To demonstrate how easy it is to track people without their knowledge, we collected public images of people who worked near Bryant Park (available on their employers' websites, for the most part) and ran one day of footage through Amazon's commercial facial recognition service. Our system detected 2,750 faces from a nine-hour period (not necessarily unique people, since a person could be captured in multiple frames). It returned several possible identifications, including one frame matched to a head shot of Richard Madonna, a professor at the SUNY College of Optometry, with an 89 percent similarity score.

Last Wednesday, US lawmakers introduced a new bill that represents one of the country's first major efforts to regulate AI. There are likely to be more to come. It hints at a dramatic shift in Washington's stance toward one of this century's most powerful technologies. Only a few years ago, policymakers had little inclination to regulate AI. Now, as the consequences of not doing so grow increasingly tangible, a small contingent in Congress is advancing a broader strategy to rein the technology in.

The history of AI is often told as the story of machines getting smarter over time. What's lost is the human element in the narrative, how intelligent machines are designed, trained, and powered by human minds and bodies. In this six-part series, we explore that human history of AI--how innovators, thinkers, workers, and sometimes hucksters have created algorithms that can replicate human thought and behavior (or at least appear to). While it can be exciting to be swept up by the idea of super-intelligent computers that have no need for human input, the true history of smart machines shows that our AI is only as good as we are. In the 1970s, Dr. Geoffrey Franglen of St. George's Hospital Medical School in London began writing an algorithm to screen student applications for admission.

Ask a layman about artificial intelligence and they might point to sci-fi villains such as HAL from 2001: A Space Odyssey or the Terminator. But the co-founders of the AI Now Institute, Meredith Whittaker and Kate Crawford, want to change the conversation. Instead of talking about far-flung super-intelligent AI, they argued on the latest episode of Recode Decode, we should be talking about the ways AI is affecting people right now, in everything from education to policing to hiring. Rather than killer robots, you should be concerned about what happens to your résumé when it hits a program like the one Amazon tried to build. "They took two years to design, essentially, an AI automatic résumé scanner," Crawford said. "And they found that it was so biased against any female applicant that if you even had the word'woman' on your résumé that it went to the bottom of the pile." That's a classic example of what Crawford calls "dirty data." Even though people think of algorithms as being ...

The Chinese government is using facial-recognition software to "track and control" a predominantly Muslim minority group, according to a disturbing new report from The New York Times. The Chinese government has reportedly integrated artificial intelligence into its security cameras to identify the Uighurs and appears to be using the information to monitor the persecuted group. The report, based on the accounts of whistleblowers familiar with the systems and a review of databases used by the government and law enforcement, suggests the authoritarian country has opened up a new frontier in the use of A.I. for racist social control--and raises the discomfiting possibility that other governments could adopt similar practices. Two people familiar with the matter told the Times that police in the Chinese city of Sanmenxia screened whether residents were Uighurs 500,000 times in a single month. Documents provided to the paper reportedly show demand for the technology is ballooning: more than 20 departments in 16 provinces sought access to the camera system, in one case writing that it "should support facial recognition to identify Uighur/non-Uighur attributes." This, experts say, is more than enough to raise red flags.

When Uber filed the paperwork for its initial public offering on Thursday, the quintessential bad boy startup signaled to the world that it was ready to grow up. In a letter to potential investors, the CEO, Dara Khosrowshahi, acknowledged the "greater responsibilities" the company will take on once it goes public, and promised to act with "passion, humility, and integrity". But references to the company's checkered past are littered throughout the more than 300 pages of public disclosures filed to the Securities and Exchange Commission. Here's a rundown of some of the biggest "risk factors" from Uber's past that may come back to haunt its $100bn future: When Uber's ride-share rival Lyft went public with its own pre-IPO disclosures in March, its "unique culture" was referenced as a positive aspect of the company dozens of times; any possible loss of that culture in the future was identified as a risk factor. For Uber, the challenge is the opposite.

The year of the gig economy IPO continues, when Uber Thursday made public its first bit of official paperwork with the Securities and Exchange Commission--a sign that the tech company is preparing to list its shares on the New York Stock Exchange. The filing shows a sprawling transportation business with operations stretching into 63 countries and over 700 cities, providing 5.2 billion rides in 2018: roughly one for every person in Europe and Asia. Uber pulled in $11.3 billion in revenue in 2018, a 42 percent jump over the year previous. And though its operating losses are still heavy--$3 billion in 2018--the company has managed to stem them, at least a bit, bringing operating losses down from $4.1 billion in 2017. Uber had 91 million active users at the end of 2018, 23 million more than a year earlier.

Lawmakers want to make sure the algorithms companies use to target ads, recruit employees and make other decisions aren't inherently biased against certain people. Sens. Ron Wyden, D-Ore., and Cory Booker, D-N.J., on Wednesday introduced legislation that would require organizations to assess the objectivity of their algorithms and correct any issues might unfairly skew their results. As society depends on tech to make increasingly consequential decisions, the Algorithmic Accountability Act aims to create a level playing field for people of all backgrounds. Rep. Yvette Clarke, D-N.Y., introduced a companion bill in the House. Under the act, the Federal Trade Commission would compel companies to test both their algorithms and training data for any shortcomings that could lead to biased, inaccurate, discriminatory or otherwise unfair decisions.

WATCH (May 24, 2018): Amazon's Alexa records family's conversation, sends it to random contact Amazon staff can listen to commands and questions users pose to the Alexa voice assistant -- and they sometimes do. The company acknowledged that the conversations aren't totally private in a statement to Global News after the news was first reported by Bloomberg. "We only annotate an extremely small number of interactions from a random set of customers in order to improve the customer experience," Amazon said in the statement. Amazon explained that the company uses samples collected to better train "speech recognition and natural language understanding systems." READ MORE: Alexa recorded one family's conversations and sent them to a friend, without them knowing Bloomberg reported Wednesday that Amazon has "thousands" of employees who are trying to improve Alexa's speech recognition technology.

Amazon's Echo speakers have a broadcast feature that will help you send a message to family members that might be scattered around the house. If you have an Amazon Echo product, you aren't the only person privy to your private conversations. Thousands of people across the globe are employed by Amazon.com to listen to Echo recordings, transcribe and annotate them and feed them back to the software so that Alexa can better grasp human speech, according to a report from Bloomberg. The employees – ranging from Boston to India – signed nondisclosure agreements barring them to speak publicly about the program. According to Bloomberg, they work nine hours per day, with each reviewer going through as many as 1,000 audio clips per shift.