Interesting. I wonder if the volume of devices is typical?
https://www.bespacific.com/a-peek-inside-the-fbis-unprecedented-january-6-geofence-dragnet/
A Peek Inside the FBI’s Unprecedented January 6 Geofence Dragnet
Wired – “The FBI’s biggest-ever investigation included the biggest-ever haul of phones from controversial geofence warrants, court records show. A filing in the case of one of the January 6 suspects, David Rhine, shows that Google initially identified 5,723 devices as being in or near [One block or one mile? Bob] the US Capitol during the riot. Only around 900 people have so far been charged with offenses relating to the siege. The filing suggests that dozens of phones that were in airplane mode during the riot, or otherwise out of cell service, were caught up in the trawl. Nor could users erase their digital trails later. In fact, 37 people who attempted to delete their location data following the attacks were singled out by the FBI for greater scrutiny. Geofence search warrants are intended to locate anyone in a given area using digital services. Because Google’s Location History system is both powerful and widely used, the company is served about 10,000 geofence warrants in the US each year. Location History leverages GPS, Wi-Fi, and Bluetooth signals to pinpoint a phone within a few yards. Although the final location is still subject to some uncertainty, it is usually much more precise than triangulating signals from cell towers. Location History is turned off by default, but around a third of Google users switch it on, enabling services like real-time traffic prediction…”
By over correcting for bias you lose the ability to detect bias?
Using sensitive data to prevent discrimination by artificial intelligence: Does the GDPR need a new exception?
There’s a new paper by Marvinvan Bekkum and Frederik Zuiderveen Borgesius: Using sensitive data to prevent discrimination by artificial intelligence: Does the GDPR need a new exception?
Abstract
Organisations can use artificial intelligence to make decisions about people for a variety of reasons, for instance, to select the best candidates from many job applications. However, AI systems can have discriminatory effects when used for decision-making. To illustrate, an AI system could reject applications of people with a certain ethnicity, while the organisation did not plan such ethnicity discrimination. But in Europe, an organisation runs into a problem when it wants to assess whether its AI system accidentally discriminates based on ethnicity: the organisation may not know the applicants’ ethnicity. In principle, the GDPR bans the use of certain ‘special categories of data’ (sometimes called ‘sensitive data’), which include data on ethnicity, religion, and sexual preference. The proposal for an AI Act of the European Commission includes a provision that would enable organisations to use special categories of data for auditing their AI systems. This paper asks whether the GDPR’s rules on special categories of personal data hinder the prevention of AI-driven discrimination. We argue that the GDPR does prohibit such use of special category data in many circumstances. We also map out the arguments for and against creating an exception to the GDPR’s ban on using special categories of personal data, to enable preventing discrimination by AI systems. The paper discusses European law, but the paper can be relevant outside Europe too, as many policymakers in the world grapple with the tension between privacy and non-discrimination policy.
Access the full paper at ScienceDirect or download the pdf (free).
Lend me your ears…
https://www.bespacific.com/listen-notes/
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