STEP 2: PROBLEM DEFINITION, EVALUATING AI/ML SUITABILITY

Does AI/ML present a viable/suitable solution to the given problem?

Co-creating a viable and valuableAI solution must include in-depth analysis of the problem it is aiming to address and consideration of the wider ecosystem surrounding the issue. Local stakeholders must be involved in both defining the problem and assessing whether AI is an appropriate solution in the given context. This can be done through an evaluation of alternatives to AI, an assessment of assets and liabilities, and through landscaping what has been done in the past in order to extract key learnings.

Please find below a legend of what can be found within the framework:

📚Resources - e.g. reports, articles, and case studies

🛠Tools - e.g. guidelines, frameworks and scorecards

🔗Links - e.g. online platforms, videos, hubs and databases

❌Gap analysis - tools or resources are currently missing

👥 List of stakeholders which should be included in the specific decision point

  • 👥 Local populations, end users, local government, academia and local universities

    📚 UNESCO Indigenous protocols for AI - A paper outlining a set of defined principles and protocols to involve local indigenous populations in the design of AI solutions

    📚 The Africa-Canada Artificial Intelligence and Data Innovation Consortium (page 4) - Consortium that engages closely with local community leaders and policy-makers to co-develop research questions and solutions relevant to local needs

    ❌ Resource assessments to determine whether AI is viable and sustainable over time

  • 👥 Academia and international organisations

    ❌Knowledge sharing platforms, hubs, or databases of use cases

  • 👥Ecosystem actors, local government, local entrepreneurs and businesses, local and international software developers

    🛠 USAID Scorecard: "Is ML Worth Trying Out as an Approach to Solving Our Problem? " (see 'Managing Machine Learning Projects' pg. 24) - Guidelines designed to help digital development practitioners integrate established best practices into technology-enabled programs and help them succeed in applying digital technologies to development programs

    🛠 Nethope and USAID: AI Suitability Toolkit - Tool to increase NGOs' internal expertise and capacity to evaluate, develop, procure, and use AI/ML in their work to ensure that people in need are aware of the technologies that affect them and their communities

    🔗 The Machine Learning Hub- Open source platform making artificial intelligence (AI), machine learning (ML), and data science accessible to everyone MLHub is an outreach program that focuses on demonstrating Hub the utility of AI today. Each package demonstrates capabilities within a few minutes where you can learn how AI tools can be applied to business and science in daily activities

    ❌ Benchmark for performance of existing AI/ML programs

  • 👥 Program developers, managers, implementers, suppliers

    🛠 Nesta: Collective Intelligence Design Playbook - Playbook to support the design and delivery of a collective intelligence project, including approaches for understanding how to harness the best ideas, information and insight to address a complex issue and activities to facilitate diverse groups of people, data and technology to achieve common goals

    ❌ Step-by-step guidelines to define AI/ML project management and timeline

Is AI/ML a feasible and desirable solution to implement in this context?