Designing a new communication channel – the Food Bot

Kenya blog 2

WFP/Lucia Casarin

After missions to several field locations (including Nigeria, Haiti, and Kenya) aimed at assessing the feasibility of deploying chatbots in WFP’s operational contexts, the mVAM team concluded that they offer great potential for both the sharing and receiving of useful information on food security.  It is now time to take a step forward and actually build a chatbot for WFP – the Food Bot!

In case you haven’t been tracking our work on chatbots (about which you can learn more here and here), here’s a quick refresher. A chatbot is a computer program designed to simulate conversation with human users over the Internet; imagine an invisible robot living inside the Internet asking you questions.

Tailoring the chatbot to its users

The first step needed in designing a new tool is to garner a strong understanding of its users – who will be using the chatbot and for what purposes?

In our case, we are working simultaneously on two levels:

  1. Chatbot builder tool: this is an interface where WFP staff will be able to design, deploy, and manage customized chatbots. The primary users of the chatbot builder tool will be WFP staff in the field, who will use the platform to design contextually-appropriate chatbots for their location. As you can imagine, each WFP Country Office envisions using the chatbot for a specific purpose. In Kenya, for example, colleagues are eager to deploy a chatbot to share updated information about WFP food and cash distributions as well as other programmatic details. In Nigeria, on the other hand, staff want to share details on how to use nutritional supplements provided by WFP.
  2. Contents within the chatbot: this refers to the information the chatbot provides and the dialogues between the chatbot and its users. Targeted users for the chatbot are people living in marginalized and food insecure communities who can use the chatbot to receive information from WFP. They can also ask us questions about WFP’s programmes in their area and provide their feedback and complaints. WFP will develop different chatbots for different locations and target populations.
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WFP/Lucia Casarin

To get to know our users better and start defining the design of the Food Bot, WFP and our technical partner InSTEDD (who has extensive experience designing innovative mobile tools) travelled once again to the Kakuma Refugee Camp, located in Western Kenya, where we spent a few days collaborating with WFP staff and refugees to understand how to create a user-friendly chatbot to meet their needs.

We first worked with a small group of refugees to better understand how they use the chatbot technology. To do so, we employed a popular prototype technique called ‘Wizard of Oz’. Under our supervision and guidance, refugees were asked to visit a Facebook page and start a conversation with what they believed was a WFP chatbot. Instead, they were actually chatting with our colleague. Through this type of human-centered approach, we were able to quickly learn what types of information the Kakuma refugees were interested in receiving as well as how they were asking questions. During the field test, we also confirmed our hypothesis that chatbot conversations need to be as light as possible (not using many pictures, menus, or emoticons) in order to minimize data charges and make conversations possible when network coverage is weak or the user is employing Messenger Lite.

We then spent some time with our WFP colleagues in the Kakuma and Nairobi Offices brainstorming the ways in which the chatbot could complement existing activities and provide useful information for our work.

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WFP/Lucia Casarin

An iterative design approach

We are now dedicating the next few months to developing the chatbot builder and refining the chatbot contents for a larger pilot project in Kenya. Building a new platform will require a lot of trial and error, and we know that we’ll not get everything right on the first try. For this reason, we have now begun an interactive, iterative design approach, meaning that we will carry out multiple field tests along the way to further refine our product. This will allow us to collect valuable feedback from users at each stage of development so that we can mitigate potential issues early on.

Stay tuned during the coming months as we share additional information on the development of our very first Food Bot!

New places, new tools: what’s up next for mVAM?

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We’ve just got back from Rwanda where we were holding a workshop on using mVAM to expand real-time food security and nutrition monitoring with Internally Displaced Persons (IDPs) and refugee populations. The project, which is made possible by the support of the Korean International Cooperation Agency (KOICA), will be implemented in ten countries in sub-Saharan Africa where WFP works.

What’s the project?

The KOICA project has two aims. First, it aims to empower information exchange with marginalized populations, specifically IDPs and Refugees. Secondly, it supports the collection of food security and nutrition data using the latest mobile and satellite technologies. This will happen in ten countries in Sub-Saharan Africa: the Central African Republic (CAR),The Democratic Republic of Congo (DRC), Kenya, Malawi, Niger, Nigeria, Rwanda, Somalia, South Sudan and Uganda.

How are we going to do this?

