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Week of February 28, 2022
CITP News Highlights

Brookings Institution Article Cites CITP Research Into Dark Patterns in Campaign Fundraising

In arguing for the regulation of deceptive emails and web pages that trick donors in giving more money to political campaigns, attorney Daniel N. Jellins, in a Brookings piece, points to work by CITP researchers that exposed various forms of dark patterns campaigns use to raise cash. To see their research ...
 
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Collaboration Between DuckDuckGo and CITP Referenced in New York Times

An article about conservative and right wing groups favoring DuckDuckGo over Google notes that DuckDuckGo is working with CITP “to study how to mitigate disinformation through information boxes” and other tools. For more information … 
 
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CITP Alumnus Joshua Kroll Says Facial Recognition Systems Should Be Presumed Biased Unless Proven Otherwise

In a policy brief for the ACM Technology Policy Council, Kroll details the unreliability of facial recognition tools and the devastating harms they can pose to personal privacy and autonomy in public spaces. For details on Kroll's piece, “ACM Tech Brief: Facial Recognition," ...
 
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Deadline Approaching for PIT-UN Fellowship

The deadline to apply to CITP’s Public Interest Technology Summer Fellowship is Monday, February 28. The program gives 16 to 20 university students first-hand experience working on technology policy, and features a boot camp.  Students must attend a school in the PIT-University Network to apply. For details ...

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Upcoming Virtual Events

CITP Seminar

Lauren Kilgour – Electronic Ankle Monitors and the Politics of Criminal Justice Technology
This talk examines why electronic ankle monitors look and feel the way they do. The form factor of a digital monitoring technology is often overlooked, as we tend to focus on its data collection ...
 
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There will be no CITP seminar today, due to Spring Recess. 

CITP Seminar

Kristian Lum – Closer Than They Appear: A Bayesian Perspective on Individual-level Heterogeneity in Risk Assessment
Using Bayesian hierarchical models applied to a large longitudinal dataset from the court system, we analyze variation in individual-level probabilities of failing to appear for court.
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