Can We Build an Equality Machine? An Introduction, by Rachel Arnow-Richman
*This is the introduction to a symposium on Orly Lobel’s The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future, selected by The Economist as a best book of 2022. All posts from this symposium can be found here. Further reviews can be found at Science, The Economist, and Kirkus.
Consider these paradoxical truths: First, contemporary society stands at the brink of unprecedented advancements in artificial intelligence that offer near limitless potential and opportunity. Second, we live in a society where potential and opportunity have never been equitably dispersed, but obey a preexisting power structure benefiting some and excluding others. As we envision and engineer a state-of-the-art, machine-powered tomorrow, we are inevitably constrained by the latent biases and ingrained perceptions that both reflect and perpetuate past harms and historical divisions.
It is natural then to assume that an AI-infused world will not merely echo but ultimately exacerbate existing inequity. And there are no shortage of pundits, scholars, and news stories to fuel these fears. But Orly Lobel has a different view. In her new book The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future, she offers a counter-narrative: AI can be a catalyst for change and a corrective tool that facilitates equal access and yields a more diversified world.
It is a seductive if controversial contention. As citizens of the twenty-first century, we are imbued with belief that technology and innovation are the solution to the full range of human-created problems, from armed conflict to global warming. A dose of caution and humility is surely in order.
But Lobel is neither evangelist nor idealist when it comes to what technology can achieve. Her claim is that artificial intelligence is inherently superior to human thinking when it comes to identifying, isolating, and correcting embedded bias. The human mind, not artificial intelligence, is the ultimate “black box.” Accepted research tells us that in every interaction we rely on faulty assumptions, stereotype, and cognitive errors we can neither observe nor fully understand. And our powers of self-correction are inherently limited.
If so, reports of alarmingly racist and gendered output from machine trials should be seen not as indictments of technology but as opportunities for engineering change. Such moments force us to stare unblinkingly at the depth and pervasiveness of bias in what we take for granted in the work-a-day world. AI affords us the rare chance to observe bias in action, to examine it unselfconsciously, reverse engineer, and ultimately chart a new course.
Of course, such an authentic response is hardly inevitable, and Lobel does not overplay her hand. She acknowledges that building an “equality machine” requires a sustained and multi-level commitment — financial investment, legislative guardrails, independent oversight, transparency, and accountability, to name a few. It also requires a lot of hard work, the old-fashioned human kind.
Thomas Edison, an icon of innovation in his day, famously said that genius is one percent inspiration, ninety-nine perception perspiration. If so, we ought to begin. In the posts that follow, scholars from a variety of fields wrestle with Lobel’s work. They consider the promise and limitations of AI when brought to bear on sites of long-standing inequity — medicine and health care, corporate personnel practices, the criminal justice system. Such nuanced reflection is exactly what Lobel demands. AI is here; we must think deeply about how best to use it.
Rachel Arnow-Richman is the inaugural Gerald A. Rosenthal Chair in Labor & Employment Law at the University of Florida Levin College of Law.