For many of the people served by the humanitarian sector, 2024 has been the worst of times. The most recent UN estimates of those forced to flee violence and disaster is a record of 120 million, a figure that has doubled in the past decade. The broader figure of those in humanitarian need, 300 million people, has been swelled by increasingly violent conflict and growing impacts of the climate crisis. Progress in meeting the UN's Sustainable Development Goals has also been either stagnating or declining in more than half of the fragile countries. A child born in those countries has a tenfold greater chance of being in poverty than one born in a stable state.

The constantly shifting paradigms of humanitarian actions, owing to the increased complexity and range of needs, has resulted in a steep demand for innovation in the area. Technological advancements, like Big Data analytics and AI, have proved effective and efficient for humanitarian applications to date. However, like any other innovation, it has also introduced new challenges and risks, making the end-user vulnerable to its repercussions. Redesigning AI seems to be the most plausible solution to accommodate the dynamics of various parameters within the sector. The world has been in constant strife to meet people's demands while improving the efficiency in the humanitarian sector since the very beginning.

Allison Fine and I have been looking at artificial intelligence, nonprofits, and philanthropy. While this particular research was focused on giving and fundraising, we have looking at topic with a broader lens. As we continue to explore this topic, I'll be posting more regular updates about new developments. I was really excited to discover research on artificial intelligence that intersects with my work on nonprofit workplace wellbeing (The Happy Healthy Nonprofit). The Partnership on AI's "Framework for Promoting Workforce Wellbeing in the AI-Integrated Workplace" provides a framework and practices to guide employers, workers, and other stakeholders towards promoting workforce wellbeing as AI becomes integrated into the workplace.

The effectiveness of machine learning algorithms depends on the qua lity and amount of data and the operationalization and interpretation by the human analyst . In humanitarian response, data is often lacking or overburdening, thus ambiguous, and t he time - scarce, volatile, insecure environments of humanitarian activities are likely to inflict cognitive biases. This paper proposes to research the effects of data ambiguity and cognitive biases on the interpretability of machine learning algorithms in humanitarian decision making .

Governments across the world came together in Marrakesh this past December to ratify a pact to improve cooperation on international migration. Among other objectives, the Global Compact for Migration seeks to use "accurate and disaggregated data as a basis for evidence-based policies." How can machine learning technologies help with deeply polarizing societal issues like migration? In early 2018, with support from IBM Corporate Citizenship and the Danish Ministry for Foreign Affairs, IBM and the Danish Refugee Council (DRC) embarked on a partnership aimed squarely at the need to better understand migration drivers and evidence-based policy guidance for a range of stakeholders. At the recent THINK Copenhagen keynote, the Secretary General of the DRC, Christian Friis Bach, presented the first results of this effort.