Tesla assures its millions of electric car owners that their privacy "is and will always be enormously important to us". The cameras it builds into vehicles to assist driving, it notes on its website, are "designed from the ground up to protect your privacy". But between 2019 and 2022, groups of Tesla employees privately shared via an internal messaging system sometimes highly invasive videos and images recorded by customers' car cameras, according to interviews by Reuters with nine former employees. Some of the recordings caught Tesla customers in embarrassing situations. One ex-employee described a video of a man approaching a vehicle completely naked.

AI is in the social network you chat on, the engine you search with, the word processor you write with, and the camera you take pictures with. One clue is where the Fortune 50 are placing their AI bets. And one big tell is what they need training data for. "Training data is really the basis for AI," Wendy Gonzalez, the president and CEO of Samasource told me in a recent TechFirst podcast. "At the end of the day, machines need to learn how to speak, see, and hear. And they do so much like a human learns how to speak, see, and hear."

Artificial Intelligence (AI) is no longer just a projection into future uses but a part of business practices. Machine learning (ML) is a tool used by businesses for predictive modeling that is used in an array of industries, from healthcare to finance to security. The question that businesses have to address is: Are we being careful to not misuse AI by having it reinforce human biases in the training data? To get insight into the various factors that play into that assurance, Martine Bertrand, Lead AI at Samasource in Montreal shared her thoughts. Bertrand holds a Ph.D. in physics and has applied her scientific rigor to ML and AI.

After recently announcing Equity and Inclusion teams to investigate racial bias across their ... [ ] platforms, and undergoing an advertising boycott over alleged racial discrimination, is Facebook doing enough to tackle racial bias? Racial bias is a deeply rooted, insidious problem in our society, and that prejudice has already leaked into many of the algorithms we use today. With Facebook recently announcing their intention to "investigate" racial bias in their algorithms, the question arises about the role of Big Tech in perpetuating racially discriminatory AI - and what benefit they might receive by sitting on the fence at a time when the anti-racism movement is stronger than ever. As we put more trust in AI to make decisions for us, and data becomes increasingly intertwined with our daily lives, is it just a political move to simply investigate bias or improve diversity if algorithms continue to make racially biased decisions? Bias is an inherent problem to AI, particularly in the deep learning systems that power digital assistants, search engines and social media.

Wendy Gonzalez, interim CEO of Samasource, poses with Agents in Nairobi, Kenya. Samasource employees ... [ ] young Kenyans and Ugandans to work in the AI supply chain, upskilling them up for a career in technology. Conscious consumers demand fair-trade when it comes to products like coffee, and when it's quality coffee, they are even willing to pay more for it. When it comes to our technology products though, many consumers don't even know that "fair-trade" is possible. Behind many acts of AI "magic," there is a human in the loop.

Many of her neighbors have fallen on hard times since Covid-19 shut the city last month, but she's been lifted into the lap of luxury. Akol, who is 28, works for Samasource, a company that labels images and other data for companies such as Google, creating the feedstock for artificial intelligence projects like self-driving cars. She's the main breadwinner in the busy Nairobi apartment she shares with her 7-year-old son and her two brothers, ages 8 and 24. But Akol hasn't seen her family or apartment for around a month because, like most of Samasource's Nairobi staff, she now lives and works from a resort hotel. Her window at the four-star Ole Sereni overlooks the grassy plains of Nairobi National Park--a major change from the company's open-plan office next to a freeway.

AI training data provider Samasource has raised a $14.8 million Series A funding round led by Ridge Ventures. The San Francisco headquartered company delivers Fortune 100 companies with the inputs they need for machine learning development in fields including autonomous transportation, e-commerce and robotics. And it does so with a global work-force of data-specialists, a large number of whom are located in East Africa. In addition to San Francisco, New York and the Hague, Samasource has offices and teams in Kenya and Uganda. The company has a global staff of 2900 and is the largest AI and data annotation employer in East Africa, according to CEO and founder Leila Janah.

Artificial Intelligence is the biggest threat to mankind, right? Even if robots aren't taking over the planet by force, the yarn goes, computers will surely push us all into unemployment in the next decade or so. Let's meet someone who can give us a slightly different perspective. This is Joel, standing in front of his house, a few kilometers outside Gulu, Uganda, where he lives with his 14 brothers and sisters. Joel works for Zillow, the leading online real estate marketplace in the US with 1.1B of revenue in 2017.

Rightly or wrongly, the AI-driven world has come to typify the woes of modern economic life, as tech giants such as Facebook and Alphabet amass vast fortunes, due in large part to the huge quantities of data that users often freely provide them. Accusations of economic imbalance tend to be multi-faceted. Not only do these companies typically employ fewer people than the industrial titans of yore, but they also attract the ire of officials over their tax practices and have grown insanely rich off the back of something users receive no compensation for. It's helped to create a world in which the haves are increasingly well off, while the have nots make do with insecure and poorly paid work. Nowhere is this exchange more evident than in the data annotation industry, where people from around the world help to prepare and tidy up the data used by the tech giants to train the algorithms upon which their fortunes increasingly rest.

Rightly or wrongly, the AI-driven world has come to typify the woes of modern economic life, as tech giants such as Facebook and Alphabet amass vast fortunes, due in large part to the huge quantities of data that users often freely provide them. Accusations of economic imbalance tend to be multi-faceted. Not only do these companies typically employ fewer people than the industrial titans of yore, but they also attract the ire of officials over their tax practices and have grown insanely rich off the back of something users receive no compensation for. It's helped to create a world in which the haves are increasingly well off, while the have nots make do with insecure and poorly paid work. Nowhere is this exchange more evident than in the data annotation industry, where people from around the world help to prepare and tidy up the data used by the tech giants to train the algorithms upon which their fortunes increasingly rest.