How can machine vision help philanthropic organizations measure their impact?

1 What is impact measurement?

Impact measurement is the process of assessing how well a philanthropic organization is achieving its objectives and creating positive change for its beneficiaries and society. This involves collecting and analyzing data on the outputs, outcomes, and impacts of the organization's activities and programs. Through impact measurement, philanthropic organizations can demonstrate their value and accountability to their donors, partners, and stakeholders, as well as learn from their successes and failures to improve their strategies and practices. Moreover, it enables them to communicate their stories and results to their audiences and the public, as well as attract more funding and support for their causes.

2 Why is impact measurement challenging?

Impact measurement is not an easy task; it requires a substantial amount of resources, skills, and tools to collect, manage, and analyze data from various sources and contexts. Philanthropic organizations often face difficulties when measuring their impact, such as defining clear and relevant indicators and metrics that capture the change they wish to see, finding reliable and valid data sources and methods that match their indicators and metrics, dealing with complex and dynamic social and environmental issues that have multiple interrelated causes and effects, measuring long-term and intangible impacts that are difficult to quantify and attribute, as well as balancing the trade-offs between rigor and feasibility, quality and quantity, depth and breadth of data.

4 What are some examples of machine vision in action?

Philanthropic organizations are increasingly leveraging machine vision to measure their impact in different domains and regions. For instance, the Stanford Sustainable Development Lab utilizes machine vision to analyze satellite images and estimate poverty levels and economic activity in rural parts of Africa and Asia. Similarly, the Wildlife Conservation Society uses machine vision to identify and count animals in camera trap images and videos, aiding them in the monitoring of endangered species and habitats. Additionally, the Humanitarian OpenStreetMap Team relies on machine vision to extract features and landmarks from aerial images, creating maps and information for humanitarian response and recovery. Lastly, the Global Good Fund is using machine vision to diagnose cervical cancer from smartphone images, providing low-cost and accessible screening and treatment for women in low-resource settings.

5 What are some challenges and limitations of machine vision?

Machine vision is not a foolproof solution for impact measurement and has several challenges and limitations that philanthropic organizations must be aware of. These include ethical and legal issues, such as data protection, consent, and ownership. In addition, data quality and availability can vary depending on the source, format, resolution, and coverage. Algorithms used in machine vision can also be biased and unfair, so it is important to test and validate them for transparency and accountability. Lastly, machine vision should be seen as a complement to human involvement and collaboration rather than a substitute. Philanthropic organizations should involve their beneficiaries, partners, and stakeholders in the design, implementation, and evaluation of their impact measurement activities.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?