ChatGPT: Considering the Role of Artificial Intelligence in the Field of Evaluation (Part 1) by Silva Ferretti

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Hello! I am Silva Ferretti, an independent consultant working mostly with development and humanitarian organizations. I am keen to understand “how change really happens” – in the practice and in complex setups. I craft my approaches to be learning-focused, participatory, fresh, creative, fun… yet deep!

By now, you have likely heard of ChatGPT, an Artificial Intelligence model that interacts in a conversational format. I have been playing with it for some time now. Not only am I amazed by it, I am surprised by the lack of debate regarding AI’s role in development and humanitarian program management. It is a game changer. We as a field should be looking into it NOW.

Lessons Learned

  1. AI can write well-crafted logical frameworks and program concepts, as well as sectoral strategies, that are on par or even better than some real ones. It is able to anticipate risks and limitations, and propose detailed activities.
  2. It is inclusive and politically aware, in a positive way. It has been trained to value inclusion and diversity, and is skilled at articulating ideas of participation and accountability, while also understanding that these ideas can generate conflict.
  3. It is progressive and embraces a variety of methods and approaches. It can easily determine when rigorous/objective research is needed and when more constructivist methods should be used. It understands the advantages and areas of application for complexity-aware and feminist approaches.
  4. It is creative and can use various communication styles. It suggested that conventional monitoring and evaluation methods may not be suitable for some programs and helped me generate anecdotes, commercials and even a rap song.
  5. It excels at concepts, not facts. It does not provide references or links, and may sometimes confuse the names of standards or approaches. However, it understands the core concepts and can provide valuable insights. It is not a search engine, but a different paradigm.

What do I take from it?

  1. The AI looks so good because a lot of developmental and humanitarian work is based on set approaches and jargon. We play by the book, when writing projects, when monitoring and evaluating change. This has advantages of course (we should not always reinvent the wheel!).  But this is also where an AI works best. It is like these professionals good at making  any project look cool, using the right words: nice, streamlined, even when reality is messy. And, sadly, what surfaces about many projects and programmes are just these sanitized proposals/reportings: confirmation of preset causal chains, with pre-set indicators… whilst local partners and change makers would tell more interesting and varied stories. It is the sanitized stories which eventually travels up the reporting chain, and into the AI of the future. This generates confirmation bias. And strengthens models accepted and established because we keep using them with the same lenses and logic. But reality is not like the blueprint.
  2. The AI is more progressive than several professionals/institutions, in recognizing the whole field of complexity and complexity-driven approaches. Have a chat with it, asking what approaches are best in diverse contexts. It is adamant that participatory and empowerment processes require ad-hoc approaches. The lesson? That available evidence already indicates that there is notonly one appropriate way  to manage and evaluate (the bureaucratic/rigourous one). The fact that a machine understands the importance of the non quantifiable, of emergence, of feminist approaches – and some human managers don’t get it… – well, it makes me think a lot.
  3. The AI can be really “creative” when prompted. Try it out, and discover the many ways we could use to share the same concepts: poems, songs, riddles, conversations, anecdotes, stories. It is liberating, and a great way to free our own creativity and reach out to new audiences – when talking about change. It can add a whole new “communication dimension” to monitoring, evaluation, and programming.
  4. It is already happening. Artificial intelligence is not going to materialize in the far away future. You can do pretty decent work with it now. For routine tasks, including proposal writing, it is at least as good as a middle level officer needing supervision. How are we going to react? How should we use this tool? What will we teach to the next generation of professionals?

Check back into the blog tomorrow for some concluding thoughts about AI and its role in the field of M&E.


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