[Carly] Fiorina, who in fact had done such an in-depth study of the company and the situation that she had many of the answers, probably meant to reassure people by her knowledge. But by not asking questions and by not failing to have some answers, she scared many of the employees. Fortunately, when this feedback was delivered and received, Fiorina adapted her active listening and allowed her natural gift for communication to flourish. This increased her credibility and at the same time deepened her insights into the company, its challenges, and its opportunities.

7. The Two Tonis

Even as Bambara agreed that women must approach “the complexity of ourselves in a fearless way,” she saw no dilemma in the way being a Black woman intersected with being a race warrior. As she told Beverly Guy-Sheftall,

It’s not as if you’re a Black or a woman. I don’t find any basic contradiction or any tension between being a feminist, being a pan-Africanist, being a socialist, and being a woman in North America. I’m not sensitive enough to people caught in the “contradiction” to be able to see the dilemma. . . . My head is somewhere else.

In 2018, the book Algorithms of Oppression by Safiya Umoja Noble, a professor of information, gender, and African American studies at the University of California, Los Angeles, had extensively documented the replication of racist worldviews in Google’s search results, such as by showing far more sexually explicit and pornographic content for “Black girls” than “white girls” and tropes about Black women being angry. Google at the time had used an older generation of language models to curate those results, which in extreme cases, Noble argued, may have also provoked racial violence.

In total, it presented four key warnings: First, large language models were growing so vast that they were generating an enormous environmental footprint, as found in Strubell’s paper.

Second, the demand for data was growing so vast that companies were scraping whatever they could find on the internet, inadvertently capturing more toxic and abusive language as well as subtler racist and sexist references.

Third, because such vast datasets were difficult to audit and scrutinize, it was extremely challenging to verify what was actually in them, making it harder to eradicate toxicity or more broadly ensure that they reflected evolving social norms and values. Finally, the model outputs were getting so good that people could easily mistake its statistically calculated outputs as language with real meaning and intent. This would make people prone not only to believing the text to be factual information but also to consider the model a competent adviser, a trustworthy confidant, and perhaps even something sentient.

For more than a year, the protests continued, picking up a second wave after Google fired Meg Mitchell less than three months later. Google said she had violated multiple codes of conduct; Mitchell had been downloading her emails and files related to Gebru’s ouster. Several Google employees, including Bengio, resigned; at least one conference and several researchers rejected Google’s sponsorship money. The company sought to stem the unending tide of criticism with the formation of a new center of expertise on responsible AI and public commitments to diversity. “This was a painful moment for the company,” a Google spokesperson said. “It reinforced how important it was that Google continue its work on responsible AI and learn from the experience.”