How artificial intelligence can predict suicide attempts before they occur
When horrible news — like the 2015 Paris attack, or Robin Williams’s suicide — breaks, crisis counseling services often get a deluge of calls from people dealing with despair. Deciding whom to help first can be a life-or-death decision.
At the Crisis Text Line, a text messaging–based crisis counseling hotline, these regular deluges overwhelm the human staff. So “we’re asking a computer to figure out” whom to help first, says Bob Filbin, the chief data scientists at CTL.
Using machine learning, a type of artificial intelligence, Filbin’s team can pull out the words and emojis that signal a person may harm themselves. The computer tells them who on hold needs to jump to the front of the line to be rescued.
Filbin can do this because CTL does something radical for a crisis counseling service: It collects a massive amount of data on the 30 million texts it has exchanged with users. While Netflix and Amazon are collecting data on tastes and shopping habits, the Crisis Text Line is collecting data on despair.
The data — some of which is available here — has turned up all kinds of interesting insight. For instance, Wednesday is the most anxiety-provoking day of the week. Crises involving self-harm often happen in the darkest hours of the night.
CTL started in 2013 to serve people who may be uncomfortable talking about their problems aloud or who are just more likely to text than call. Anyone in the United States can text the number “741741” and be connected with a crisis counselor. “We have around 10 active rescues per day, where we actually send out emergency services to intervene in an active suicide attempt,” Filbin says.
Sending help to people in crisis is just the start. CTL hopes its data could one day actually help predict and prevent instances of self-harm from happening in the first place. Recently I talked to Filbin about what data science and artificial intelligence can learn about how to help people. This conversation has been edited for length and clarity.
Tell me about the service Crisis Text Line provides.
The idea is that a person in crisis can reach out to us — no matter the issue, no matter where they are — 24/7 via text and get connected to a volunteer crisis counselor who has been trained. The great thing about that is people have their phones everywhere: You can be in school, you can be at work, and whenever a crisis occurs, we want to be immediately accessible at the time of crisis.
[Suicide attempt] is the greatest type of risk that we’re trying to prevent.
You’re also collecting data on these interactions. Why is that necessary to run the service?
We have 33 million messages exchanged with texters in crisis. We have had over 5,300 active rescues [where they’ve dispatched emergency services to someone attempting suicide] and the entire message and conversations associated with those.
Our data gives the rich context around why a particular crisis event happened. We get both the cause and the effect. And we get how they actually talk about these issues. I think it’s going to provide a lot more context on how can we actually spot these events and prevent them.
How can we spot these events before they occur? Seeing the actual language that people use is going to be critical to [answer] that.
From the very beginning, we believed in the idea that our data could help to improve the crisis space as a whole. By collecting this data and then sharing it with the public, with policymakers, with academic researchers … it could provide value to people in crisis whether or not they actually used our service.
So, someone in crisis texts your service. I’m curious about the specific data you’re collecting from that interaction.
There are three types of data we’re collecting:
The conversation — the exchange between a texter and a crisis counselor, and then a lot of metadata around those conversations (timestamps; the people who were involved: the crisis counselor, the texter).