As you know, two-way communication systems are an important part of our work. As well as getting information that we can use to inform WFP programmes, we want to ensure that the line is open so that people in the communities we serve can contact us and access information that is useful to them. We’ve already been using Interactive Voice Response and live calls to share information with affected populations, and are now expanding our toolbox to include new technologies: Free Basics and a chatbot.

Remote data collection isn’t just done by mobile phones – VAM already uses other sources, such as  satellite imagery analysis – to understand the food security situation on the ground.  Under this project, we’ll also help countries incorporate similar analysis which will complement two-way communication systems to provide a fuller picture of the food security situation.

Finally, we’re going to harness our knowledge of Call Detail Records analysis: de-identified metadata collected via cell phone towers about the number of calls or messages people are sending and which towers they are using. We have already used this technique in Haiti to track displacement after Hurricane Matthew, and we’re really excited to transfer these ideas to another context to ensure we get up-to-date information on where affected communities are so we can better target food assistance in the right locations.

What happened at the workshop?

Representatives from all 10 country offices, three regional bureaus and staff from HQ came together to discuss the three main project components. During the workshop, the different country offices had the chance to learn more from members of the mVAM team about the specific tools they can harness and ensure their collected data is high quality, standardised and communicated effectively. However, the best part about bringing everyone together was that country teams could share their experiences about how they are already using mVAM tools. We heard from the Malawi country office about their Free Basics pilot, and Niger and Nigeria explained how they’re implementing IVR so affected communities can easily contact WFP, even after work hours. Sharing their different experiences and learning about how different tools have worked in each context not only gave everyone an overview of what mVAM is doing so far, it also helped everyone understand the implementation challenges and how to overcome them.

What’s next for the KOICA project?

We’re really excited for the next stage of the project. Each country office has now planned what tools they’re going to use to increase their communications with affected communities and how they will improve their existing data collection systems. It’s going to be great to see the impact these tools will have not only on WFP’s response, but also how they will empower the communities we’re serving. 

Our experiment using Facebook chatbots to improve humanitarian assistance

Testing the chatbot in Nigeria

Testing the chatbot in Nigeria

It must have been above 40 degrees Celsius that afternoon in Maiduguri, Nigeria. Hundreds of people were waiting to cash the mobile money they receive from the World Food Programme (WFP), sitting under tarps that provided some protection from the sun – in other words, the perfect time to sit and chat.

“How many of you have smartphones?” we asked. We waited for the question to be asked in Hausa, and out came mobile devices of all shapes and sizes. “How many of you have Facebook accounts?” Even before the question was translated, we saw nods all around.

“Of course we’re on Facebook – it’s the way we can message friends and family”

Displaced people in Nigeria, even those facing famine and urgently need aid, are connected and rely on messaging apps.

A leap of faith: from SMS to chatbot surveys

Collecting information in communities on the humanitarian frontline is dangerous, cumbersome and expensive, particularly in conflict settings. In north-east Nigeria, our assessment teams travel by helicopter or in convoys, and some locations are simply too insecure to visit at all. This means that decisions about emergency food assistance are sometimes made with very limited information.

But increasing access to mobile phones is changing this. WFP’s mobile Vulnerability Analysis and Mapping (mVAM) project has adopted SMS, Interactive Voice Response and call centres to collect food security information from communities enduring crises like Ebola or the Syrian civil war. Nelsen, a global information and measurement company, found that using SMS we are able to run our surveys 50% cheaper and 83% faster than we would have for face-to-face surveys, while putting no enumerators in harm’s way. The system’s success means we’re now using mobile tools to collect and share information in 33 countries.

Our successes with automated surveys meant we were keen to look into using chatbots (automated assistants that are programmed into messaging apps) to collect food security data. We were especially curious about the fact that a bot could help us ‘chat’ with thousands of people simultaneously and in real-time, like others have.

chatbot interaction

A sample chatbot interaction

To reach as many people as possible, we decided to create a bot that would operate on a popular messaging app, like Facebook Messenger or Telegram, so people could take our surveys on a platform they already use.

You might think it’s unreasonable to expect people in conflict settings to be connected at all. But, as our Nigeria example shows, their connection is a lifeline to normality. We also found that in many countries operators sell ‘social bundles’ that offer unlimited Facebook, WhatsApp or other social media for a single low price.

Where ‘Facebook Lite’ is available, people can even connect for free. All this means that communicating with vulnerable communities could happen in real time and at little to no cost to the respondent or WFP.

