
An AI Hammer in Search of a Nail
05/17/22 • 33 min
1 Listener
It often feels like machine learning experts are running around with a hammer, looking at everything as a potential nail - they have a system that does cool things and is fun to work on, and they go in search of things to use it for. But what if we flip that around and start by working with people in various fields - education, health, or economics, for example - to clearly define societal problems, and then design algorithms providing useful steps to solve them?
Rediet Abebe, a researcher and professor of computer science at UC Berkeley, spends a lot of time thinking about how machine learning functions in the real world, and working to make the results of machine learning processes more actionable and more equitable.
Abebe joins EFF's Cindy Cohn and Danny O’Brien to discuss how we redefine the machine learning pipeline - from creating a more diverse pool of computer scientists to rethinking how we apply this tech for the betterment of society’s most marginalized and vulnerable - to make real, positive change in people’s lives.
In this episode you’ll learn about:
- The historical problems with the official U.S. poverty measurement
- How machine learning can (and can’t) lead to more just verdicts in our criminal courts
- How equitable data sharing practices could help nations and cultures around the world
- Reconsidering machine learning’s variables to maximize for goals other than commercial profit.
If you have any feedback on this episode, please email [email protected]. Please visit the site page at https://eff.org/pod208 where you’ll find resources – including links to important legal cases and research discussed in the podcast and a full transcript of the audio.
This podcast is supported by the Alfred P. Sloan Foundation's Program in Public Understanding of Science and Technology.
Music for How to Fix the Internet was created for us by Reed Mathis and Nat Keefe of BeatMower.
This podcast is licensed Creative Commons Attribution 4.0 International, and includes the following music licensed Creative Commons Attribution 3.0 Unported by their creators:
http://dig.ccmixter.org/files/djlang59/59729
Probably Shouldn't by J.Lang
http://dig.ccmixter.org/files/Skill_Borrower/41751
Klaus by Skill_Borrower
http://dig.ccmixter.org/files/airtone/58703
commonGround by airtone
http://dig.ccmixter.org/files/JeffSpeed68/56377
Smokey Eyes by Stefan Kartenberg
http://dig.ccmixter.org/files/NiGiD/62475
Chrome Cactus by Martijn de Boer (NiGiD)
It often feels like machine learning experts are running around with a hammer, looking at everything as a potential nail - they have a system that does cool things and is fun to work on, and they go in search of things to use it for. But what if we flip that around and start by working with people in various fields - education, health, or economics, for example - to clearly define societal problems, and then design algorithms providing useful steps to solve them?
Rediet Abebe, a researcher and professor of computer science at UC Berkeley, spends a lot of time thinking about how machine learning functions in the real world, and working to make the results of machine learning processes more actionable and more equitable.
Abebe joins EFF's Cindy Cohn and Danny O’Brien to discuss how we redefine the machine learning pipeline - from creating a more diverse pool of computer scientists to rethinking how we apply this tech for the betterment of society’s most marginalized and vulnerable - to make real, positive change in people’s lives.
In this episode you’ll learn about:
- The historical problems with the official U.S. poverty measurement
- How machine learning can (and can’t) lead to more just verdicts in our criminal courts
- How equitable data sharing practices could help nations and cultures around the world
- Reconsidering machine learning’s variables to maximize for goals other than commercial profit.
If you have any feedback on this episode, please email [email protected]. Please visit the site page at https://eff.org/pod208 where you’ll find resources – including links to important legal cases and research discussed in the podcast and a full transcript of the audio.
This podcast is supported by the Alfred P. Sloan Foundation's Program in Public Understanding of Science and Technology.
Music for How to Fix the Internet was created for us by Reed Mathis and Nat Keefe of BeatMower.
This podcast is licensed Creative Commons Attribution 4.0 International, and includes the following music licensed Creative Commons Attribution 3.0 Unported by their creators:
http://dig.ccmixter.org/files/djlang59/59729
Probably Shouldn't by J.Lang
http://dig.ccmixter.org/files/Skill_Borrower/41751
Klaus by Skill_Borrower
http://dig.ccmixter.org/files/airtone/58703
commonGround by airtone
http://dig.ccmixter.org/files/JeffSpeed68/56377
Smokey Eyes by Stefan Kartenberg
http://dig.ccmixter.org/files/NiGiD/62475
Chrome Cactus by Martijn de Boer (NiGiD)
Previous Episode

The Philosopher King
Computer scientists often build algorithms with a keen focus on “solving the problem,” without considering the larger implications and potential misuses of the technology they’re creating. That’s how we wind up with machine learning that prevents qualified job applicants from advancing, or blocks mortgage applicants from buying homes, or creates miscarriages of justice in parole and other aspects of the criminal justice system.
