AI is racist.

Hundreds of people gathered for the first lecture at what had become the world’s most important conference on artificial intelligence — row after row of faces. Some were East Asian, a few were Indian, and a few were women. But the vast majority were white men. More than 5,500 people attended the meeting, five years ago in Barcelona, Spain.

Timnit Gebru, then a graduate student at Stanford University, remembers counting only six Black people other than herself, all of whom she knew, all of whom were men.

The homogeneous crowd crystallized for her a glaring issue. The big thinkers of tech say A.I. is the future. It will underpin everything from search engines and email to the software that drives our cars, directs the policing of our streets and helps create our vaccines.

But it is being built in a way that replicates the biases of the almost entirely male, predominantly white work force making it.

In the nearly 10 years I’ve written about artificial intelligence, two things have remained a constant: The technology relentlessly improves in fits and sudden, great leaps forward. And bias is a thread that subtly weaves through that work in a way that tech companies are reluctant to acknowledge.

On her first night home in Menlo Park, Calif., after the Barcelona conference, sitting cross-​legged on the couch with her laptop, Dr. Gebru described the A.I. work force conundrum in a Facebook post.

“I’m not worried about machines taking over the world. I’m worried about groupthink, insularity and arrogance in the A.I. community — especially with the current hype and demand for people in the field,” she wrote. “The people creating the technology are a big part of the system. If many are actively excluded from its creation, this technology will benefit a few while harming a great many.”

The A.I. community buzzed about the mini-manifesto. Soon after, Dr. Gebru helped create a new organization, Black in A.I. After finishing her Ph.D., she was hired by Google.

She teamed with Margaret Mitchell, who was building a group inside Google dedicated to “ethical A.I.” Dr. Mitchell had previously worked in the research lab at Microsoft. She had grabbed attention when she told Bloomberg News in 2016 that A.I. suffered from a “sea of dudes” problem. She estimated that she had worked with hundreds of men over the previous five years and about 10 women.

Their work was hailed as groundbreaking. The nascent A.I. industry, it had become clear, needed minders and people with different perspectives.

About six years ago, A.I. in a Google online photo service organized photos of Black people into a folder called “gorillas.” Four years ago, a researcher at a New York start-up noticed that the A.I. system she was working on was egregiously biased against Black people. Not long after, a Black researcher in Boston discovered that an A.I. system couldn’t identify her face — until she put on a white mask.

In 2018, when I told Google’s public relations staff that I was working on a book about artificial intelligence, it arranged a long talk with Dr. Mitchell to discuss her work. As she described how she built the company’s Ethical A.I. team — and brought Dr. Gebru into the fold — it was refreshing to hear from someone so closely focused on the bias problem.

But nearly three years later, Dr. Gebru was pushed out of the company without a clear explanation. She said she had been fired after criticizing Google’s approach to minority hiring and, with a research paper, highlighting the harmful biases in the A.I. systems that underpin Google’s search engine and other services.

“Your life starts getting worse when you start advocating for underrepresented people,” Dr. Gebru said in an email before her firing. “You start making the other leaders upset.”

As Dr. Mitchell defended Dr. Gebru, the company removed her, too. She had searched through her own Google email account for material that would support their position and forwarded emails to another account, which somehow got her into trouble. Google declined to comment for this article.

Their departure became a point of contention for A.I. researchers and other tech workers. Some saw a giant company no longer willing to listen, too eager to get technology out the door without considering its implications. I saw an old problem — part technological and part sociological — finally breaking into the open.

It should have been a wake-up call.

In June 2015, a friend sent Jacky Alciné, a 22-year-old software engineer living in Brooklyn, an internet link for snapshots the friend had posted to the new Google Photos service. Google Photos could analyze snapshots and automatically sort them into digital folders based on what was pictured. One folder might be “dogs,” another “birthday party.”

When Mr. Alciné clicked on the link, he noticed one of the folders was labeled “gorillas.” That made no sense to him, so he opened the folder. He found more than 80 photos he had taken nearly a year earlier of a friend during a concert in nearby Prospect Park. That friend was Black.

He might have let it go if Google had mistakenly tagged just one photo. But 80? He posted a screenshot on Twitter. “Google Photos, y’all,” messed up, he wrote, using much saltier language. “My friend is not a gorilla.”

Like facial recognition services, talking digital assistants and conversational “chatbots,” Google Photos relied on an A.I. system that learned its skills by analyzing enormous amounts of digital data.

Called a “neural network,” this mathematical system could learn tasks that engineers could never code into a machine on their own. By analyzing thousands of photos of gorillas, it could learn to recognize a gorilla. It was also capable of egregious mistakes. The onus was on engineers to choose the right data when training these mathematical systems. (In this case, the easiest fix was to eliminate “gorilla” as a photo category.)

As a software engineer, Mr. Alciné understood the problem. He compared it to making lasagna. “If you mess up the lasagna ingredients early, the whole thing is ruined,” he said. “It is the same thing with A.I. You have to be very intentional about what you put into it. Otherwise, it is very difficult to undo.”