Introducing Food Bot

Last summer, we decided to try it out. InSTEDD developed a chatbot prototype that we demoed with Sub-Saharan African migrants in Rome. The demo asked the respondent to share information about food security in their community and allowed them to look up updated food prices.

Our testers liked the fact that talking to our bot felt like having a conversation with a real person. We felt like we were on to something! Earlier this year, Nielsen helped us further develop a chatbot design that calls for multiple gateways, natural language processing capabilities, and a reporting engine.

The current version of Food Bot is programmed to ask a predefined set of questions to the user – it does not rely on artificial intelligence yet. Food Bot goes through a simple questionnaire and saves the answers so that our analysts can process them.

The chatbot format also lets users ask us questions and is a channel for us to give useful information we’ve collected back to these communities. These include messaging on WFP programmes, food prices, weather updates, nutrition and disease prevention. The version we are using for testing currently runs on Facebook Messenger, but we want to make sure it works on all the relevant messaging apps.

No walk in the park

Before we get carried away, we need to consider some of the very real challenges. A timely report by the ICRC, Block Party and the Engine Room emphasizes the new responsibilities that humanitarian agencies assume as they make use of messaging apps to communicate with affected populations. Notably, the use of chat apps to collect information from people who have fled their countries or home raises the important issue of responsible data practices. If we are ever hacked, people’s personal details could be put at risk, including names and pictures. We will certainly have to review our existing data responsibility guide and continue obtaining advice from the International Data Responsibility Group (IDRG), as well as build an understanding of data responsibility principles in the field.

We also suspect that the audience we reach through Food Bot will be younger, better off, more urban and more male than the general population. The convenience of collecting data through a bot does not dispense with the hard task of seeking out those who are not connected and who are probably the most vulnerable. We want to explore ways to make our bot as accessible as possible like translating text into local languages, using more icons in low literacy settings and working with civil society organisations that specialize in digital inclusion.

Finally, we realize that we must prepare to manage all of the unstructured information that Food Bot will collect. Colleagues in the field are already weary of collecting yet more data that won’t be analysed or used. As a result, the team is working on setting up the infrastructure that is needed to process the large volumes of free text data that we expect the bot to produce. This is where our work with automated data processing and dashboards should pay dividends.

This post was originally published on ICT Works as part of a series on humanitarian chatbots.

If you’re not human then who are you?

Experimenting with chatbots in Nigeria and Haiti

WFP/Lucia Casarin

Testing the bot in Haiti – WFP/Lucia Casarin

Readers of this blog know that the team has been experimenting with chatbots to communicate with disaster-affected communities – read our previous posts about our prototype and the Nielsen Hackathon.

As part of this effort, during recent missions to Haiti and Nigeria, our team went out to talk to communities to find out whether a chatbot would be right for them.

Would a chatbot be a stretch in these communities?

Well it’s not that much of a stretch.

In North East Nigeria, most displaced people live in Maiduguri, a city of over 1 million people. In this ‘urban’ setting connectivity is good, most people own cell phones and many young people use social media and messaging apps. Mobile operators have been offering services that allow people to access the internet by selling ‘social bundles’ (unlimited social media access sold in very small increments) and offer some services for free, including Facebook Light and Facebook Messenger.

In Haiti, three-quarters of the population live in the capital, Port-au-Prince, where 3G connectivity is good and most people use messaging apps to communicate with friends and family. Even in rural and difficult-to-reach communities, leaders and young people own smartphones and connect to the internet. There is a lot of competition between mobile operators so the prices for mobile data are very low. This means that most people can afford to access the internet either via their own smartphone or from shared smartphones.

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Mobile phones charging station on the road from Léogane Peri to Port-au-Prince WFP/Lucia Casarin

A bare-bones demo

In both countries we tested a simple chatbot that asks people about food prices and what the food security is like in their community. The survey we used was much more basic than our usual mobile questionnaires as we felt it was important to keep things simple at this stage.

For Nigeria, the bot demo was initially in English but we soon translated it into Hausa, the primary language spoken by displaced persons in Maiduguri. In Haiti we made it available both in Creole and French. The chatbot was very responsive on 3G and it even worked with slower 2G connections so the technology works in these contexts. But this was only the starting point, what we really wanted to know was what ‘real’ people thought about the bot.

We organized focus group discussions with displaced people in Maiduguri and with community representatives in Haiti. We helped people access the WFP bot via their Facebook accounts, and they began chatting away.

