Another week, another privacy horror show: Crisis Text Line, a nonprofit text message service for people experiencing serious mental health crises, has been using “anonymized” conversation data to power a for-profit machine learning tool for customer support teams. (After backlash, CTL announced it would stop.) Crisis Text Line’s response to the backlash focused on the data itself and whether it included personally identifiable information. But that response uses data as a distraction. Imagine this: Say you texted Crisis Text Line and got back a message that said “Hey, just so you know, we’ll use this conversation to help our for-profit subsidiary build a tool for companies who do customer support.” Would you keep texting?
That’s the real travesty—when the price of obtaining mental health help in a crisis is becoming grist for the profit mill. And it’s not just users of CTL who pay; it’s everyone who goes looking for help when they need it most.
Americans need help and can’t get it. The huge unmet demand for critical advice and help has given rise to a new class of organizations and software tools that exist in a regulatory gray area. They help people with bankruptcy or evictions, but they aren’t lawyers; they help people with mental health crises, but they aren’t care providers. They invite ordinary people to rely on them and often do provide real help. But these services can also avoid taking responsibility for their advice, or even abuse the trust people have put in them. They can make mistakes, push predatory advertising and disinformation, or just outright sell data. And the consumer safeguards that would normally protect people from malfeasance or mistakes by lawyers or doctors haven’t caught up.
This regulatory gray area can also constrain organizations that have novel solutions to offer. Take Upsolve, a nonprofit that develops software to guide people through bankruptcy. (The organization takes pains to claim it does not offer legal advice.) Upsolve wants to train New York community leaders to help others navigate the city’s notorious debt courts. One problem: These would-be trainees aren’t lawyers, so under New York (and nearly every other state) law, Upsolve’s initiative would be illegal. Upsolve is now suing to carve out an exception for itself. The company claims, quite rightly, that a lack of legal help means people effectively lack rights under the law.
The legal profession’s failure to grant Americans access to support is well-documented. But Upsolve’s lawsuit also raises new, important questions. Who is ultimately responsible for the advice given under a program like this, and who is responsible for a mistake—a trainee, a trainer, both? How do we teach people about their rights as a client of this service, and how to seek recourse? These are eminently answerable questions. There are lots of policy tools for creating relationships with elevated responsibilities: We could assign advice-givers a special legal status, establish a duty of loyalty for organizations that handle sensitive data, or create policy sandboxes to test and learn from new models for delivering advice.
But instead of using these tools, most regulators seem content to bury their heads in the sand. Officially, you can’t give legal advice or health advice without a professional credential. Unofficially, people can get such advice in all but name from tools and organizations operating in the margins. And while credentials can be important, regulators are failing to engage with the ways software has fundamentally changed how we give advice and care for one another, and what that means for the responsibilities of advice-givers.
And we need that engagement more than ever. People who seek help from experts or caregivers are vulnerable. They may not be able to distinguish a good service from a bad one. They don’t have time to parse terms of service dense with jargon, caveats, and disclaimers. And they have little to no negotiating power to set better terms, especially when they’re reaching out mid-crisis. That’s why the fiduciary duties that lawyers and doctors have are so necessary in the first place: not just to protect a person seeking help once, but to give people confidence that they can seek help from experts for the most critical, sensitive issues they face. In other words, a lawyer’s duty to their client isn’t just to protect that client from that particular lawyer; it’s to protect society’s trust in lawyers.
And that’s the true harm—when people won’t contact a suicide hotline because they don’t trust that the hotline has their sole interest at heart. That distrust can be contagious: Crisis Text Line’s actions might not just stop people from using Crisis Text Line. It might stop people from using any similar service. What’s worse than not being able to find help? Not being able to trust it.
Fears of Artificial intelligence fill the news: job losses, inequality, discrimination, misinformation, or even a superintelligence dominating the world. The one group everyone assumes will benefit is business, but the data seems to disagree. Amid all the hype, US businesses have been slow in adopting the most advanced AI technologies, and there is little evidence that such technologies are contributing significantly to productivity growth or job creation.
