Woebot— Your AI Cognitive Behavioral Therapist: An Interview with Alison Darcy

Part of the Bot Master Builders Series, Alison Darcy was a clinical psychologist at Stanford before founding Woebot.

Woebot is your 24/7 cognitive behavioral therapy (CBT) chatbot. Woebot is genderless but the company refers to it with the pronouns “he”/”him”. While the field of mental health is classically more feminine and social-relational, Woebot’s users’ gender distribution is equally split between men and women. Currently Woebot sends 2 million messages a week to users in over 135 countries across the globe. I estimate Woebot has a few hundred thousand monthly active users (MAUs) in early 2018.

So what’s the big problem here? To replace human therapists? Not really. A recent estimate suggests that roughly one in five Americans have a mental illness, and close to two-thirds have gone at least a year without treatment. The big problem is that many people cannot see human therapists. More than 106 million people — nearly a third of the US — live in areas that are federally designated as having a shortage of mental-health-care professionals, according to the Kaiser Family Foundation. In a 2013 study, mental health disorders were the top medical condition with the highest spending, around $202bn.

Woebot does not replace humans or practice therapy (for which you need years of training and a license) — rather it fills in the gaps for humans who cannot get therapy and need some help. And unlike therapists who don’t scale, Woebot can talk to millions of people simultaneously. Woebot right now practices cognitive behavioral therapy (CBT), a type of talk therapy for emotional problems. Users talk to the chatbot in a structured way for a limited number of sessions. He helps you become aware of inaccurate or negative thinking so you can view challenging situations more clearly and respond to them more effectively. Woebot may chat with you or deliver videos teaching you how to change patterns.

Unlikely many other online, chatbot therapy options, Woebot is backed by clinical research done by his creator Alison Darcy, who was a clinical psychologist at Stanford. She tested a version of the technology on a sample of 70 real people with depression and anxiety long before launching it. The results of the randomized control trial were published in the Journal of Medical Internet Research Mental Health. The participants who said they experienced symptoms of depression and anxiety were split into two groups. One group spent two weeks chatting with Woebot. The other, control group was directed to a National Institute of Mental Health e-book about depression. Over two weeks, people in the Woebot group reported both chatting with the bot almost every day and experiencing a significant reduction in their depressive symptoms. It was a promising, early result for bot-to-human therapy. It also accords with a recent meta-study that compared people who received CBT online with people who received it in person. The study found that the online setting was just as effective.

To use Woebot:

● Facebook Messenger link: m.me/drwoebot

● Cost: Woebot is currently FREE to all users.

Interview with Alison Darcy

(Arun) What is the biggest value proposition you’re seeing right now for users — is it mood improvements? What is the long-term design vision for your bot beyond basic CBT?

(Alison) Thought restructuring is the centerpiece of CBT and the our main focus. It’s where you look at automatic negative thoughts. It takes some guidance to see your own self talk and it can get very critical. We first help people identify this, so they write them down (externalize them and separate from the person). They can then look objectively at the their thoughts. The goal is to think about actual reality and not just your dramatic or extreme perception of reality. The actual experience people have in therapy is so variable, so we are an alternative (we do not provide therapy).

CBT’s main idea is that the actual event isn’t as bad, but our distortions and perceptions are what matter — actively re-writing negative thoughts. So for example, someone losing their job. They feel “I’m a complete failure”. They are responding to the negative thought, not the actual event. Everybody has this negative self-directed thinking and you would never say these negative things to others, but we say them to ourselves. It’s hard to intervene for a mood, but you can tackle a behavior. CBT can hard to teach because it’s rather abstract.

Our goal is to broaden the clinical skills to Dialectical Behavioral Therapy (DBT) — this focuses more on mindfulness and action. In the 1950s, physical fitness wasn’t a thing as physicians didn’t prescribe it. We now realize it’s ongoing and needed. We are trying to do the same for mental health — it’s something we all have to actively manage. My vision is that Woebot helps break that barrier and stigma, from diagnosis and deficit, to taking care of your mental health. Laughs and breakups are part of being human, you need someone to help you. Woebot should be the agent there for you. He can see more people in one day than a human sees in a lifetime. We want to bring really good psychological tools to the masses.

Team — how did your team come together, and what are the roles?

Initially it was myself and Pierre building web prototypes and games. We tried to make video games for CBT for 9 months, things like interactive fiction prototypes for dynamics of engagement. They all had so much dialogue it made sense for us to move to chatbots. Pamela Fox stepped in as our head of engineering and helped rebuild the stack.

My background is in clinical psychology research and I reached out to the best in the field. We all agree that our work doesn’t scale. Athena Robinson, a former Stanford psychiatry professor just joined as our Chief Clinical Officer. Other than that, some of my other colleagues or friends have joined too — people who really care about mental health issues. It makes for a really great workplace — this idea is bigger than any of us. When you look at the data and what people share, it’s so personal and you don’t even hear things like this in human therapy.

How do you measure success; what are your metrics? What are successful interactions? What are failed interactions?

At the end, we always ask: How are you feeling, better, the same, or worse? We look at the failed transcripts and try to troubleshoot on what went wrong — we try to minimize people feeling worse. It’s hard to figure out what we can do for those feeling the same. When you get it wrong people are not shy about telling you — when a button doesn’t cover everything users want to say, users will tell you.

