Mary L. Gray on the Power of AI Access for All of Humanity

Throughout history, revolutionary new technologies have required new approaches to regulation and oversight. In her upcoming book aimed at technologists interested in responsible computing, Banality of Scale: An Anthropologist’s Fieldguide to the Future of Computing, anthropologist and Microsoft Research principal researcher Mary L. Gray includes AI as the latest example of a technology whose production and distribution has profound implications for human rights.

Mary’s Bellagio Reflections: “I went to Bellagio thinking I had already completed a very rough draft of a book, and while there I tried to answer this question: ‘What can we do when it seems inevitable that so much data about us will be collected, and so much modeling is based on superficial examples of everyday decision-making scraped from the web?’ The residency gave me even more conviction. We are in a moment of opportunity. The most important thing we can do is take advantage of the need for large-scale data to develop AI by demanding that this experimental research be held accountable to a broader set of social obligations, since AI must learn from people and people’s data.

“The lack of public governance of AI is having such an impact already. There’s a palpable feeling that we’re all trapped in a race among a few companies and countries without a voice representing the public’s interest. I was fired up after Bellagio, convinced that there really was something we could say – something that I have to say – and it was sharpened by conversations with the other residents about both the history of AI and the possibilities for its future.”

Here, Mary argues that measuring what AI can or cannot do is a distraction from a more crucial task for policymakers and engineers: ensuring its development is democratic, open, and regulated.

When it comes to AI, and the development of large language models in general, my fear is that we’re worrying about the wrong things. Focusing on philosophical concepts like sentience, or even AI’s ability to make individual decisions in isolation, distracts us from the crucial opportunity we have to bring more public governance and engagement to the direction of this technology’s development. In spite of what many of us think, AI cannot generate ideas and decisions on its own; it needs a continuous stream of human decisionmaking to advance. Our biggest challenge is to realize that, in all cases, the benefits and successes of AI hinge on the conscious decisions we make about when, why, and how we use it – and on who makes these decisions.

The history of technological innovation gives us a good reason to pay attention to the so-called margins where we imagine technology trickles down in society. Whether it was the telegraph, the internet, or smartphones, the reality was that new technologies followed the flow of power and money, even if in principle they should have been accessible to everyone. The threat with AI is that we create yet another technological divide that people have to work to overcome. I study those moments of hubris, when that belief that technology will inevitably flow out and down resurfaces. Given the power imbalance involved, there is no evidence technologies in and of themselves create level playing fields. Tech, in fact, sharpens divisions that already exist, unless active efforts are made to counter such trends. If we believe in the power of this technology – and I do – then we must understand that that power comes from how we share it and its value to the full range of society, not just those individuals seen as its paying customers.

I believe that the most important thing we can do right now is realize that the most creative, effective, and productive uses of generative AI are going to come from us being very aware, collectively, that its value to humanity is dependent upon the breadth of humanity having access to it. It’s vitally important to see access to, and oversight of, AI as a social challenge and activity. We should work to actively move new technologies into new places in our lives, and do that globally, and equitably.

This goal requires three priorities. The first should be to ask ourselves, “What are the decisions we want AI to be making, and who are the people being left out or pushed out of decision-making as a result?” A model, by definition, is less detailed than reality. Its equation necessarily leaves out much of the rich complexity of our social worlds, which in turn means leaving someone out. For example, anyone who hasn’t generated a digital signal of how they make decisions isn’t included – that means many oral cultures and all sign languages globally.