The MSF REACH platform on a phone in Jakarta 2018

Artificial Intelligence (AI) has become a buzzword within the humanitarian sector in recent years. Much like ‘blockchain’ or ‘drones’, it’s an area where new technology is developing quickly and operators are keen to test its possible applications. From an economic perspective, it’s big business too: from a total of $1.3B raised in 2010 to over $40.4B in 2018, funding has increased at an average annual growth rate of over 48%.

Understanding how exactly AI can positively impact humanitarian field work remains a work in progress. Lack of actionable knowledge about impact, potential, and infrastructure needed for a long-term strategy are slowing the adoption of the technology. Yet, AI-based interventions could: automate time-consuming tasks; aid in data collection and management; enhance user capacities and capabilities; and ensure emergency specialists focus on complex analysis and decision-making. But where and how should these applications be utilised to achieve this potential?

My experience working with AI

For the past three years I have managed REaction Assessment Collaboration Hub (REACH): an emergency support program to enable Médecins Sans Frontières (MSF) act faster in emergencies. REACH combines institutional data with crowd-sourced information (including social media, early alert websites, and relevant RSS feeds) in real-time to provide the organisation with virtual eyes on the ground.

Humanitarian organisations have disaster teams who specifically focus on monitoring emergencies — ensuring that collected data is timely, reliable, and shared with relevant stakeholders. The ability to deliver critical information is currently highly person-dependent, often taking significant time for the relevant information to reach decision-makers during disasters.

REACH’s platform addresses these challenges by providing a quick and more accurate insight into the evolving situation on the ground, which in turn allows for rapidly rolled-out interventions, adapted to the specific needs of an affected area.

In the initial phases of the REACH project, we wanted to integrate AI components into the system. However, it was through extensive research, scoping, interviews and testing that we made a strategic decision to leave these components out of the platform. The following explains why we made that decision based first on three main misconceptions we identified, followed by possible areas of added value.

Three common misconceptions about AI

Misconception 1: AI is the same as other types of automation
There is a general skepticism within the humanitarian sector about ‘automation’ — humanitarian work has traditionally been a sector that relies on human relationships and diplomacy in volatile contexts. To hand such delicate and high-stakes interactions to machines is understandably seen as too risky. However, to extrapolate this to all possible uses of AI in the sector is naive. There are clear situations in which AI can help inform stakeholders, but we require a new understanding of how to design and interact with AI.
More specifically, what is needed is a hybrid solution that combines the experts with the machine. Such a methodology can help us develop this approach and ensure that any solutions are appropriate for the context and address the users’ specific needs. In each and every context, we need to define a goal for the technology to solve. An algorithm should produce reliable data that will support people running operations, not replace them. With this in mind, solutions should not simply be a concept, but real tools enabling end-users to focus on tasks that require human intelligence (i.e. analysis, choices, etc).
Misconception 2: AI will replace human labour
AI interventions are intended to minimise human effort on tasks that can be streamlined, allowing for human skills and interactions to be more meaningfully focused. For example, when we look at the applications of AI in healthcare to date, such as clinical decision support, this is intended to reduce the clinician’s administrative burden and allow for increased face time with patients.
There is an increasing understanding in many sectors that humans will not be replaced by AI but rather supplemented by it. However, it may also be speculated that those who choose to explore and leverage AI applications within this frame may just replace those who refuse to consider AI optimisations. To work effectively, AI requires proficient data managers and data scientists to feed data into the algorithm and maintain it in addition to various other roles to validate and translate AI insights into tangible practices.
To this end, AI works best when:
1. A. Common-sense is not a requirement, and the answers are unambiguous. AI can outperform humans on some complex tasks, but it performs poorly on some others that humans take for granted (e.g. AI cannot answer questions such as ‘How can you tell if my carton of milk is full?’); AI works best in ‘black or white’ binary scenarios. Such as ‘Is my carton of milk is full?’
2. B. Detailed explanations of results are not needed. It can be extremely hard to offer a satisfactory answer to the question ‘Why did the machine give this answer?’ When dealing in unstable contexts or with vulnerable populations, this lack of accountability can have serious implications.
Misconception 3: AI can solve any problem
The success of AI depends on the quality of the dataset. Before an algorithm can operate on a dataset, the data needs to be processed and cleaned so that the results produced by the algorithm are not skewed or imprecise.
Cleaning data is laborious. Given the value of clean and structured data, an important design choice for a socio-technical system is how many resources to use up-front to ensure that the inflow of data is structured and stored appropriately. To build a high-quality database, the platform should incentivise users to input data abundantly and in the correct formats. It should have data managers to monitor the process and clean the database. With a pre-existing high-quality database, solutions can be adapted to harness the power of AI, but this option involves costs and design choices at the very beginning of the project/program.

