Benjamin Valdez, a rideshare driver with Uber and Lyft in the Los Angeles area, used to drive seven days a week when the gig was more lucrative--but he says he makes far less per ride these days. When Valdez started driving, around nine years ago, he told me that he could earn anywhere from 60 to 85 to drive from West Hollywood to downtown Los Angeles at peak surge, a roughly 6-to-10-mile trip depending on the specific route. Now, if "the stars align," he can earn between 25 and 35 for the same trip. "It's gotten harder and harder to make money," he said. In recent years, rideshare drivers like Valdez have experienced shrinking incomes as the companies continue to increase their cut from each ride.

The backup Uber driver for a self-driving vehicle that killed a pedestrian in suburban Phoenix in 2018 pleaded guilty Friday to endangerment in the first deadly crash involving a fully autonomous car. Arizona state judge David Garbarino, who accepted the plea agreement, sentenced Rafaela Vasquez to three years of supervised probation for the crash that killed 49-year-old Elaine Herzberg. Vasquez, 49, told police that Herzberg "came out of nowhere" and that she didn't see Herzberg before hitting her on a darkened Tempe street on 18 March 2018. Vasquez had been charged with felony negligent homicide. The charge to which she pleaded could be reclassified as a misdemeanor if she completes probation. Authorities say Vasquez was streaming the television show The Voice on a phone and looking down in the moments before Uber's Volvo XC-90 SUV struck Herzberg, who was crossing with her bicycle.

Waymo starts limited trial of self-driving taxi service. The Uber driver behind the wheel during the first reported fatal collision involving a fully autonomous vehicle pleaded guilty to endangerment on Friday, and was sentenced to three years of supervised probation. In March of 2018, Elaine Herzberg was killed while walking her bike outside the lines of a crosswalk in suburban Phoenix. Driver Rafaela Vasquez, 49, was streaming a show on her phone and not watching the road at the moment of the fatal accident, authorities said. Video released by the Tempe Police Department from inside the Vasquez's Volvo XC90 SUV shows her looking down at the moment of the crash, during which the vehicle was moving at 40 miles per hour.

One evening in February last year, a 23-year-old Uber driver named Niradi Srikanth was getting ready to start another shift, ferrying passengers around the south Indian city of Hyderabad. He pointed the phone at his face to take a selfie to verify his identity. The process usually worked seamlessly. But this time he was unable to log in. Srikanth suspected it was because he had recently shaved his head.

The Uber app prompted Srikanth to try again, so he waited a few minutes and took another picture. "I was worried about bookings. We have daily targets where if we complete a certain number of bookings, we get incentives," Srikanth says. "I was anxious to log in and start driving, and not waste any time." So he tried once more.

The Jobs and Skills Summit being convened this week in Canberra sets out to address the employment challenges that Australia currently faces. But even as we respond to today's skills shortage, we must also keep an eye on the long-term consequences of Artificial Intelligence (AI) and machine learning on jobs. Fantasies in which AIs advance to the point of doing all of our jobs are seductive in some tech circles. Elon Musk has long promised fully driverless cars that will be safer than cars driven by humans. When these vehicles do arrive, they may delete the human Uber driver.

The following is an excerpt adapted from AI 2041 by Kai-Fu Lee and Chen Qiufan. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher. Artificial intelligence can perform many tasks better than people can, at essentially zero cost. This simple fact is poised to generate tremendous economic value but also to cause unprecedented job displacement -- a wave of disruption that will hit blue- and white-collar workers alike. In the future, AI will be doing everything from underwriting our loans to building our homes, and even hiring and firing us.

Abiodun Ogunyemi has been an Uber Eats delivery driver since February 2020. But since March he has been unable to work due to what a union supporting drivers claims is a racially-biased algorithm. Ogunyemi, who is Black, had submitted a photograph of himself to confirm his identity on the app, but when the software failed to recognize him, he was blocked from accessing his account for "improper use of the Uber application." Ogunyemi is one of dozens of Uber drivers who have been prevented from working due to what they say is "racist" facial verification technology. Uber uses Microsoft Face API software on its app to verify drivers' identification, asking drivers to submit new photos on a regular basis.

The growth of the "gig economy" in recent years has revolutionised the way that millions of people work. Proponents argue that the gig economy gives people more flexibility and opportunities and lowers barriers of entry to the labour market, while detractors say that it erodes workplace regulations and standards while encouraging businesses to treat workers as increasingly disposable. No matter which side of the debate you fall on, it's clear that the gig economy is here to stay. But with more and more people signing up for these flexible and freelance work arrangements, how can businesses manage them effectively? Enter "algorithmic management": the use of algorithms to oversee the efforts of human workers. As algorithmic management becomes more commonplace, it's important to understand what this practice is, the pros and cons of using it, and what the future holds.

The growth of the "gig economy" in recent years has revolutionised the way that millions of people work. Proponents argue that the gig economy gives people more flexibility and opportunities and lowers barriers of entry to the labour market, while detractors say that it erodes workplace regulations and standards while encouraging businesses to treat workers as increasingly disposable. No matter which side of the debate you fall on, it's clear that the gig economy is here to stay. But with more and more people signing up for these flexible and freelance work arrangements, how can businesses manage them effectively? Enter "algorithmic management": the use of algorithms to oversee the efforts of human workers. As algorithmic management becomes more commonplace, it's important to understand what this practice is, the pros and cons of using it, and what the future holds.