The wide use of chatbots has increased dramatically since the start of the COVID-19 pandemic. Their use as a tool for public information has proven extremely effective over previous dissemination tools such as websites or hotlines, due to their targeted and adapted answers. The use of chatbots has now gained a firm foothold in humanitarian and development organisations (HDOs) — with these organisations looking to provide adapted systems to different communities that they serve.

At Outsight International, we have a number of experiences working with chatbots in the past. In this blog, Devangana Khokhar, Hanna Phelan and Michelle Chakkalakal provide and overview of what chatbots are and how they can be used. Stay tuned for a follow-up blog on key considerations when designing a chatbot.

What’s a chatbot? The Types of chatbots

Chatbots are a relatively recent addition to the world of human computer interaction. While Question-Answering systems have existed for a long time — powered by both rule engines as well as Artificial Intelligence (AI) —, recent advancements in the world of Natural Language Processing (NLP) have made it possible and convenient to build chatbots that are context-aware and optimise the interaction between a human and a machine.

Chatbots come in a number of different forms. At their most basic, they are a tool for automation. Chatbots can vary from very simple ‘rule-engines’ that work like integrated voice recognition (IVR) where users select predefined options to more advanced forms of computer programming that use natural language processing (NLP) and artificial intelligence (AI) to provide genuinely responsive experiences.

Broadly speaking, we can break chatbots down into three main categories:

Rules-/Menu-Based Chatbots: Users can select options using a pre-defined scenario tree, similar to integrated voice recognition menus. You can tell you're using a menu-based chatbot, when the prompt is something like: Hi I'm Pavi, your friendly customer service agent, and I can help you with these issues. Please select your issue from this drop-down menu or press this number/letter etc...
Hybrid Bots: Users can select their issues from a pre-existing menu or they can type their question in. It looks something like this. Hi, I'm John. How can I help you today? Select your issue from this menu OR type in your question below. The more complex the issue, the greater the chance a company is using a combination of pre-defined scenario trees, mechanical turk (a human assisting the language sorting), and training its AI with more queries. This approach works well when a certain set of frequently-asked questions are known along with their answers, thereby solving the cold start problem.
AI Chatbot: Users can interact with the bot by typing their question, and the bot powered by AI and NLP to find the answer. Such chatbots often work with engines that extract and understand the intent as well as the entity/entities tied to that intent from the user’s query. The identified intent as well as the entity/entities are used to query a knowledge base in order to build the context and respond to the user with that context. There have been recent advancements in context and next-step prediction inspired by the use of AI in the gaming world that allows the system to predict the next question that a user is going to ask thereby improving the end user experience.

chatbots in the humanitarian and development sector

As mentioned, chatbots have started to be used by a wide range of actors looking to impart information or provide services to populations quickly and in a more personalised way. For many HDOs, this theoretically fits well with their model of responsive operations that can reach swathes of beneficiary groups in an on-demand manner.

However the success of chatbots is often determined by the consideration that went into their design and implementation. How one looks understands the problem they are trying to solve — taking a system view, involving AI, the user experience, and the feedback loops — determine the longevity of the solution.

To consider the different approaches to chatbots, we have identified two case studies from the sector which we think took differing approaches to chatbot design.

Case study 1: Praekelt.org - Scaling COVID-19 Truths

*World Health Organization: HealthAlert from praekelt.org*

WhatsApps has received a lot of negative press in the past 12-months over concerns with their role as a channel where COVID-19 misinformation was rife, as well as over updates to their privacy policy.

Despite this, many organisations recognised that WhatsApp — with 1.6 billion users — would have to be addressed if evidence-based public health awareness was to prevail. Enter, Praekelt.org and their chatbot-based solution Turn.io. The South African organisation focused on building technical infrastructure to provide users with information hotlines and chatbots to understand which healthcare services were available and what precautions should be taken to remain safe during the pandemic. The solution soon attracted interest from a number of significant healthcare stakeholders including the World Health Organisation (WHO) and governments of Ethiopia and Mozambique. This was the first time WHO has used the WhatsApp for Business API.

Since its launch, the chatbot has been used by over 12 million users around the world, seeing a particular peak in usage in locations experiencing spikes in infections. The offering has since been made available free of charge by Praekelt.org and Turn.io to any ministry of health worldwide.

