Imagine knowing which buildings would catch on fire, which people wouldn’t pay their loans, or to solve overcrowding in California’s prisons. Only a few years ago, such ideas might have seemed outside the realm of possibility. Now, the Bayes Impact Fellowship is trying to transform these ideas into realities- with the help of big data, of course.
Bayes Impact launched their fall 2014 fellowship last week, with an impressive roster of talent to support the fellows’ grand ambitions. The founders alone are impressive- Paul Duan, formerly Eventbrite’s lead data scientist, Andrew Jiang, a non-profit veteran and Eric Liu, a former Thomvest Ventures analyst founded the fellowship earlier this year. The fellowship’s mentors and scientists hail from Airbnb, Ayasdi, Intuit, LinkedIn, Netflix, OpenTable, Salesforce.com, and Square- to name but a few.
Already, non-profits are leveraging big data to provide insights and spark change. We’ve previously reported on the use of big data in conflict prevention initiatives, and the Data Science for Social Good Fellowship, ran by Obama’s former chief data scientist. The Bayes Impact team want to see a greater scope of work in this field. “Almost all technology companies, no matter their size, use data to their and their users’ advantage: not-for-profits and governments can and should do the same thing,” Zachary Townsend, founder of the startup Standard Treasury and a Bayes Impact board member, told VentureBeat.
The fellowship lasts between 6 and 12 months, and the fall intake will be 20-25 fellows working on 10-12 projects. The Summer pilot project saw fellows tackling fraud prevention and assessing credit worthiness for the microloan initiative Zidisha, and tackling overcrowding in the Californian prison system. The aforementioned project to determine which buildings are most likely to catch on fire (using metrics from fire safety inspections) is already a confirmed project for the Fall programme.
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“[The mentors at Bayes Impact are] pretty carefully selected, for the particular problems, not just somebody who has been working as a data scientist, but really knows about what problems in particular the fellow might be working on,” said summer fellow William Lane. Let’s hope the right fellows are teamed with the right experts, so that these lofty ideas can be translated into actionable change.
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