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Content type: Explainer
Behind every machine is a human person who makes the cogs in that machine turn - there's the developer who builds (codes) the machine, the human evaluators who assess the basic machine's performance, even the people who build the physical parts for the machine. In the case of large language models (LLMs) powering your AI systems, this 'human person' is the invisible data labellers from all over the world who are manually annotating datasets that train the machine to recognise what is the colour…
Content type: Examples
New workplace technologies are generating mountains of data on workers despite a lack of clarity over how the data is used and who owns it. In offices, smart badges track interactions and sensors track fitness and health; in trucks sensors monitor drivers' performance in the name of safety. In the US state of Illinois, between July and October 2017 26 lawsuits were filed by employees alleging that their employers had violated the state's Biometric Information Privacy Act, which requires a…
Content type: Examples
In January 2019 the UK's Information Commissioner's Office announced it was investigating an incident in which the food service company Deliveroo reported that some of its customers had complained they were charged up to £1,000 for orders they had not placed. Customers have used social media to report similar problems in the past. Deliveroo claimed that the problem was not its own security but customers who had reused user names and passwords that had been exposed by data breaches on other…
Content type: Examples
For some months in 2017, in one of a series of high-risk missteps, Uber violated Apple's privacy guidelines by tagging and identifying iPhones even after their users had deleted Uber's app. When Apple discovered the deception, CEO Tim Cook told Uber CEO Travis Kalanick to cease the practice or face having the Uber app barred from the App Store.
External Link to Story
https://www.nytimes.com/2017/04/23/technology/travis-kalabnick-pushes-uber-and-himself-to-the-precipice.html
Content type: Case Study
Gig economy jobs that depend on mobile applications allow workers’ movements to be monitored, evaluated, and exploited by their employers.
The so-called “gig economy” has brought to light employers’ increasing ability and willingness to monitor employee performance, efficiency, and overall on-the-job conduct. Workplace surveillance of gig economy workers often happens without employees’ awareness or consent. This is especially evident in the app-based gig economy, where apps act both as an…