Webinar: Needles in Haystacks: Using Tech for Good

July 8th, 2020

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 July 8th, 2020  
 Free Event

Webinar: Needles in Haystacks: Using Tech for Good

Event submitted on Thursday, June 25th 2020, approved by Content Team

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July 8th, 2020
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* Due to COVID, it is most likely that this event will be online - but please make sure! The situation is constantly changing...
This event is FREE!
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This event is part of a series managed by Aspen Tech Policy Hub

 Our Review

The Aspen Institute‘s Tech Policy Hub has organized a bunch of excellent virtual events this year. This webinar takes a look at: “Using Tech for Good”; something we can all agree on!

- Review written by Henry Dalziel on Thursday, June 25th 2020.
- If you would like to edit or ammend facts in my review please either send us a message or connect with me via LinkedIn.

Conference Event Summary

The following description was either submitted by the Conference Organizer on Thursday, June 25th 2020, or created by us.

The growing power of artificial intelligence and machine learning provides many opportunities to augment human-centered decisions and point out discrepancies and/or patterns of discrimination that individuals cannot easily see. How can we harness AI to find the needles in the proverbial haystacks and advance causes for social good?

Join Aspen Tech Policy Hub Fellows as they showcase their projects focused on Needles in Haystacks: Using Tech for Good. Following the presentations of the projects, a keynote speaker (to be announced) will give further remarks. The projects to be presented are:

1) Combating Domestic Terrorism. An FBI study on active shooters shows that there were on average three distinct bystanders who observed concerning behaviors about the suspect prior to their attack. Unfortunately, nearly 60 percent of bystanders did not report what they knew, resulting in many missed opportunities to save lives. Anjana Rajan will demo one way to solve this low reporting rate: through the use of a digital escrow.

2) Fair Algorithmic Housing Loans. Mortgage lenders increasingly use machine learning algorithms to make loan approval and pricing decisions. While there are some benefits to this automation, how can we ensure fairness among historically marginalized and underbanked populations? Samara Trilling recommends that state lending regulators define a fairness metric for mortgage algorithms and pilot automated fair lending tests.

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