How AI can add value the humanitarian sector

In the humanitarian sector, there are some specific areas already where AI may be harnessed for specific tasks to add value. These are:

Predictive Analytics - Predictive models of humanitarian crisis (such as: migration patterns during conflicts, famines, epidemics, or natural disasters) allow for early preparation. These predictive analyses may also be leveraged for the improvement of workflows and the optimisation of supply chains. The Forced Migration Forecast developed by a team of scientist at the university of Brunel in London is an example of this.

Image recognition - Used to identify disaster zones from satellite or drone data. Something currently being used by the Humanitarian OpenStreetMap Team.
Natural language processing - Semantic models allow for complex searches for navigating information. This may be performed through: chatbot style interactions, speech recognition, transcription, and translation for various communication tasks. These tools can be rolled out to help people adapt to new contexts (i.e. due to forced migration) and better understand how to navigate their new surroundings and services.
Adaptive web design - Sites that offer personalised interactions based on users’ behaviour. Allowing, for instance, prioritisation of the most relevant information for that user.

Smoothing the implementation of ai in humanitarian contexts

Humanitarian organisations need to invest in educating their personnel on relevant points of progress in other sectors. In any organisation, one of the major limiting factors of adopting AI is identifying expertise that can determine if AI is actually the right answer to a specific challenge. Education and knowledge transfer should happen frequently and bring the basic expertise to the workforce; enabling all staff members to understand how to, for instance, input data and set up data structures etc. With this in place it is possible to get the most return of investment from the technology application to a certain context or problem.

Also, it is very important to educate staff to engage with what has been tested — successfully or not — in order to learn from the others. It is very important to share lessons learned and new reports and publications should to be digested in order to stay up to date. For example the essay published by UNHCR and this publication written by IFC, a member of World Bank Group.

Given how resource-intensive creating AI solutions is — from data sourcing and cleaning, to validating the output — obtaining organisational buy-in with proper consideration of its risks and benefits is currently rare in the humanitarian sector. We must acknowledge that AI is still at a relatively nascent stage and a plethora of potential applications are still being tested and validated; mostly in high-income or private sector contexts. However, it is expanding in the humanitarian sector and low income contexts… albeit a little slowly.

One key final consideration for digital humanitarian projects and actors today is to focus on building large datasets that are clean and structured so that AI models could be trained on the data in the future. Mobile phones and other devices for data collection are already key components in humanitarian response and international development programs — offering a potential ready-to-use goldmine of insights, if structured correctly. Adding algorithms and automation to this well-structured data, allows for the fast identification of patterns in the data that can inform decisions and real-time analysis for a greater impact for your operations in the field.

ABOUT lucie AND OUTSIGHT INTERNATIONAL

Lucie studied at the Université Catholique de Louvain in Belgium. For the past three years, she has managed the MSF REACH project — researching AI and machine learning in the humanitarian context. More widely, she focuses on digital implementation for the development and humanitarian sector. She believes that digital solutions can be harnessed in order to increase the efficiency of the humanitarian sector and the service provisions for the most vulnerable.

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 Lucie and the Outsight team, please get in touch or follow us on LinkedIn for regular updates.