We also use standard search metrics, like precision and recall. For example, to detect people in crisis, we prefer recall search methods over precision as we want to be overinclusive and to identify anyone who could have a problem so we can refer them to human counselors or a help hotline.

Editorial and scripting — what have you learn from flows so far? What have you learned from building rails and making a fun, engaging experiences (with images, gifs, emojis, etc)?

We learn all the time and have learned to keep track of our learning. If you have two buttons, they should represent genuinely different pathways. If there is no natural response or utterance, we use an emoji as an easy button filler. We’ve learned a lot about images. We used to have a black and white image of a bomb and we found it was triggering for certain people, so we had to remove it. Generally I like the black and white images. Many people really dislike minions. There’s a lot due to personal taste here — some people are into videos, others aren’t into it. We have veered away from videos, but we may make some to help teach people difficult techniques. When people are upset, they can only process a little — so our language and scripts have to be really short. We find it easier to have many chat bubbles with 1 or 2 lines — we try to keep it lean and bouncy. There’s almost a rhythm to it — there are a few flows where we can capture help.

What are the most common things users ask outside of the main function — do they ask for jokes or other advice? That is, when they stray, where do they go?

Some users want to be served something else. We always issue an invitation to engage in a conversation. There’s no assumption people want help. Some people just want to chat, they don’t want help. So we try not to explicitly ask people what they are looking for. There really isn’t a lot of open chat away from buttons in Woebot. People may want a past video or lesson.

User acquisition strategy — how do people hear about your bot and start using it?

The only strategy that we’ve done is press — that has been reasonably successful. Interestingly, we got far more conversations as a result of blog article reviews than mainstream press. Bloggers just seem to find us. The tongue and cheek name helped and launching with data and the study helped. Some of the other mental health bots are less than useful and possibly dangerous — so having actual data from a study was important.

Re-triggering and re-use strategy — how do you get initial users to engage again?

The first is to just ask people if they want to converse. I’m not sure if we’ve nailed the invitation. Some of our push navigations were like “beep boop” — we are trying to understand the re-triggering schedule. [Note: Checking in to see what someone’s mental health is is a great reason to re-engage — Woebot naturally does this.]

Monetization? How did you decide to go from charging $39 a month to free?

It was a nice to have a paywall at first to get validation that people would pay for it. We had a decent conversion rate, but I wanted to gather data. Direct to consumer will be the longer term model. People were emailing us saying this is less expensive than our therapist; they were the ones who valued us the most. Convenience was a huge value proposition.

How do you think about the ownership of data and the privacy issues? It sounds like you have it set up so no clinician ever sees a user’s name?

It’s all completely de-identified and anonymized data — nobody in the company can ever see anyone’s Facebook profile. It was to protect the user and to protect us. The guiding principle is transparency. There is no open ended generative conversation — everything is scripted. We want to launch an app to have more privacy.

What can you tell us about your tech stack? Do you do NLP in-house, and what external services do you like?

We use AWS Lambda and NodeJS. Our app will have an animated Woebot tied to NLP that can respond to verbal language with animation. We do our analytics in house as we move to HIPAA compliance for our apps. We don’t want a 3rd party looking at data. We built a dashboard over the summer. It’s hard to find really good data scientists and AI people, and Android engineers.

Thoughts on the different platforms? FB Messenger vs iOS / Android apps? Do you plan on making a voice version?

We are planning an iOS and Android app so people have extra privacy and to be HIPAA-compliant. There’s a good reason to keep it with text and to NOT do it in voice. You cannot see a negative thought in voice, but in a text chatbot you write it out and this externalization helps you overcome it. Messenger was a great launch platform — they make it easy to create and launch a bot. Users loved our prototypes there and we decided to launch there as it was easy.

What other bots have you looked to for inspiration — what other chatbots made you say “WOW”? Are there other use cases you’ve thought were simply brilliant?

I really like ParentSpark — it helps parents with parenting. Their storytelling is great. It’s a mom coaching her kids and they help you as a parent. I also like Jeyant for medical screening; they have over a million users and screen for Zika and other diseases. PullString has done some nice character-based chatbots like Dr. Who. The Mabu chatbot, with eyes that follow you, is neat and I think IDEO designed it.

Other smart people in the chatbot world you’ve met, whether on the tech stack, UX, scripting, or even financing sides?

Andrew Ng, our board Chairman, obviously [a well-known expert in the field of machine learning]. He really sees many use cases for chatbots.

Any final lessons to share with other chatbot builders?

I’m very surprised that people aren’t building more character based chatbots. People want a personality and there is so much scope for really interesting characters. I would still say focus on content before getting too sophisticated on personality and interaction variables. Prototype and test low fidelity versions as much as you can. Constantly refine the conversation. It’s a design exercise.

Read the prior articles in this series:

X.AI’s Amy and Andrew Ingram (Diane Kim)

Rose the Loebner Chatbot Winner (Bruce Wilcox)

Poncho the WeatherCat bot (Greg Leuch)

Howdy and Botkit (Eric Soelzer)

Statsbot for Business Metrics (Artyom Keydunov)

Earplay: What Chatbots can Learn from Interactive Voice Games (Jon Myers)