Much ‘Artificial Intelligence’ is still people behind a screen

Building AI systems requires many hours of humans training algorithms, and some companies have fallen into the gray area between training and operating. Photo: Bloomberg

The nifty app CamFind has come a long way with its artificial intelligence. It uses image recognition to identify an object when you point your smartphone camera at it. But back in 2015 its algorithms were less advanced: The app mostly used contract workers in the Philippines to quickly type what they saw through a user’s phone camera, CamFind’s co-founder confirmed to me recently. 

You would not have guessed that from a press release it put out that year which touted industry-leading “deep learning technology,” but did not mention any human labellers.

The practice of hiding human input in AI systems still remains an open secret among those who work in machine learning and AI. A 2019 analysis of tech startups in Europe by London-based MMC Ventures even found that 40 percent of purported AI startups showed no evidence of actually using artificial intelligence in their products. 

This so-called AI washing should not be surprising. Global investment into AI companies has steadily risen over the past decade and more than doubled in the past year, according to market intelligence firm PitchBook. Calling your startup an “AI company” can lead to a funding premium of as much as 50 percent compared to other software companies, according to the MMC Ventures analysis.

Yet ignoring the workers who power these systems is leading to unfair labour practices and skewing the public’s understanding of how machine learning actually works. 

In Silicon Valley, many startups have succeeded by following the “fake it ’til you make it” mantra. For AI companies, hiring people to prop up algorithms can become a stopgap, which on occasion becomes permanent. Humans have been discovered secretly transcribing receipts, setting up calendar appointments or carrying out bookkeeping services on behalf of “AI systems” that got all the credit. 

In 2019, a whistleblower lawsuit against a British firm claimed customers paid for AI software that analysed social media while staff members were doing that work instead.

There is a reason this happens so often. Building AI systems requires many hours of humans training algorithms, and some companies have fallen into the gray area between training and operating. A common explanation is that human workers are providing “validation” or “oversight” to algorithms, like a quality-control check. But in some cases, these workers are doing more cognitively intensive tasks because the algorithms they oversee do not work well enough on their own.

That can bolster unrealistic expectations about what AI can do. “It’s part of this quixotic dream of super-intelligence,” says Ian Hogarth, an angel investor, visiting professor at University College London and co-author of an annual State of AI report that was released on Tuesday. For the hidden workers, working conditions can also be “anti-human,” he says. That can lead to inequalities and poor AI performance.

For instance, Cathy O’Neil has noted that Facebook’s machine-learning algorithms do not work well enough in stopping harmful content. (I agree.