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[…] from Google users who had opted-in to share their data for research. Then they correlated the decreases in mobility tied to state-level policies with changes in the number of reported COVID-19 cases. The project produced the following insights: State-level emergency declarations resulted in a 9.9% reduction in time spent away from places of residence. […]

[…] infections prior to the onset of symptoms. To ensure that this data sharing project minimized potential privacy risks, both Empatica and the Stanford Medicine team took a number of steps, including:  Establishing limits on the sharing and use of personal health information. Using a researcher-friendly device, Empatica’s E4, that prevents the collection of geolocation […]

[…] like to integrate an AI component, that would be considered high-risk AI. Tielemans also noted that, once a system is qualified as high-risk, then providers acquire a number of obligations on training models, record keeping, among others.  On the DSA, Tielemans pointed out that the proposal is geared towards providers of online intermediary services. […]

[…] being regulated can be localized, or have its geographic location readily inferred. Email addresses, despite being personal information, give no indication of the owner’s location, while residential phone numbers were straightforward to relate to a particular state when the law was drafted in 1991.  Thus, while differing state telemarketing laws can present compliance costs […]

[…] the rights of access, correction, deletion, and portability, the law follows existing standards and incentivizes covered entities to maintain data in less identifiable formats.  As a growing number of states begin to pass their own consumer privacy laws, concerns about interoperability may begin to emerge. For instance, definitional differences regarding what constitutes sensitive data, […]

[…] “personally identifiable information.” Category 1 information includes personal data “that an individual may use in a personal, civic, or business setting,” including a SSN, a driver’s license number, passport number, unique biometric information, physical or mental health information, private communications, etc. Category 2 information includes personal data that may present a “privacy risk” to […]

[…] were employed in training algorithms to develop the “Iruda” AI model, without any efforts by ScatterLab to delete or encrypt users’ personal information, including their names, mobile phone numbers, and addresses. Additionally, 100 million KakaoTalk messages from women in their twenties were added to the response database with “Iruda” programmed to select and respond […]

[…] underneath algorithms are human biases and data that is often biased. That’s the key learning we want kids to take away.” Each of the panelists shared a number of terrific resources and recommendations for parents and educators, which we have listed and linked to below, along with a few of our own. Watch the […]

[…] techniques, that makes a decision or facilitates human decision-making” (New Jersey S1943) would likely include a wide range of traditional statistical data processing, such as estimating average number of vehicles per hour on a highway to facilitate automatic lane closures in intelligent traffic systems. This would place an additional, significant requirement for conducting complex […]

The recent amendments to Japan’s data protection law contain a number of new provisions certain to alter – and for many foreign businesses, transform – the ways in which companies conduct business in or with Japan.