Sounds cool, but what are the limitations?

Here’s what people said:

First of all, people thought the bot is a convenient, quick, and easy way to get in touch directly with WFP and they really liked that the bot allows them to speak to WFP without intermediaries. They had lot to tell us particularly through the open-ended question where they typed out detailed responses.

In Nigeria, they did tell us that our (somewhat wordy) English-language demo should be translated into Hausa because it would make it easier for everyone to use. Our first group of testers were young people who were already Facebook users and so were familiar with Messenger. It was therefore no surprise that they were interacting smoothly with the bot and able to go through our questionnaire in minutes.

WFP/Jean-Martin Bauer

Testing the bot in Nigeria – WFP/Jean-Martin Bauer

In Haiti, people started interacting with the bot as if it was a human rather than an automated questionnaire so they got stuck pretty fast when it wasn’t as naturally responsive as they’d expected. This means that either we give clearer instructions to people or we add Natural Language Processing capabilities to our bot.

There are of course other barriers. In both countries women appeared to be less likely to own a smartphone. This means that bot users will likely be overwhelmingly young, male and better educated than other people – hardly ‘representative’ of WFP’s target affected population. The free version of the bot is also not always available: in Nigeria only Airtel subscribers can access it, while in Haiti the free service doesn’t exist yet.

This means that the bot would need to be a complement to the other tools we have. We might use data from the bot to obtain a quick situation update, but we will continue relying on other sources for more representative data.

How many pizzas does it take to build a chatbot?

Hackers are hungry Photo: WFP/Pia Facultad

Hackers are hungry
Photo: WFP/Pia Facultad

This week, government, business, academia and civil society leaders will gather at Davos to discuss solutions to the world’s biggest challenges – including how new technologies can be leveraged to solve some of the most serious problems we face. At mVAM, we continue to explore how some of these technologies could be used to help eliminate chronic hunger, malnutrition and food insecurity – most recently looking at how chatbots could help collect important information during a humanitarian response.

Last week, our collaborators at Nielsen – one of the early supporters of mVAM – organized a 24-hour hackathon at the Nielsen Tech Hub in New York City. As part of ongoing efforts through Nielsen Cares, the hackathon aimed to develop an open-source humanitarian chatbot that can collect real-time information about food security. This came at the right time for WFP – we’d developed and tested a prototype of the chatbot with InSTEDD, and Nielsen’s technology and development input helped bring in important new capabilities. Ultimately, our goal is to field-test a chatbot in Haiti in the next few months to help us track food security conditions as people recover from the impacts of Hurricane Matthew.

The event was open to the public. A diverse group of students, volunteer hackers, and Nielsen staff showed up to take on the challenge, despite the wintry weather. InSTEDD’s Director of Platform Engineering, Nicolás di Tada also participated.

Much more than a chatbot

What the hackers built is much more that a chatbot: it is a bona-fide chat-based data collection and reporting system. Rather than attempt to outdo each other (as is the case in most hackathons), the teams split up to build the different components of the system. The different teams, made up of perfect strangers, communicated during the hackathon through Slack. After 24 hours, most components were fully coded up, but there were still bugs with the orchestrator and the gateway that additional post-hackathon work will resolve.

The architecture of the system, as defined by Nielsen, includes:

  • a management interface that allows an analyst to set up a questionnaire, including and skip logic, and validation rules that prompt the user when they enter a wrong answer. The interface was built using the Angular 2 JavaScript framework;
  • a gateway that is able to interact with respondents through Facebook Messenger and potentially other chat applications. The Facebook gateway was built on top of the AWS Lambda service;
  • a natural language processing engine that analyzes text on the fly. It allows the chatbot to ‘interpret’ a user’s answers. For now, the NLP engine processes English language text, although the engine includes a translation service and, by default, translates all languages to English for more advanced NLP tasks. The engine was built using the AWS Lambda service and leverages IBM Watson’s AlchemyLanguage service for text processing.;
  • a set of ‘backend APIs’ that manage respondent and survey data, route respondents from each response to the next question, and provide data to user interfaces .  The APIs were built using the Django framework for python and deploys on the AWS Elastic Beanstalk service;
  • an ‘orchestration layer’ that maintains survey status and routes messages between the end user and the various backend services. The orchestration service is built on top of the AWS Lambda service; and
  • a “reporting and data visualization engine”. Data vizzes were built using Highcharts, a JavaScript-based application. This allows an analyst to instantly see the results of the chatbot surveys.

