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The AI ethicist Eleanor Drage believes that to thrive alongside artificial intelligence, humans need to recognise AI’s “humanity”. She means that while we often speak of AI as though it were some mystical, formless thing, it is the product of hours of human labour, in silicon and quartz mines, microchip factories and data labelling centres. If citizens want to wrest power from the tech companies that are building AI, it helps to understand clearly what they are selling us. This also means seeing through the marketing and hype: “the cloud”, that nebulous, distant place where your photos are stored, is just someone else’s computer; an AI “hallucination” is a system error or data labelling mistake; intelligence is a very broad – and potentially misleading – term.
The story in Silicon Valley is that AI will either save us, ushering in a new era of prosperity and human longevity, or it will wipe us all out. Drage argues, convincingly, that big tech cannot possibly deliver on its utopian promises while it continues to chase profit and power above all else, and while it serves the interests of a narrow few. Meanwhile, the cultural obsession with the AI-induced apocalypse conveniently redirects attention from the practical conversations we could be having about how to make AI safer – by ensuring citizens have more power over how their information is used, and how AI models are trained and regulated, for example. Tech titans’ apocalyptic visions also create the false impression that AI safety is more important than climate change, geopolitical tensions and inequality, three problems that are already causing a huge amount of suffering and that, if addressed, would reduce many AI-linked risks too.
Drage, an assistant research professor at the University of Cambridge’s Leverhulme Centre for the Future of Intelligence, has conducted research that debunks AI-powered recruitment processes, and exposes the dangers of incorporating AI into law enforcement. Many of her criticisms of big tech will be familiar: that AI algorithms perpetuate and entrench pre-existing biases; that for all their bland, generic promises to be “transparent” and “accountable”, tech giants have become too big to care about ethics; that a handful of executives have amassed far too much power; and that the rollout of AI is happening without our consent and without consideration for planetary health. But her writing has intellectual heft, and her philosophical explorations of, for example, how we define intelligence and why this matters, are very interesting.
Occasionally, however, Drage’s arguments seem rushed and become sloppy. “It’s merely an illusion that the labour force is disappearing: work is simply shifting, from artists to data labellers and from marketeers to engineers,” she writes at one point. But it’s not clear that artists and “marketeers” will become redundant in the coming AI era. It’s also not as though the data labellers working for poverty wages in Kenya would have been artists 10 years ago. Equally, given the importance she rightly places on promoting diversity in AI, it’s disappointing that she dismisses unconscious bias as “merely another way of taking the perspective of the perpetrator rather than the recipient of harm”, as though intention is completely irrelevant. She writes that people are ready for “less placid” conversations about equality, I’d add that these conversations also demand accuracy and nuance.
The book’s title suggests Drage wants to offer an alternative vision of what AI could be. She gives a few examples of the kind of ethical, community-based AI projects she thinks should be replacing top-down big tech products, and many of them are strangely underwhelming. They include an app called Mumkin designed to facilitate difficult conversations about female genital mutilation (FGM) in India, and Kuini, an AI chatbot built to help Māori women quit smoking. In both instances I found myself wondering what value AI brings to these endeavours – particularly given all that Drage has discussed about its environmental impact and exploitative supply chains. Is an app more effective than training community members to act as counsellors, or funding support groups and peer support workers?
Drage describes a toolkit she co-designed to help AI companies act ethically and comply with EU regulations as a “cornerstone of utopianism”, because it shifts the focus on to how AI is built and made. In her view, utopia is not a destination but the “process of working together to develop a fairer world”. Maybe it would be unfair to expect too specific a utopian vision, but I’d nonetheless be interested to know how AI, specifically, can help us build a better future. She does not mention the things that many of AI’s biggest critics nonetheless welcome, such as its potential to improve drug discovery and disease detection. What if we got AI right, this book asks. After reading it, I can’t say I really know.
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