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Discussing ChatGPT with the World Bank: Questions on research, knowledge management, bias and creativity

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      (Illustrative Image generated with Midjourney) On May 3, 2023, I had the pleasure to talk to the World Bank Group's Community of Practice on Knowledge Management about the opportunities that ChatGPT and similar tools present to the KM space. The group was exceptionally astute and came up with a number of very important questions that I believe are critical to reflect on. In the following, I am showcasing a few of them, along with my own thoughts that I articulated in the exchange: Question 1: I am interested to know if systems like ChatGPT can generate valuable new knowledge, i.e. combine lessons from a few past projects and suggest how should we approach the upcoming project? Generative models like GPT-4 will always build on existing bodies of knowledge, and therefore are (at the moment at least) better suited for a look into the past than as a tool for foresight or predictive analysis. However, as you suggest, we can ask it to combine information, theories, framew...
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The “Duh-test”, or what is not a lesson learned

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I was recently reviewing a number of texts which my organization collected from past projects and initiatives (some through an internal mandatory monitoring tool, others gathered as part of After Action Reviews or Lessons Learned Papers), which all meant to capture ‘lessons learned’ from specific experiences. And while these texts were not wrong per se, I realized that there seems to be a fundamental misconception what constitutes a good lesson, and what doesn’t. Here are a few typical examples of what we often collect as part of such lessons learned exercises: “Ensure that the [Team] Manager has excellent leadership, project and team management skills, understanding of programming and experience working in [the subject matter].” “Project outputs must be compatible towards project goals. Throughout the project there is a need for careful identification of project goals and outputs to ensure that they are compatible with each other. This can be only ensured through a consultativ...
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Using ChatGPT to Counter Bias, Prejudice, and Discrimination

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Bias is a problem in the use of AI. What most people don't know, however, is that you can use ChatGPT specifically to expose bias in AI outputs (as well as in our human-written texts). In this article, I'll show you how! All people have biases. AI is trained on human-generated texts from the web. This means AI also inherits the biases of an internet dominated by content from Western, white, and English-speaking people. Just as we have to deal with the unquestioned assumptions and prejudices of our fellow humans (and ourselves!), we must also do so with AI. Even more so, because AI, by default (i.e., without specifically asking for possible biases), actually amplifies existing biases in the training data .  So, what can we as users do? We need to question every text, and every assumption, no matter who wrote it or who made it. And this is where AI can help us again. Because just as it amplifies existing advantages in training data without special instructions, AI can also be cal...
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