 

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Leveraging cloud services from the Amazon Web Services product catalog, the teams were able to build a scalable, cost effective platform that can be deployed quickly to multiple locations globally.

Remember the humans

We also received tips from a chatbot specialist, Alec Lazarescu from Chatbots Magazine. He encouraged us to ‘onboard’ users with an initial message that gives people a clear idea of what the chatbot is for. He told us to avoid ‘dead ends’ and allow users to speak to a human being in case they get stuck.

We’re very grateful to Nielsen for their support and to all the participants for their energy and creativity. The next steps involve WFP and InSTEDD accessing the code and work on ironing out the kinks. We expect challenges with the natural language processing in Haitian Creole, a language that is probably under-researched. Making the different parts of the chatbot work together seamlessly also appears to be an area we will still have to work on.  And, of course, the final test will be to see whether our target group – people living in Haiti – find the chatbot engaging.

Our 5 mVAM Highs from 2016

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1. Awards for Remote Mobile Data Collection Work

At the Humanitarian Technology 2016 conference, our paper Knowing Just in Time Knowing Just in Time’ won Best Paper for Outstanding Impact. In the paper, we assessed mVAM’s contribution to decision-making by looking at use cases for mVAM in camps, conflict settings and vulnerable geographies. Check out our blog Tech for Humanity for more on it and our other conference paper  mVAM: a New Contribution to the Information Ecology of Humanitarian Work

To close the year, we had a nice surprise from Nominet Trust, the UK’s leading tech for good funder. We made their 100 most inspiring social innovations using digital technology to drive social change around the world.  

2. New Tech

In this day and age there’s a lot of buzz around data visualization. We’ve been honing our skills with Tableau. Check out the data visualizations we did for Yemen and Haiti.

We’re also in the era of Big Data. We partnered with Flowminder, experts in analyzing call detail records, to track displacement in Haiti after Hurricane Matthew.  Find out more in ‘After the storm: using big data to track displacement in Haiti

We’re also super excited about the chatbot we started developing for messaging apps and our roll out of Free Basics in Malawi which is allowing us to share the food prices we collect in mVAM surveys with people in Malawi With mVAM, our main focus has been reaching people on their simple feature phones. But we know that smartphone ownership is only going to increase. Contacting people through internet-enabled phones opens up loads of new forms of communication and data collection. is still reaching people on their -free basics

3. Expansion!

mVAM expanded to 16 new countries facing a wide set of challenges: conflict, El Nino drought, hurricanes, extremely remote geographies. We’ve been tracking and learning about what remote mobile data collection can add to food security monitoring systems and what its limits are in different contexts. For some of the highlights, check out our blogs on Afghanistan, Democratic Republic of Congo, Haiti, Nigeria, Papua New Guinea, and  El Nino in Southern Africa,

4. Dynamic Partnerships

To have a lasting impact, we need to work with governments. We are really proud of our partnership with CAID, the Cellule d’Analyses des Indicateurs du Développement  under the Prime Minister’s Office in the Democratic Republic of Congo. We collaborated on setting up a national market monitoring system- mKengela that they are now running. We’ve had intensive technical sessions with the CAID team in Rome and Kinshasa to work on solutions that will fit their data management and analysis needs. The CAID team even traveled to Johannesburg to share their remote mobile data experience with other African countries and help other governments use this technology.

We’re also working with Leiden University. Bouncing ideas off of their team at the Centre for Innovation helps us move forward on tricky challenges. We’re also collaborating with them to develop an online course where we’re going to share our methodologies and how to use remote technology to monitor food security. Check out Welcome to Vamistan for more.

We are in the field of tech. So we can’t do our job well without partnering with the private sector. It’s definitely a dynamic area, and also one where we at mVAM are learning what works best in melding our humanitarian goals with the exciting private tech potential out there. Check out our blog From the Rift Valley to Silicon Valley and our hackathon with Data Mission for more.

5. Learning- the neverending process

In addition to trying out new technology, we’ve been trying to answer some important questions about the live calls, SMS, and IVR surveys which make up the bulk of mVAM data collection.  We’re also doing mode experiments to understand how people answer differently based on which mode we use to contact them. Check out our first Mind the Mode article with more coming in 2017. In Kenya, we are looking into whether we can ask nutrition indicators through mVAM methods. A major challenge is reaching women through phone surveys so we organized a gender webinar with partners to learn from what they are doing- check out our key gender takeaways. These are key questions and they can’t be resolved overnight. But we’re making steady progress in understanding them, and we’re excited for what more we’ll find out in 2017.

