Utah is on a roll?
Utah House Passes Bill to Further Limit Warrantless Collection of Electronic Data
More positive news out of Utah this week. Mike Maharrey explains:
… the Utah House unanimously passed a bill that would require police to get a warrant before accessing data transmitted through an electronic communication service. The proposed law would not only increase privacy protections in Utah; it will also hinder the expansion of the federal surveillance state.
Rep. Craig Hall (R-West Valley City) introduced House Bill 87 (HB87 ) on Jan 19. The proposed law would prohibit law enforcement agencies from accessing electronic information or data transmitted through a provider of an electronic communication service. In practice, this tightens up the existing law to ensure police must get a warrant before accessing communication service provider networks in order to intercept data.
The proposed law also makes some technical changes to warrant reporting procedures.
On March 5, the House Judiciary Committee approved HB87 by a 7-0 vote. Yesterday, the full House passed the bill with a vote of 72-0.
Read more on Tenth Amendment Center.
(Related)
https://www.pogowasright.org/ut-suspect-has-a-5a-right-to-not-give-up-unlock-code-to-cell-phone/
UT: Suspect has a 5A right to not give up unlock code to cell phone
Defendant had a Fifth Amendment right to not give up the unlock code to his cell phone. Utah declines to apply the foregone conclusion exception to the Fifth Amendment to attempt to require a suspect to give up his cell phone unlock code. The state’s comment on it at trial as an inference of guilt was reversible error. State v. Valdez, 2021 UT App 13, 2021 UT App LEXIS 14 (Feb. 11, 2021)
Read excerpts from the opinion on FourthAmendment.com
Read worthy.
https://www.newyorker.com/tech/annals-of-technology/who-should-stop-unethical-ai
Who Should Stop Unethical A.I.?
In computer science, the main outlets for peer-reviewed research are not journals but conferences, where accepted papers are presented in the form of talks or posters. In June, 2019, at a large artificial-intelligence conference in Long Beach, California, called Computer Vision and Pattern Recognition, I stopped to look at a poster for a project called Speech2Face. Using machine learning, researchers had developed an algorithm that generated images of faces from recordings of speech. A neat idea, I thought, but one with unimpressive results: at best, the faces matched the speakers’ sex, age, and ethnicity—attributes that a casual listener might guess.
… As Hanna argued that voice-to-face prediction was a line of research that “shouldn’t exist,” others asked whether science could or should be stopped. “It would be disappointing if we couldn’t investigate correlations—if done ethically,” one researcher wrote. “Difficult, yes. Impossible, why?”
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