Beyond the COVID-19 chatbot use case, Turn.io has been developed to address a variety of other needs. One such partnership is in collaboration with Girl Effect, an international non-profit supporting adolescent girls in low- and middle-income countries (LMIC) to make informed health and wellbeing choices. The initial pilot of this chatbot solution was launched in South Africa for girls between the age of 13-17 to answer questions that may be difficult to raise in another forum, concerning emotional, social, and practical elements of sex and relationships. The Girl Effect chatbot has since been tested in three other countries (Nigeria, Philippines and Tanzania), with over 10,000 users having interacted with ‘Big Sis’ — the chatbot’s ‘persona’.

The scale and speed of rollout for this particular solution is impressive. However WhatsApp data privacy concerns will have to be addressed head-on going forward particularly when considering implementations for vulnerable communities.

Case study 2: Babylon Health GP at Hand Decision Support Chatbot

Babylon Health is in many ways a digital health success story now employing over 1,000 people in the UK, US and Rwanda, however it hasn’t all been smooth sailing. Babylon, since their founding in 2013 have embedded themselves in the UK’s NHS — offering a consumer facing AI-powered decision support tool and other telehealth interactions where users can access video or phone consultations with NHS clinicians and book in-person appointments.

The main concern around Babylon has been centred around the ambition of the AI diagnostic and triage chatbot. While the company claimed that the chatbot element was not intended to act as a validated diagnosis, critics pointed to methodological concerns; especially in their claims that the Babylon chatbot outperformed the average human doctor on a subset of the Royal College of General Practitioners exam. Questions included whether the Babylon chatbot would perform as well in real-world situations with data being entered by people with no clinical experience and additionally if it would be as successful in a more unusual situations. According to a recent paper in the BMJ, it would not. There have been calls for independent review of these types of solutions and increased regulatory measures to validate AI healthcare solutions.

Babylon seemed to recognise the risk associated with the chatbot element of their offering when expanding efforts to Rwanda in 2016 and chose a slightly different operating model in this context focusing primarily on phone and SMS services that connect clinicians and community health workers with users.

Conclusion

Given the range of different chatbot solutions available and their diverse applications. Picking the right tool for the job can be daunting. Considering the factors that lead to the success or failure of new chatbot platform will thus be the topic of our next blog where we’ll provide you with key considerations when deciding if to use a chatbot, and how to implement it successfully.

If you've worked with chatbots yourself or interacted with one that stood out to you, either as a success or a failure, we'd love to know about it! Share with us your tales of chatbots or just leave the link to your favourite chatbots in the comments. If you’re looking for chatbot expertise, get in touch with us through our contact form.

ABOUT THE AUTHORS AND OUTSIGHT INTERNATIONAL

Devangana Khokhar
Devangana Khokhar is an experienced data scientist and strategist with years of experience in building intelligent systems for clients across domains and geographies and has a research background in theoretical computer science, information retrieval, and social network analysis. Her interests include data-driven intelligence, data in the humanitarian sector, and data ethics and responsibilities. In the past, Devangana led the India chapter of DataKind. Devangana frequently consults for nonprofit organisations and social enterprises on the value of data literacy and holds workshops and boot camps on the same. She’s the author of the book titled Gephi Cookbook, a beginner's guide on network sciences. Devangana currently works as Lead Data Scientist with ThoughtWorks.

Hanna Phelan
Hanna is an expert in digital health implementation currently working as a health innovation Case Manager with the MSF Sweden Innovation Unit. In the past, she has advised leadership teams in health systems and pharmaceuticals. She received her MSc in Global Health from Trinity College Dublin, during which she conducted field assessments of rehabilitation approaches by Handicap International for Syrian refugee populations in IDP camps and community settings.

Michelle Chakkalackal
Michelle is an experienced entrepreneur, researcher, and impact strategist, specialising in growing a project or an organisation from start to scale, globally. She has 15+ years of experience working in systems change and facilitation at the crossroads of impact, tech, gender, diversity, equity, and inclusion (DEI).

Outsight International
Outsight International provides services to the humanitarian and development sector in an efficient and agile way. Outsight International builds on the range of expertise offered by a network of Associates in order to deliver quality results adapted to the specific tasks at hand. If you’d like to discuss working with the Outsight team, please get in touch or follow us on LinkedIn for regular updates.