Thanks to everyone who has supported our work this year and kept up with our blog!

Chatbot: back to the drawing board

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We’ve recently developed a prototype of a chatbot to communicate with people via the Telegram messaging app, but it will eventually work on any messaging app. The purpose of a prototype is to test our approach thus far in the real world and then go back to the drawing board to improve it. Before this month, our testing had been limited to our colleagues here in Rome and our partners at InSTEDD.  However, we really needed feedback from people in the communities we are actually trying to reach.

Eventually we’ll test a later version of the chatbot in the countries where we work. But for some initial feedback, we were able to get in contact with people right on our doorstep, who had completed a difficult journey to Rome. UNHCR recently estimated that there are roughly 65.3 million people currently forcibly displaced worldwide. Instability and conflict in the Middle East and Africa has led many to flee to Europe in the hope of a safer, better life. One of the most used and most dangerous routes is via Libya and then across the Mediterranean Sea to southern Italy. To give you an idea of the scale, between 29 August and 4 September this year, Italy averaged over 2000 arrivals every day. Of those who reach the mainland, many make their way north to Rome. There are now many centres across the city that provide refuge, often in the form of meals, language learning and legal support.  

UN photo/UNHCR/Phil Behan

We went to one of these migrant centres to speak with people and get their feedback on whether the chatbot would be useful in their home communities. We can hear migration statistics but listening to people’s stories really made these statistics come alive. One person we talked to described being saved by the Italian Coast Guard as the ship transporting him sunk in the Mediterranean. He said he will always be grateful to the Italian government for his rescue.

So needless to say, we were very grateful that people would take time and test the bot. Its is currently in English so we were only able to test it with English speakers right now. The goal is to get it running well in English and then translate and adapt it to other languages.

First we asked people a couple of questions about smartphone ownership in their country of origin. They told us that while the poorest people in rural areas don’t have smartphones around 70% of the population does – meaning that we can still communicate with a lot of people via smartphone. They then had a go at using our chatbot, first answering the food security survey and then trying out the price database. Here are a few things that speaking with them helped us realize:

Simplify our questions and build up to them more. We know we spend a lot of our time working with food security surveys and we know our food security questions by heart. We can forget how weird they can sound to everyone else, especially over a chat. For our participants, it was the first time they’d seen something like this so they were at times confused about how to respond to the questions about their diet or their coping strategies. They were especially confused because the questions seemed to come out of nowhere, with no build up or putting them in context. By the second time around, they went through the survey much quicker, but we need to make sure to get the best first time responses. We need to speak normal language, not make everyone else try to speak our specialized jargon.

No one wants to interact with a robot: make the chatbot as chatty and friendly as possible. Our participants also advised us that it would be good to add some slang and colloquial language. But it is important to have it to feel like as natural an interaction as possible: As one of them said: “ If I want to say something or someone to talk to, I can write, and the chatbot can help and I can relax.’

Make it as intuitive as possible. The chabot users will have different backgrounds and tech literacy. Right now, as one of our participants put it, it’s accessible for “any educated person’, but we don’t want to limit our target audience. Our users might not even have secondary education so we want anyone who can use facebook to find it straightforward.img_2350

Make sure the bot recognises typos! Everyone knows how easy it is to make a typo on your smartphone so it’s essential that our chatbot recognises a few of the easy ones. When we ask people how many days in the past week that they ate vegetables for example, it’s pretty easy to give ‘3 days’ ‘three days’ ‘three’ and ‘3days’ and all mean the same thing! Even potentially typos like “theee days” or ‘three dyas”. We need to integrate these differences in text and typos as acceptable responses, asking for confirmation when needed, so we get the best results.

Put the bot on different messaging apps. One of the reasons why they were a bit hesitant with the bot at the beginning was the fact that they were not used to the Telegram app. It’s important for the bot to run on the app people use most. This can vary depending on the country, so when we do our pilot, we need to put the chatbot on the most commonly used messaging app.

