"This guide has explored some of the key considerations that should inform the conceptualization and implementation of Machine Learning (ML) and artifical intelligence (AI) components within a development project. New, automated decision systems can offer considerable and rapid efficiency gains, but we must always remember that they embed numerous and ongoing human decisions. These may be intentional or unintentional, benevolent or malicious, general or highly context specific. As with physical infrastructure such as roads and bridges, digital infrastructure can all too easily encode unexamined bias – sometimes in ways that can undermine development gains. As outlined in this guide, a wide variety of decisions need to be made at different stages of the project lifecycle: from which stakeholders should be involved and how, to measuring model accuracy and success, to determining overall whether ML is an appropriate tool to use for your development context. There is no one-size-fits all answer to these questions. But whatever the specific ML/AI technologies and applications you consider, broad guidance is offered in the four thematic areas woven throughout this guide: Responsible, equitable, and inclusive design; Strategic partnerships and human capital; Adaptive management; Enabling environment for ML/AI. These focal points should help you and your project team make the best possible choices at each stage of the project life cycle." (Conclusion)