James Mickens—a lifelong hacker, perennial wisecracker, and would-be philosopher-king who also happens to be a Harvard University professor of computer science—says we must educate computer scientists to consider the bigger picture early in their creative process. In a world where much of what we do each day involves computers of one sort or another, the process of creating technology must take into account the society it’s meant to serve, including the most vulnerable.
Mickens speaks with EFF's Cindy Cohn and Danny O’Brien about some of the problems inherent in educating computer scientists, and how fixing those problems might help us fix the internet.
In this episode you’ll learn about:
- Why it’s important to include non-engineering voices, from historians and sociologists to people from marginalized communities, in the engineering process
- The need to balance paying down our “tech debt” —cleaning up the messy, haphazard systems of yesteryear—with innovating new technologies
- How to embed ethics education within computer engineering curricula so students can identify and overcome challenges before they’re encoded into new systems
- Fostering transparency about how and by whom your data is used, and for whose profit
- What we can learn from Søren Kierkegaard and Stan Lee about personal responsibility in technology.
If you have any feedback on this episode, please email [email protected]. Please visit the site page at https://eff.org/pod207 where you’ll find resources – including links to important legal cases and research discussed in the podcast and a full transcript of the audio.
This podcast is supported by the Alfred P. Sloan Foundation's Program in Public Understanding of Science and Technology.
Music for How to Fix the Internet was created for us by Reed Mathis and Nat Keefe of BeatMower.
This podcast is licensed Creative Commons Attribution 4.0 International, and includes the following music licensed Creative Commons Attribution 3.0 Unported by their creators:
http://dig.ccmixter.org/files/djlang59/59729
Probably Shouldn't by J.Lang (c) copyright 2019
http://dig.ccmixter.org/files/airtone/58703
commonGround by airtone (c) copyright 2018
http://dig.ccmixter.org/files/mwic/58883
Xena's Kiss / Medea's Kiss by mwic (c) copyright 2018
http://dig.ccmixter.org/files/Skill_Borrower/41751
Klaus by Skill_Borrower (c) copyright 2013
http://dig.ccmixter.org/files/NiGiD/62475
Chrome Cactus by Martijn de Boer (NiGiD) (c) copyright 2020
Next Episode

Securing the Vote
U.S. democracy is at an inflection point, and how we administer and verify our elections is more important than ever. From hanging chads to glitchy touchscreens to partisan disinformation, too many Americans worry that their votes won’t count and that election results aren’t trustworthy. It’s crucial that citizens have well-justified confidence in this pillar of our republic.
Technology can provide answers - but that doesn’t mean moving elections online. As president and CEO of the nonpartisan nonprofit Verified Voting, Pamela Smith helps lead the national fight to balance ballot accessibility with ballot security by advocating for paper trails, audits, and transparency wherever and however Americans cast votes.
On this episode of How to Fix the Internet, Pamela Smith joins EFF’s Cindy Cohn and Danny O’Brien to discuss hope for the future of democracy and the technology and best practices that will get us there.
In this episode you’ll learn about:
- Why voting online can never be like banking or shopping online
- What a “risk-limiting audit” is, and why no election should lack it
- Whether open-source software could be part of securing our votes
- Where to find reliable information about how your elections are conducted
If you have any feedback on this episode, please email [email protected]. Please visit the site page at https://eff.org/pod209 where you’ll find resources – including links to important legal cases and research discussed in the podcast and a full transcript of the audio.
Pamela Smith, President & CEO of Verified Voting, plays a national leadership role in safeguarding elections and building working alliances between advocates, election officials, and other stakeholders. Pam joined Verified Voting in 2004, and previously served as President from 2007-2017. She is a member of the National Task Force on Election Crises, a diverse cross-partisan group of more than 50 experts whose mission is to prevent and mitigate election crises by urging critical reforms. She provides information and public testimony on election security issues across the nation, including to Congress. Before her work in elections, she was a nonprofit executive for a Hispanic educational organization working on first language literacy and adult learning, and a small business and marketing consultant.
This podcast is supported by the Alfred P. Sloan Foundation's Program in Public Understanding of Science and Technology.
Music for How to Fix the Internet was created for us by Reed Mathis and Nat Keefe of BeatMower.
This podcast is licensed Creative Commons Attribution 4.0 International, and includes the following music licensed Creative Commons Attribution 3.0 Unported by their creators:
http://dig.ccmixter.org/files/Skill_Borrower/41751
Klaus by Skill_Borrower
http://dig.ccmixter.org/files/airtone/58703
commonGround by airtone
http://dig.ccmixter.org/files/NiGiD/62475
Chrome Cactus by Martijn de Boer (NiGiD)
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