Give people food price information for their areas. At the moment, our bot automatically reads the general WFP food price database. Whilst this is a cool way of looking at food prices all over the world, it’s not actually that useful on a day-to-day basis. Our participants said that knowing up-to-date regional prices would be great – as it would allow them to go to different parts of the country to buy food if the price was particularly low there. As we are already collecting high frequency price data, we want to be able to integrate this into the chatbot. This would be a great way to use the real-time data that we collect to give directly back to the people we collect from.

Overall the participants were positive to the bot as it stands, even saying that they think it’s ‘really cool’. However, there’s a lot of work to be done to make it more user friendly. Using this feedback we are going back to the drawing board. We hope to have an even better version for our official pilot in sub Saharan Africa later this year. We are very grateful to the people who helped us test this last week. They have much more pressing things to worry about so we thank them again for generously giving us a bit of time.

Introducing our Chatbot

bot pictureAn important part of our job at mVAM is to stay tuned into the developments in the rapidly evolving mobile technology sector. Lately we noticed two main trends: first, more and more people in the places we work are using smartphones and chat apps to communicate, leading us to think about how to better reach out to this segment of the population. Second, chatbots, robots that live in chat applications, are all the rage and have a big potential to contribute to our work.

What is a chatbot?

A chatbot is a computer programme that uses artificial intelligence to interact with users through a messaging service in a way that is designed to seem like a conversation. We’ve been experimenting with ways to expand our capacity for two-way communication, i.e. contacting local communities but also hearing back from them. A chatbot provides a friendlier, more responsive way to interact with people by letting them communicate more naturally, in a “chat” as the name implies. As well as answering the chatbot’s questions, users can also ask the chatbot simple questions.

Since we piloted mVAM in 2013, we’ve collaborated with InSTEDD, a nonprofit design and technology company that develops innovative open source tools for social impact. For three years, we used their SMS and IVR software to collect food security information. So when we wanted to delve into using chatbots, it was only natural that we reached out to them. Of course, not everyone we want to survey will have access to a smartphone. A large proportion of people using messaging apps at moment are young, urban, and male, introducing a bias to our surveys. But as smartphone ownership becomes more prevalent this won’t always be the case. This technology is really promising so we want to stay on top of it and see how it can be used for humanitarian purposes. As a first step, we want to use a chatbot to conduct a mobile food security survey on a messaging app. At the moment we are using Telegram because they have an API, which allows developers to easily build customized tools, but we are designing the bot so that it can be used on other messaging apps.

Here’s what our chatbot with InSTEDD would look like. Respondents are contacted on Telegram via their smartphones and asked a series of questions, about their food security and livelihood situations just like they would be by phone, SMS, or on our other mVAM modalities.

Check out our chatbot demo:

Why are we so excited about chatbots?

Chatting on a messaging app lets us collect new types of information. People can send our chatbot pictures, voice notes and geolocations that would enrich our food security analysis. As part of our analysis we ask people socio-demographic questions about things like their roof type, which give indications about a household’s economic status. Using the chatbot we can actually get pictures of these answers! We’ll literally see and hear about the situation on the ground and get to double check where these pockets of food insecurity actually are.WhatsApp-Image-20160721

It’s cheap! Not only does the chatbot have the potential to reach more people, the format is also cheaper than SMS, IVR and Live Calls.

It’s way more fun. The chatbot can process more complex sentences and respond more dynamically, letting the user drive the conversation. There’s a whole spectrum of things a chatbot can be programmed to do anything from a stilted, regimented conversation where users can only answer in a certain way, to natural language processing where users can chat as they would with a human. We think the technology just might not be there yet to meet our needs for a completely natural chat- we followed the Microsoft chatbot problem closely. However since we are only focusing on a specific topic we are opting for something in the middle- that allows us to get the food security information we need but also give users a natural, fun experience.

It lets us share more information. The chatbot can automatically read WFP’s food price database and tell people about the food and commodity prices where they live and give information about any big changes in the last few months. This database is so detailed that we can actually provide this information down to the market level in many countries!  Every time a new dataset is added to the original database, the chatbot automatically updates its price information, ensuring that local communities can access the latest information.

It’s flexible. The chatbot doesn’t have to just be used for prices. Users could ask WFP questions about our food distributions or programmes – whatever information we are able to insert in our database. This way we can provide a great incentive for people to complete our surveys, giving our beneficiaries a chance to give us feedback on the services we provide, and sending them a variety of information at a low cost.

Our chatbot is still a prototype, but we will let you know how our testing goes before we roll out our first pilot.