80,000 Hours Podcast31 Tammi 2018

#18 - Ofir Reich on using data science to end poverty & the spurious action-inaction distinction

Ofir Reich started out doing math in the military, before spending 8 years in tech startups - but then made a sharp turn to become a data scientist focussed on helping the global poor.

At UC Berkeley’s Center for Effective Global Action he helps prevent tax evasion by identifying fake companies in India, enable Afghanistan to pay its teachers electronically, and raise yields for Ethiopian farmers by messaging them when local conditions make it ideal to apply fertiliser. Or at least that’s the hope - he’s also working on ways to test whether those interventions actually work.

Full post about this episode, including a transcript and relevant links to learn more.

Why dedicate his life to helping the global poor?

Ofir sees little moral difference between harming people and failing to help them. After all, if you had to press a button to keep all of your money from going to charity, and you pressed that button, would that be an action, or an inaction? Is there even an answer?

After reflecting on cases like this, he decided that to not engage with a problem is an active choice, one whose consequences he is just as morally responsible for as if he were directly involved. On top of his life philosophy we also discuss:

* The benefits of working in a top academic environment
* How best to start a career in global development
* Are RCTs worth the money? Should we focus on big picture policy change instead? Or more economic theory?
* How the delivery standards of nonprofits compare to top universities
* Why he doesn’t enjoy living in the San Francisco bay area
* How can we fix the problem of most published research being false?
* How good a career path is data science?
* How important is experience in development versus technical skills?
* How he learned much of what he needed to know in the army
* How concerned should effective altruists be about burnout?

Keiran Harris helped produce today’s episode.

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