Big data philanthropy

This recent article in the New Yorker introduces a new counselling service, based in New York, which uses text messages a point of contact for young people experiencing crisis. It’s an intriguing discussion of changing generational norms regarding communication, offering a rich account of why SMS is so amenable a channel for young people seeking help:

A person can contact Crisis Text Line without even looking at her phone. The number—741741—traces a simple, muscle-memory-friendly path down the left column of the keypad. Anyone who texts in receives an automatic response welcoming her to the service. Another provides a link to the organization’s privacy policy and explains that she can text “STOP” to end a conversation at any time. Meanwhile, the incoming message appears on the screen of Crisis Text Line’s proprietary computer system. The interface looks remarkably like a Facebook feed—pale background, blue banner at the top, pop-up messages in the lower right corner—a design that is intended to feel familiar and frictionless. The system, which receives an average of fifteen thousand texts a day, highlights messages containing words that might indicate imminent danger, such as “suicide,” “kill,” and “hopeless.”

http://www.newyorker.com/magazine/2015/02/09/r-u

However what really stood out to me was the role of data science in this charity’s activities. The CEO of the charity describes how “We think of ourselves a lot more like Airbnb or Uber or Lyft” and this is reflected in the centrality accorded to the analysis of digital data across all their activities:

Like a tech company, C.T.L. analyzes feedback from users, performs A/B testing, and is quick to make changes on the basis of what it finds. Although other data-driven philanthropic missions exist—Kiva, the microfinance site, and the public-school donation service Donors Choose are among the more well known—nonprofits have generally been reluctant to embrace methods of quantification that big corporations increasingly take for granted. But at C.T.L. the chief data scientist, Bob Filbin, was Lublin’s second hire. He co-wrote the data algorithms for C.T.L.’s system after travelling to crisis centers across the country and interviewing hundreds of volunteers about how their work could be made more effective. The communication techniques employed by C.T.L. counsellors are largely modelled on standard crisis-counselling practices, but C.T.L. has made modifications based on its data. It turns out that, for instance, statements couched in the first person (“I’m worried about how upset you seem”) are associated with positive responses.

The organization’s quantified approach, based on five million texts, has already produced a unique collection of mental-health data. C.T.L. has found that depression peaks at 8 P.M., anxiety at 11 P.M., self-harm at 4 A.M., and substance abuse at 5 A.M. The organization is working on predictive analysis, which would allow counsellors to determine with a high degree of accuracy whether a texter from a particular area, writing in at a particular time, using particular words, was, say, high on methamphetamine or the victim of sex trafficking. A texter who uses the word “Mormon” tends to be reaching out about L.G.B.T.Q. issues.

Out of consideration for texters’ anonymity, Crisis Text Line displays its findings only by state. (Arkansas ranks highest for eating disorders, Vermont for depression; suicidal thoughts are most common in Montana and least common in New Hampshire.) But eventually there will be enough data to allow the organization to confidently reveal Zip codes and area codes without the risk of making any single texter identifiable. Such a wealth of data is new in the field of mental health. Isaac Kohane, a pediatrician who also has a Ph.D. in computer science and is the co-director of the Center for Biomedical Informatics at Harvard Medical School, told me, “You cannot have accountable care—financially or morally accountable care—if you cannot count, and until recently we literally could not count with any degree of acceptable accuracy.” He added, “It’s been mind-boggling, to those of us who knew what was available, that Amazon and Netflix were creating a far more customized, data-driven, evidence-based experience for their consumers than medicine has.”

http://www.newyorker.com/magazine/2015/02/09/r-u

While they have thus far resisted pressure to monetise the data by selling access to it, it’s hard not to wonder if this is a stance likely to persist in the event of a shortfall in funding. My point is not to criticise a charitable initiative that is doing important work in an innovative way but only to point out potential longer-term ramifications of this model. It surely represents a seismic shift in philanthrophy which needs to be better understood.


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