The summer of 2020 saw an unprecedented number of corporate leaders publicly acknowledge and condemn institutional and structural racism, as part of a national reckoning on racial injustice. Issued in response to widespread protests against police violence, these statements called for an end to racial disparities across all systems, including health care, education, housing, criminal justice, and employment. This movement emerged at a moment when people of color were more likely to be working on the dangerous front lines of a pandemic, for low wages and with scant protective gear, while higher-wage earners sheltered at home. Businesses claiming to support racial equity are, in reality, committing to change facts such as these: Black workers are more likely to work in low-wage industries with high turnover, earn less than their white counterparts, and report a median net worth of one-tenth that of whites. This fixed inequality holds even for Black college graduates, whose households report a lower net worth than those headed by white high school dropouts.

A well-known artificial intelligence researcher at Google tweeted Wednesday that she was fired over an email expressing dismay with management over the censorship of new research. Timnit Gebru, a technical co-lead of Google's Ethical A.I. team, who researches algorithmic bias and data mining, has been an outspoken advocate for diversity in technology, claimed, in a series of tweets, she was fired for refusing to retract a research paper that outlines how A.I. discriminates against minorities and also due to a complaint in an email to colleagues. Expressing her frustrations in an email to an internal company group named Google Brain Women and Allies - Gebru criticized Google's hiring of minorities and not doing enough to promote "responsible A.I." The email was shared by Platformer's Casey Newton: I had stopped writing here as you may know, after all the micro and macro aggressions and harassments I received after posting my stories here (and then of course it started being moderated). Recently however, I was contributing to a document that Katherine and Daphne were writing where they were dismayed by the fact that after all this talk, this org seems to have hired 14% or so women this year. Samy has hired 39% from what I understand but he has zero incentive to do this.

Timnit Gebru, a pioneering researcher on algorithmic bias, said Wednesday night that she had been abruptly let go by Google, where she was technical co-lead of the company's Ethical Artificial Intelligence Team, after she had privately threatened to resign. Gebru is known for her co-authorship with Joy Buolamwini of an influential 2018 paper on bias in facial recognition software, among other work. The study found that three leading facial recognition systems were far more likely to misidentify women and people of color than white men. The findings helped to fuel a backlash against facial recognition that has led some major companies and jurisdictions to stop developing or using the technology. OneZero's Dave Gershgorn wrote in June about the study's profound impact.

Many Google employees and others in the tech and academic communities are furious over the sudden exit from Google of a pioneer in the study of ethics in artificial intelligence--a departure they see as a failure by an industry titan to foster an environment supportive of diversity. Timnit Gebru is known for her research into bias and inequality in AI, and in particular for a 2018 paper she coauthored with Joy Buolamwini that highlighted how poorly commercial facial-recognition software fared when attempting to classify women and people of color. Their work sparked widespread awareness of issues common in AI today, particularly when the technology is tasked with identifying anything about human beings. At Google, Gebru was the co-leader of the company's ethical AI team, and one of very few Black employees at the company overall (3.7% of Google's employees are Black according to the company's 2020 annual diversity report)-- let alone in its AI division. The research scientist is also cofounder of the group Black in AI.On Wednesday night, Gebru tweeted that she had been "immediately fired" for an email she recently sent to Google's Brain Women and Allies internal mailing list.

Artificial intelligence-powered use cases for climate action could help organisations meet up to 45% of the Economic Emission Intensity (EEI) targets of the Paris Agreement. New research from the Capgemini Research Institute has found that while AI offers many climate action use cases, only 13% of organisations are successfully combining climate vision with AI capabilities. AI use cases include improving energy efficiency, reducing dependence on fossil fuels and optimising processes to aid productivity. The research found that 67% of organisations have long-term business goals to tackle climate change. While many technologies address a specific outcome, such as carbon capture or renewable sources of energy, AI can accelerate organisations' climate action across sectors and value chains.

Hundreds of Google employees and more than 1,000 academic researchers are speaking out in protest after a prominent Black scientist studying the ethics of artificial intelligence said she was fired by Google after the company attempted to suppress her research and she criticized its diversity efforts. Timnit Gebru, who was the technical co-lead of Google's Ethical AI team, wrote on Twitter on Wednesday that she had been fired after sending an email to an internal group for women and allies working in the company's AI unit. The email, which was first published by the tech newsletter Platformer, referenced a dispute over a research paper, but more broadly expressed frustration at Google's diversity programs. In it, Gebru argued that "there is zero accountability" or real incentive for Google leadership to change. "Your life gets worse when you start advocating for underrepresented people, you start making the other leaders upset," Gebru wrote.

Timnit Gebru, one of Google's top artificial intelligence researchers, says the company abruptly fired her yesterday. The technical co-lead of Google's Ethical Artificial Intelligence Team claims managers were upset about an email she'd sent to colleagues. The email, which was sent to the Brain Women and Allies listserv, voiced frustration that managers were trying to get Gebru to retract a research paper. The full text was first published in Platformer. "A week before you go out on vacation, you see a meeting pop up at 4:30pm PST on your calendar," it reads.

Artificial intelligence (AI) for translation is something Google and other companies have provided for individuals. It can be accessed on your phone. However, translation is still a much larger and complex issue than many people realize. The business community has many complex and unique needs that add to the challenge of accurate and reliable translation, and AI is showing increasing capability. One of the keys to business translation is the simple reality that each business sector has its own terms, phrases, and even idioms.

After the buyer used the weapon to kill his estranged wife and two others, the site successfully invoked Section 230 to avoid liability. More recently, Grindr, a dating app, took cover behind Section 230 when Matthew Herrick, an actor in New York, sued the site as a result of false profiles that were created by an ex-boyfriend. The profiles, which included Herrick's home and work addresses, suggested that Herrick had rape fantasies, and that any resistance he put up was part of the fantasy. As a consequence, hundreds of men showed up at his apartment door or at his workplace, at all hours, month after month, forcibly demanding sex. "You look at that law, and it seems very narrow," Herrick's lawyer, Carrie Goldberg, told me.

How could software designed to take the bias out of decision making, to be as objective as possible, produce these kinds of outcomes? After all, the purpose of artificial intelligence is to take millions of pieces of data and from them make predictions that are as error-free as possible. But as AI has become more pervasive--as companies and government agencies use AI to decide who gets loans, who needs more health care and how to deploy police officers, and more--investigators have discovered that focusing just on making the final predictions as error free as possible can mean that its errors aren't always distributed equally. Instead, its predictions can often reflect and exaggerate the effects of past discrimination and prejudice. In other words, the more AI focused on getting only the big picture right, the more it was prone to being less accurate when it came to certain segments of the population--in particular women and minorities.