
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request audits, become a tester
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and join the ongoing conversation,
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#codedgaze.
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So I invite you to join me
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in creating a world where technology works for all of us,
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not just some of us,
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a world where we value inclusion and center social change.
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Thank you.
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(Applause)
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But I have one question:
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Will you join me in the fight?
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(Laughter)
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(Applause)
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