This disappointing performance is not merely due to the relative immaturity of AI technology. It also comes from a fundamental mismatch between the needs of business and the way AI is currently being conceived by many in the technology sector—a mismatch that has its origins in Alan Turing’s pathbreaking 1950 “imitation game” paper and the so-called Turing test he proposed therein.
The Turing test defines machine intelligence by imagining a computer program that can so successfully imitate a human in an open-ended text conversation that it isn’t possible to tell whether one is conversing with a machine or a person.
At best, this was only one way of articulating machine intelligence. Turing himself, and other technology pioneers such as Douglas Engelbart and Norbert Wiener, understood that computers would be most useful to business and society when they augmented and complemented human capabilities, not when they competed directly with us. Search engines, spreadsheets, and databases are good examples of such complementary forms of information technology. While their impact on business has been immense, they are not usually referred to as “AI,” and in recent years the success story that they embody has been submerged by a yearning for something more “intelligent.” This yearning is poorly defined, however, and with surprisingly little attempt to develop an alternative vision, it has increasingly come to mean surpassing human performance in tasks such as vision and speech, and in parlor games such as chess and Go. This framing has become dominant both in public discussion and in terms of the capital investment surrounding AI.
Economists and other social scientists emphasize that intelligence arises not only, or even primarily, in individual humans, but most of all in collectives such as firms, markets, educational systems, and cultures. Technology can play two key roles in supporting collective forms of intelligence. First, as emphasized in Douglas Engelbart’s pioneering research in the 1960s and the subsequent emergence of the field of human-computer interaction, technology can enhance the ability of individual humans to participate in collectives, by providing them with information, insights, and interactive tools. Second, technology can create new kinds of collectives. This latter possibility offers the greatest transformative potential. It provides an alternative framing for AI, one with major implications for economic productivity and human welfare.
Businesses succeed at scale when they successfully divide labor internally and bring diverse skill sets into teams that work together to create new products and services. Markets succeed when they bring together diverse sets of participants, facilitating specialization in order to enhance overall productivity and social welfare. This is exactly what Adam Smith understood more than two and a half centuries ago. Translating his message into the current debate, technology should focus on the complementarity game, not the imitation game.
We already have many examples of machines enhancing productivity by performing tasks that are complementary to those performed by humans. These include the massive calculations that underpin the functioning of everything from modern financial markets to logistics, the transmission of high-fidelity images across long distances in the blink of an eye, and the sorting through reams of information to pull out relevant items.
What is new in the current era is that computers can now do more than simply execute lines of code written by a human programmer. Computers are able to learn from data and they can now interact, infer, and intervene in real-world problems, side by side with humans. Instead of viewing this breakthrough as an opportunity to turn machines into silicon versions of human beings, we should focus on how computers can use data and machine learning to create new kinds of markets, new services, and new ways of connecting humans to each other in economically rewarding ways.
An early example of such economics-aware machine learning is provided by recommendation systems, an innovative form of data analysis that came to prominence in the 1990s in consumer-facing companies such as Amazon (“You may also like”) and Netflix (“Top picks for you”). Recommendation systems have since become ubiquitous, and have had a significant impact on productivity. They create value by exploiting the collective wisdom of the crowd to connect individuals to products.
Emerging examples of this new paradigm include the use of machine learning to forge direct connections between musicians and listeners, writers and readers, and game creators and players. Early innovators in this space include Airbnb, Uber, YouTube, and Shopify, and the phrase “creator economy” is being used as the trend gathers steam. A key aspect of such collectives is that they are, in fact, markets—economic value is associated with the links among the participants. Research is needed on how to blend machine learning, economics, and sociology so that these markets are healthy and yield sustainable income for the participants.
Democratic institutions can also be supported and strengthened by this innovative use of machine learning. The digital ministry in Taiwan has harnessed statistical analysis and online participation to scale up the kind of deliberative conversations that lead to effective team decisionmaking in the best managed companies.