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Ethics & AI: Equal Access and Algorithmic Bias
The potential for AI to help society is enormous. But at the same time we need to develop technology with a focus on ethics, access and fairness. This video explores the influence of AI on every aspect of life and underscores the importance of ethical oversight to prevent the creation of biased AI algorithms.
Featuring
Amanda Askell - Ethicist at Open AI
Alejandro Carrillo - Roboticist at Farmwise
Deb Raji - the Algorithmic Justice League
Kate Park - PM at Tesla Autopilot
Dr. Regina Barzilay - Professor of CS & AI at MIT
Dr. Mehran Sahami - Professor of CS & AI at Stanford
Deon Nicholas - CEO of Forethought AI
Start learning at http://code.org/
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Produced and Directed by Jael Burrows
Co-produced by Kristin Neibert
Written by Hadi Partovi, Winter Dong and Jael Burrows
Edited by Neal Barenblat
Camera by Bow Jones, Stanford Media Lab, Sand Bay Entertainment, the Clock Factory, and Vic Ferrer
. Created by Code.org.
Featuring
Amanda Askell - Ethicist at Open AI
Alejandro Carrillo - Roboticist at Farmwise
Deb Raji - the Algorithmic Justice League
Kate Park - PM at Tesla Autopilot
Dr. Regina Barzilay - Professor of CS & AI at MIT
Dr. Mehran Sahami - Professor of CS & AI at Stanford
Deon Nicholas - CEO of Forethought AI
Start learning at http://code.org/
Stay in touch with us!
• on Twitter https://twitter.com/codeorg
• on Facebook https://www.facebook.com/Code.org
• on Instagram https://instagram.com/codeorg
• on Tumblr https://blog.code.org
• on LinkedIn https://www.linkedin.com/company/code-org
• on Google+ https://google.com/+codeorg
Produced and Directed by Jael Burrows
Co-produced by Kristin Neibert
Written by Hadi Partovi, Winter Dong and Jael Burrows
Edited by Neal Barenblat
Camera by Bow Jones, Stanford Media Lab, Sand Bay Entertainment, the Clock Factory, and Vic Ferrer
. Created by Code.org.
Want to join the conversation?
- How are AI systems being trained currently? What would be the best ways to ensure that there's more representation in training sets — who makes these decisions and how can we make it more equitable?(0 votes)
- A basic logic algorithm is where it begins, and targeted learned directs the AI to gain in understanding based on trial and error. This is a nutshell explanation.(1 vote)
Video transcript
No matter what field you end up going into, it's quite likely that AI is going to
have some impact on what you're doing. The potential for AI to help society is enormous! It's something that is influencing a lot of very important decisions about
real humans and their lives. It could be used in education, to be more of an
equalizer between people. It could be used in healthcare to develop new drugs. It could be
used in science to develop new technologies. Like any technology its application
will depend on how it is utilized. At the same time, we need to think about
the risks that are associated with doing that. The consequences are huge! The kind
of artificial intelligence technology that really dominates the applications
we're seeing today is machine learning. Machine learning depends entirely
on the information that you feed it. The problem is that with real world data, there's
often information in there that you didn't intend to be in there, but is captured because of
the bias in the data collection process. So if you're building an AI to
determine who gets a home loan, or who should be charged with
a crime, it could definitely bubble up the racial biases that humans
and our current society already does. A lot of what it means to build less harmful
AI is really systems that are including the perspectives of those that are most vulnerable
or most marginalized, most likely to be hurt by the deployment of that system. In many
ways I've worried that the people who are particularly vulnerable to AI are the people who
are already underprivileged in many respects. Most people in the world
just have AI applied to them, rather than playing an active role
in guiding what AI gets applied to. Everybody you know has a computer in their pocket.
That's young people, old people, rich people, poor people. To me, that's actually quite exciting
from a democratization of technology perspective. It means that AI, powerful as it is, could theoretically be in everybody's
pockets, benefiting everybody. We should strive to make sure that things that provide
value for society can be reached to anybody. How do we give a greater voice to the
people who are being impacted by AI, to in turn be able to turn around
and impact how AI is used for them? Every time when you're looking at a new problem,
you have an opportunity to change the world. Sometimes we succeed, sometimes
we don't, but we always try. It's really critically important that we have
as many diverse perspectives as possible, influencing the development of AI. We need the participation of
more women, more people of color, to provide a different perspective and a different
lens on which problems matter and how we should approach these
problems.