A fight for the soul of machine learning Posted on : May 21 - 2020

Last Tuesday, Google shared a blog post highlighting the perspectives of three women of color employees on fairness and machine learning. I suppose the comms team saw trouble coming: The next day NBC News broke the news that diversity initiatives at Google are being scrapped over concern about conservative backlash, according to eight current and former employees speaking on condition of anonymity.

The news led members of the House Tech Accountability Caucus to send a letter to CEO Sundar Pichai on Monday. Citing Google’s role as a leader in the U.S. tech community, the group of 10 Democrats questioned why, despite corporate commitments over years, Google diversity still lags behind the diversity of the population of the United States. The 10-member caucus specifically questioned whether Google employees working with AI receive additional bias training.

When asked by VentureBeat, a Google spokesperson did not respond to questions raised by members of Congress but said any suggestion that the company scaled back diversity initiatives is “categorically false.” Pichai called diversity a “foundational value” for the company. For her part, Google AI ethical research scientist Timnit Gebru, one of the three women featured in the Google blog post, spelled out her feelings about the matter on Twitter.

Hiring AI practitioners from diverse backgrounds is seen as a way to catch bias embedded in AI systems. Many AI companies pay lip service to the importance of diversity. As one of the biggest and most influential AI companies on the planet, what Google does or doesn’t do stands out and may be a bellwether of sorts for the AI industry. And right now, the company is cutting back on diversity initiatives at a time when clear ties are being drawn between surveillance AI startups and alt-right or white supremacy groups. Companies with documented algorithmic bias like Google, as well as those associated with alt-right groups, seem to really like government contracts. That’s a big problem in an increasingly diverse America. Stakeholders in this world of AI can ignore these problems, but they’ll only fester and risk not just a public trust crisis, but practical harms in people’s lives.

Reported diversity program cutbacks at Google matter more than at virtually any other company in the world. Google began much of the modern trend of divulging corporate diversity reports that spell out the number of women and people of color within its ranks. According to Google’s 2020 diversity report, roughly 1 in 3 Google employees are women, whereas 3.7% are African American, 5.9% are Latinx, and 0.8% are Native American.

Stagnant, slow progress on diversity in tech matters a lot more today than it did in the past now that virtually all tech companies — especially companies like Amazon, Google, and Microsoft — call themselves AI companies. Tech, and AI more specifically, suffers from what’s referred to as AI’s “white guy problem.” Analysis and audits of a vast swath of AI models have found evidence of bias based on race, gender, and a range of other characteristics. Somehow, AI produced by white guys often seems to work best on white guys. View More